Podcast appearances and mentions of Thomas Schelling

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Darren is back at the mic after a month away and, again, back on the Iran/Hormuz story. The deal he spent an hour analysing in June is dead inside three weeks, the two sides are fighting again, American soldiers are dying, important infrastructure has been destroyed across the region, and a fresh ceasefire proposal is circulating even as Washington weighs a much larger campaign alongside Israel. This episode is his effort to describe the machinery that keeps generating these crises, with his analysis intended to hold up whether there's a ceasefire or a major escalation by the time you listen. Darren starts with the collapse of the June memorandum and why the agreement that used the word "permanent" turned out to be the shortest-lived of the lot. He then works through what Tehran believes it saw in the three weeks after signing, a short theory of how wars stay limited and how those limits break, and the idea that sits at the centre of the episode: that this is no longer a war about what the two sides don't know, but about their inability to make any promise the other has reason to believe. From there it's the oil clock — how close the market is running to empty, and why the cushions that saved us the first time may not be there again. Then he moves to the the risk that Iran's apparent victory is quietly turning into a trap, what a durable settlement over the Strait of Hormuz might actually look like, and why none of it touches the nuclear question that started the war. The episode closes well beyond Iran, with a more general thought about a world economy that has become cheap to disrupt, slow to reassure, and which no government can any longer credibly promise not to weaponise. Australia in the World is written, hosted, and produced by Darren Lim, with research and editing by Hannah Nelson and theme music composed by Rory Stenning. Relevant links Note: Not everything below is named in the episode; some pieces sit behind the analysis rather than being quoted directly. Where Darren refers to a work explicitly, it's marked with an asterisk (*). On the collapse of the deal and the view from Tehran * Vali Nasr, "Why Iran is returning to war," Financial Times, 17 July 2026: https://www.ft.com/content/22fdb2d5-b454-4a65-9aa9-e56371533ccf * Ali Vaez, "This Is a Forever War in the Making," The New York Times, 15 July 2026: https://www.nytimes.com/2026/07/15/opinion/us-iran-forever-war-strait-of-hormuz.html   * International Crisis Group, "From Deal to Ordeal: The U.S. and Iran" (panel featuring Vali Nasr; moderated by Ali Vaez), 16 July 2026: https://www.youtube.com/watch?v=Ka_G_Oi2Ez8 * Rob Malley, remarks on the drafting of paragraph 5 (via X / Crisis Group), July 2026: https://x.com/Rob_Malley/status/2076796118604632398 * Ali Hashem, “'The word belongs to the battlefield': Why Iran, US are drifting back to conflict”, Al-Monitor, 14 July 2026: https://www.al-monitor.com/originals/2026/07/word-belongs-battlefield-why-iran-us-are-drifting-back-conflict On Trump, strategy and the limits of American power * Trita Parsi, “Yesterday's US-Iran escalation is a mere prelude to what is likely to come”, Substack 19 July 2026: https://tritaparsi.substack.com/p/yesterdays-us-iran-escalation-is Peter Baker, "In Iran, Trump Has Found an Opponent He Cannot Easily Dominate," The New York Times, 14 July 2026: https://www.nytimes.com/2026/07/14/us/politics/iran-trump-war.html Robin Wright, "Donald Trump's Ad-Lib Strategy on Iran," The New Yorker, 17 July 2026: https://www.newyorker.com/news/the-lede/donald-trumps-ad-lib-strategy-on-iran Greg Jaffe and Eric Schmitt, "Trump Faces the Limits of U.S. Firepower and the Lessons of Past Wars," The New York Times, 17 July 2026: https://www.nytimes.com/2026/07/17/us/politics/trump-iran-war-afghanistan-iraq.html "The Iran war is America's least popular since polls began," The Economist, 19 July 2026: https://www.economist.com/united-states/2026/07/19/the-iran-war-is-americas-least-popular-since-polls-began On Hormuz, oil markets and the equilibrium * Javier Blas, "Iran Risks Overplaying Its Hand on Hormuz," Bloomberg Opinion, 16 July 2026: https://www.bloomberg.com/opinion/articles/2026-07-16/strait-of-hormuz-iran-risks-overplaying-its-hand-on-oil-chokepoint "Donald Trump has no good options for reopening the Strait of Hormuz," The Economist, July 2026: https://www.economist.com/middle-east-and-africa/2026/07/14/donald-trump-has-no-good-options-for-reopening-the-strait-of-hormuz "Oil traders warn market is close to running on empty as Hormuz shuts again," Financial Times, 15 July 2026: https://www.ft.com/content/6c314457-ce9a-4689-b3e1-ff637de0d6bf?syn-25a6b1a6=1 Lewis Jackson and Sam Li, “China's oil imports have plunged during the Iran war. How much will they recover?”, Reuters, 17 July 2026: https://www.reuters.com/business/energy/chinas-oil-imports-have-plunged-during-iran-war-how-much-will-they-recover-2026-07-17/ Amos Hochstein, thread on depleted energy buffers, 17 July 2026: https://x.com/amoshochstein/status/2077812227890131406 On the nuclear file * James Acton in conversation with Jon Bateman, World Unpacked (Carnegie), 12 July 2026: https://carnegieendowment.org/podcasts/the-world-unpacked/a-physicist-explains-how-iran-can-still-go-nuclear The theory * Thomas Schelling, Arms and Influence (1966): https://books.google.com.au/books/about/Arms_and_Influence.html?id=P-_RDwAAQBAJ&redir_esc=y * Herman Kahn, On Escalation: Metaphors and Scenarios (1965): https://books.google.com.au/books?id=0No5uIPpD8AC&printsec=frontcover&redir_esc=y#v=onepage&q&f=false * Robert Jervis, Perception and Misperception in International Politics (1976): https://www.jstor.org/stable/j.ctvc77bx3 * James Fearon, "Rationalist Explanations for War," International Organization (1995): https://www.jstor.org/stable/2706903 * Robert Powell, "War as a Commitment Problem," International Organization (2006): https://www.jstor.org/stable/3877871

Francis J. Gavin, chair of the TNSR editorial board, joins us to discuss his article, "Strategic Stability and Its Limits: Reflections on Schelling." Gavin explains why Thomas Schelling remains foundational to nuclear strategy despite being an economist, and argues that "strategic stability" is often invoked without clear definition. He highlights tensions between mutual vulnerability and US extended deterrence and nonproliferation goals, and describes contradictions between Schelling's writings on arms control and coercion. Gavin critiques simplified historical lessons about surprise attack and inadvertent war shaping stability theory, traces how Cold War political constraints drove US nuclear posture, and urges policymakers to put politics and state interests first when assessing nuclear risks and emerging technologies such as AI, cyber, autonomy, and biotechnology. Hosts: Sheena Chestnut Greitens and Ryan Vest Producer: Jordan Morning

Also available in markdown at theMultiplicity.ai/blog/schelling-goodness. This post explores a notion I'll call Schelling goodness. Claims of Schelling goodness are not first-order moral verdicts like "X is good" or "X is bad." They are claims about a class of hypothetical coordination games in the sense of Thomas Schelling, where the task being coordinated on is a moral verdict. In each such game, participants aim to give the same response regarding a moral question, by reasoning about what a very diverse population of intelligent beings would converge on, using only broadly shared constraints: common knowledge of the question at hand, and background knowledge from the survival and growth pressures that shape successful civilizations. Unlike many Schelling coordination games, we'll be focused on scenarios with no shared history or knowledge amongst the participants, other than being from successful civilizations. Importantly: To say "X is Schelling-good" is not at all the same as saying "X is good". Rather, it will be defined as a claim about what a large class of agents would say, if they were required to choose between saying "X is good" and "X is bad" and aiming for a mutually agreed-upon answer. This distinction is crucial [...] ---Outline:(01:59) This essay is not very skimmable(03:44) Pro tanto morals, is good, and is bad(06:39) Part One: The Schelling Participation Effect(13:52) What makes it work(15:50) The Schelling transformation on questions(19:10) Part Two: Schelling morality via the cosmic Schelling population(21:12) Scale-invariant adaptations(22:54) An example: stealing(30:32) Recognition versus endorsement versus adherence(31:34) The answer frequencies versus the answer(33:59) Ties are rare(35:06) Is the cosmic Schelling answer ever knowable with confidence?(36:02) Schelling participation effects, revisited(38:03) Is this just the mind projection fallacy?(39:42) When are cosmic Schelling morals easy to identify?(42:59) Scale invariance revisited(44:03) A second example: Pareto-positive trade(47:45) Harder questions and caveats(50:01) Ties are unstable(51:43) Isnt this assuming moral realism?(53:07) Dont these results depend on the distribution over beings?(54:41) What about the is-ought gap?(56:29) Tolerance, local variation, and freedom(58:25) Terrestrial Schelling-goodness(59:42) So what does good mean, again?(01:01:08) Implications for AI alignment(01:06:15) Conclusion and historical context(01:09:16) FAQ(01:09:20) Basic misunderstandings(01:12:20) More nuanced questions --- First published: February 28th, 2026 Source: https://www.lesswrong.com/posts/TkBCR8XRGw7qmao6z/schelling-goodness-and-shared-morality-as-a-goal --- Narrated by TYPE III AUDIO.

In this episode, in conversation with Robert Patton-Spruill, Mark Sussman, and Nikita Petrov, I provide an introduction to the life and work of Thomas Schelling and consider how a “Schellingesque” point of view might influence our interpretation of current events, like the Ukraine War, the “human shields” argument in Gaza, the Trump administration's new national […]

When the little boy said the emperor was naked, he wasn't telling them anything they didn't already know. But he added to their knowledge nonetheless. By blurting out what everyone could see within earshot of the others, he ensured that everyone knew that everyone else knew what they knew, that everyone knew that, and so on. And that changed their relationship with the emperor from obsequious deference to ridicule and scorn.当那个小男孩说皇帝没穿衣服时，他并没有告诉大家他们原本不知道的事情。但他仍然在一定程度上增加了他们的认知。因为他当众喊出大家都能看到的事实，让所有人都知道了其他人也知道他们所知道的，而所有人也知道这一点，如此循环往复。于是，这改变了他们对皇帝的态度——从谄媚恭敬转为嘲笑与蔑视。Hans Christian Andersen's immortal story draws on a momentous logical distinction. With private knowledge, I know something, and you know it. With common knowledge, I know that fact, and you know it, but in addition, I know that you know it, and you know that I know it, and I know that you know that I know it, ad infinitum. Of course, the reason that common knowledge is significant is that it is essential for coordination.安徒生这篇不朽的故事揭示了一个重要的逻辑区别：在“私有知识”中，我知道某件事，你也知道它。而在“公共知识”中，我知道这个事实，你也知道，但除此之外，我知道你知道它，你也知道我知道它，我还知道你知道我知道它，如此无限递归。当然，“公共知识”之所以重要，是因为它对于协调行动至关重要。In a classic example from Thomas Schelling, a couple is separated in Manhattan, incommunicado, and somehow must find each other. Well he knows that she likes to browse the aisles of certain bookstore, so he heads there. But then he realizes that she knows that he likes to hang out in a certain camera store, so he changes course until he figures that she will anticipate that he will guess that she will opt for the bookstore. So he does another about face, only for it to dawn on him that it will occur to her that he knows that she is aware that he likes to haunt the bookstore, so he pirouettes once again. Meanwhile, she is whipsawed by the same futile empathy. Nothing short of common knowledge can guarantee that they'll end up at the same place and at the same time.托马斯·谢林的一个经典例子是：一对情侣在曼哈顿走散，无法联系，但必须想办法找到对方。男方知道女方喜欢逛某家书店，于是往那边走；但他又想到，女方知道他喜欢待在某家相机店，于是改道过去；接着他又推测，女方可能会预料到他会猜她会选择书店，于是他再次掉头；然而他又意识到，女方可能会想到他知道她清楚他喜欢去书店，于是他又转回去。与此同时，女方也在被同样无休止的推测折腾着。除了“公共知识”，没有任何东西能确保他们在同一时间出现在同一地点。Of course, no one can think an infinite Russian doll of “I know that she knows that I know that she knows” thoughts. Our heads start to spin with three or four layers, let alone an infinite number.当然，没有人能在脑中无限套娃地思考“我知道她知道我知道她知道”这种想法。三四层就足以让人头晕，更别说无限层了。In a well-known episode of "Friends," Phoebe says to Rachel, “They don't know we know they know we know. Joey, you can't say anything!"在著名美剧《老友记》的一集中，菲比对瑞秋说：“他们不知道我们知道他们知道我们知道。乔伊，你什么也不能说！”And he replies, "I couldn't even if I wanted to!"乔伊回答：“我就算想说，也说不出来！”Instead, common knowledge can be captured in a simple mental intuition that something is public or conspicuous or out there, and that can be conveyed by direct speech. In the case of our separated couple, a cell phone call. Indeed, solving coordination dilemmas may be the reason that language evolved in our species in the first place.实际上，“公共知识”可以通过一种简单的心理直觉来获得——某事是公开的、显而易见的、众所周知的——而这种状态可以通过直接的语言传递。在走散情侣的例子中，一个电话就足够了。事实上，解决协调困境可能正是人类语言最初演化的原因之一。In the absence of a public event, the next best thing is conspicuous salience, or a focal point. Schelling suggests that our couple might gravitate toward the big clock in Grand Central Station, even if it wasn't particularly close to the point at which they'd been separated, simply because each might anticipate that it would pop into the mind of the other.如果没有公共事件，次优的选择是明显的“显著性”或“焦点”。谢林建议，这对情侣可能会选择去中央车站的大钟下见面，即使它离他们走散的地方并不近，仅仅是因为他们都可能想到对方会首先想到这个地方。A third solution is a convention. A tacit agreement to do something in a certain way for no other reason than they have agreed to do it that way, which is reason enough. Our separated couple might agree that should they ever be separated in the future, they will adopt the convention of chivalry and go to the bookstore. Or patriarchy, and go to the camera store. Or whimsy, and go to a lost-and-found in a department store. Or fairness, and take turns or flip coins.第三种解决方式是“约定”。即双方默契地同意以某种方式行事，唯一的理由就是他们已经同意了这样做，而这本身就足够了。这对走散的情侣可能会约定，如果以后再走散，就按照“骑士精神”去书店；或者按照“男权主义”去相机店；又或者凭“异想天开”去百货商店的失物招领处；或者讲求“公平”，轮流决定或抛硬币来定。

Annie Jacobsen has a favorite word for America's nuclear doctrine: madness. It's madness that any single person has six minutes to decide the fate of civilization, madness that we've built weapons capable of ending the world in 72 minutes, and madness that everything hangs by the thread of deterrence. But to Tyler, life is "a lot of different kinds of madness," and the real question is simply getting the least harmful form available to us. It's a conversation sparked by her latest book Nuclear War: A Scenario, which Tyler calls one of his favorites from last year—and which is compelling enough that Denis Villeneuve is turning it into a screenplay. Tyler and Annie explore whether we should be more afraid of nuclear weapons or if fear itself raises the risks, who should advise presidents during the six-minute decision window, whether moving toward disarmament makes us safer or more vulnerable, what Thomas Schelling really meant about nuclear war and rational actors, the probability that America would retaliate after a nuclear attack, the chances of intercepting a single incoming ICBM, why missile defense systems can't replicate Israel's Iron Dome success, how Pakistan-India nuclear tensions could escalate, why she's surprised domestic drone attacks haven't happened yet, her reporting on JFK assassination mysteries and deathbed phone calls, her views on UFOs and the dark human experiments at Area 51, what motivates intelligence community operators, her encounters with Uri Geller and CIA psychic research, what she's working on next, and more. Read a full transcript enhanced with helpful links, or watch the full video on the new dedicated Conversations with Tyler channel. Recorded May 19th, 2025. Help keep the show ad free by donating today! Other ways to connect Follow us on X and Instagram Follow Tyler on X Follow Annie on X Sign up for our newsletter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here.

rWotD Episode 2876: Wassily Leontief Welcome to Random Wiki of the Day, your journey through Wikipedia’s vast and varied content, one random article at a time.The random article for Wednesday, 19 March 2025 is Wassily Leontief.Wassily Wassilyevich Leontief (Russian: Васи́лий Васи́льевич Лео́нтьев; August 5, 1905 – February 5, 1999), was a Soviet-American economist known for his research on input–output analysis and how changes in one economic sector may affect other sectors.Leontief won the Nobel Memorial Prize in Economic Sciences in 1973, and four of his doctoral students have also been awarded the prize (Paul Samuelson 1970, Robert Solow 1987, Vernon L. Smith 2002, Thomas Schelling 2005).This recording reflects the Wikipedia text as of 00:06 UTC on Wednesday, 19 March 2025.For the full current version of the article, see Wassily Leontief on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm generative Joanna.

America aims to avoid nuclear war by relying on the principle of 'mutually assured destruction,' right? Wrong. Or at least... not officially.As today's guest — Jeffrey Lewis, founder of Arms Control Wonk and professor at the Middlebury Institute of International Studies — explains, in its official 'OPLANs' (military operation plans), the US is committed to 'dominating' in a nuclear war with Russia. How would they do that? "That is redacted."Rebroadcast: this episode was originally released in December 2022.Links to learn more, highlights, and full transcript.We invited Jeffrey to come on the show to lay out what we and our listeners are most likely to be misunderstanding about nuclear weapons, the nuclear posture of major powers, and his field as a whole, and he did not disappoint.As Jeffrey tells it, 'mutually assured destruction' was a slur used to criticise those who wanted to limit the 1960s arms buildup, and was never accepted as a matter of policy in any US administration. But isn't it still the de facto reality? Yes and no.Jeffrey is a specialist on the nuts and bolts of bureaucratic and military decision-making in real-life situations. He suspects that at the start of their term presidents get a briefing about the US' plan to prevail in a nuclear war and conclude that "it's freaking madness." They say to themselves that whatever these silly plans may say, they know a nuclear war cannot be won, so they just won't use the weapons.But Jeffrey thinks that's a big mistake. Yes, in a calm moment presidents can resist pressure from advisors and generals. But that idea of ‘winning' a nuclear war is in all the plans. Staff have been hired because they believe in those plans. It's what the generals and admirals have all prepared for.What matters is the 'not calm moment': the 3AM phone call to tell the president that ICBMs might hit the US in eight minutes — the same week Russia invades a neighbour or China invades Taiwan. Is it a false alarm? Should they retaliate before their land-based missile silos are hit? There's only minutes to decide.Jeffrey points out that in emergencies, presidents have repeatedly found themselves railroaded into actions they didn't want to take because of how information and options were processed and presented to them. In the heat of the moment, it's natural to reach for the plan you've prepared — however mad it might sound.In this spicy conversation, Jeffrey fields the most burning questions from Rob and the audience, in the process explaining:Why inter-service rivalry is one of the biggest constraints on US nuclear policyTwo times the US sabotaged nuclear nonproliferation among great powersHow his field uses jargon to exclude outsidersHow the US could prevent the revival of mass nuclear testing by the great powersWhy nuclear deterrence relies on the possibility that something might go wrongWhether 'salami tactics' render nuclear weapons ineffectiveThe time the Navy and Air Force switched views on how to wage a nuclear war, just when it would allow *them* to have the most missilesThe problems that arise when you won't talk to people you think are evilWhy missile defences are politically popular despite being strategically foolishHow open source intelligence can prevent arms racesAnd much more.Chapters:Cold open (00:00:00)Rob's intro (00:01:05)The interview begins (00:03:31)Misconceptions in the effective altruism community (00:06:24)Nuclear deterrence (00:18:18)Dishonest rituals (00:28:59)Downsides of generalist research (00:32:55)“Mutual assured destruction” (00:39:00)Budgetary considerations for competing parts of the US military (00:52:35)Where the effective altruism community can potentially add the most value (01:02:57)Gatekeeping (01:12:46)Strengths of the nuclear security community (01:16:57)Disarmament (01:27:40)Nuclear winter (01:39:36)Attacks against US allies (01:42:28)Most likely weapons to get used (01:45:53)The role of moral arguments (01:47:22)Salami tactics (01:52:43)Jeffrey's disagreements with Thomas Schelling (01:57:42)Why did it take so long to get nuclear arms agreements? (02:01:54)Detecting secret nuclear facilities (02:04:01)Where Jeffrey would give $10M in grants (02:06:28)The importance of archival research (02:11:45)Jeffrey's policy ideas (02:20:45)What should the US do regarding China? (02:27:52)What should the US do regarding Russia? (02:32:24)What should the US do regarding Taiwan? (02:36:09)Advice for people interested in working on nuclear security (02:38:06)Rob's outro (02:39:45)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

She's an economist, an institution-builder, an ecosystem-nurturer and one of our finest thinkers. Shruti Rajagopalan joins Amit Varma in episode 410 of The Seen and the Unseen to talk about her life & times -- and her remarkable work. (FOR FULL LINKED SHOW NOTES, GO TO SEENUNSEEN.IN.) Also check out: 1. Shruti Rajagopalan on Twitter, Substack, Instagram, her podcast, Ideas of India and her own website. 2. Emergent Ventures India. 3. The 1991 Project. 4. Life Lessons That Are Priceless -- Episodes 400 of The Seen and the Unseen. 5. Other episodes of The Seen and the Unseen w Shruti Rajagopalan, in reverse chronological order: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18. 6. The Day Ryan Started Masturbating -- Amit Varma's newsletter post explaining Shruti Rajagopalan's swimming pool analogy for social science research. 7. A Deep Dive Into Education -- Episode 54 of Everything is Everything. 8. Fixing Indian Education — Episode 185 of The Seen and the Unseen (w Karthik Muralidharan). 9. Population Is Not a Problem, but Our Greatest Strength -- Amit Varma. 10. Our Population Is Our Greatest Asset -- Episode 20 of Everything is Everything. 11. Where Has All the Education Gone? -- Lant Pritchett. 12. Lant Pritchett Is on Team Prosperity — Episode 379 of The Seen and the Unseen. 13. The Theory of Moral Sentiments — Adam Smith. 14. The Wealth of Nations — Adam Smith. 15. Commanding Heights -- Daniel Yergin. 16. Capitalism and Freedom -- Milton Friedman. 17. Free to Choose -- Milton Friedman and Rose Friedman. 18. Economics in One Lesson -- Henry Hazlitt. 19. The Road to Serfdom -- Friedrich Hayek. 20. Four Papers That Changed the World -- Episode 41 of Everything is Everything. 21. The Use of Knowledge in Society -- Friedrich Hayek. 22. Individualism and Economic Order -- Friedrich Hayek. 23. Understanding the State -- Episode 25 of Everything is Everything.  24. Richard E Wagner at Mercatus and Amazon. 25. Larry White and the First Principles of Money -- Episode 397 of The Seen and the Unseen. 26. Fixing the Knowledge Society -- Episode 24 of Everything is Everything. 27. Marginal Revolution. 28. Paul Graham's essays. 29. Commands and controls: Planning for indian industrial development, 1951–1990 -- Rakesh Mohan and Vandana Aggarwal. 30. The Reformers -- Episode 28 of Everything is Everything. 31. India: Planning for Industrialization -- Jagdish Bhagwati and Padma Desai. 32. Open Borders: The Science and Ethics of Immigration -- Bryan Caplan and Zach Weinersmith. 33. Cows on India Uncut. 34. Abdul Karim Khan on Spotify and YouTube. 35. The Surface Area of Serendipity -- Episode 39 of Everything is Everything. 36. Objects From Our Past -- Episode 77 of Everything is Everything. 37. Sriya Iyer on the Economics of Religion -- The Ideas of India Podcast. 38. Episodes of The Seen and the Unseen with Ramachandra Guha: 1, 2, 3, 4, 5, 6. 39. Episodes of The Seen and the Unseen with Pratap Bhanu Mehta: 1, 2. 40. Rohit Lamba Reimagines India's Economic Policy Emphasis -- The Ideas of India Podcast. 41. Rohit Lamba Will Never Be Bezubaan — Episode 378 of The Seen and the Unseen. 42. The Constitutional Law and Philosophy blog. 43. Cost and Choice -- James Buchanan. 44. Philip Wicksteed. 45. Pratap Bhanu Mehta on The Theory of Moral Sentiments -- The Ideas of India Podcast. 46. Conversation and Society — Episode 182 of The Seen and the Unseen (w Russ Roberts). 47. The Common Sense of Political Economy -- Philip Wicksteed. 48. Narendra Shenoy and Mr Narendra Shenoy — Episode 250 of The Seen and the Unseen. 49. Sudhir Sarnobat Works to Understand the World — Episode 350 of The Seen and the Unseen. 50. Manmohan Singh: India's Finest Talent Scout -- Shruti Rajagopalan. 51. The Importance of the 1991 Reforms — Episode 237 of The Seen and the Unseen (w Shruti Rajagopalan and Ajay Shah). 52. The Life and Times of Montek Singh Ahluwalia — Episode 285 of The Seen and the Unseen. 53. The Forgotten Greatness of PV Narasimha Rao — Episode 283 of The Seen and the Unseen (w Vinay Sitapati). 54. India's Massive Pensions Crisis — Episode 347 of The Seen and the Unseen (w Ajay Shah & Renuka Sane). 55. The Life and Times of KP Krishnan — Episode 355 of The Seen and the Unseen. 56. Breaking Through — Isher Judge Ahluwalia. 57. Breaking Out — Padma Desai. 58. Perestroika in Perspective -- Padma Desai. 59. Shephali Bhatt Is Searching for the Incredible — Episode 391 of The Seen and the Unseen. 60. Pics from the Seen-Unseen party. 61. Pramod Varma on India's Digital Empowerment -- Episode 50 of Brave New World. 59. Niranjan Rajadhyaksha Is the Impartial Spectator — Episode 388 of The Seen and the Unseen. 60. Our Parliament and Our Democracy — Episode 253 of The Seen and the Unseen (w MR Madhavan). 61. Episodes of The Seen and the Unseen with Pranay Kotasthane: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13. 62. The Overton Window. 63. When Ideas Have Sex -- Matt Ridley. 64. The Three Languages of Politics — Arnold Kling. 65. Arnold Kling and the Four Languages of Politics -- Episode 394 of The Seen and the Unseen. 66. The Double ‘Thank You' Moment — John Stossel. 67. Economic growth is enough and only economic growth is enough — Lant Pritchett with Addison Lewis. 68. What is Libertarianism? — Episode 117 of The Seen and the Unseen (w David Boaz). 69. What Does It Mean to Be Libertarian? — Episode 64 of The Seen and the Unseen. 70. The Libertarian Mind: A Manifesto for Freedom -- David Boaz. 71. Publish and Perish — Agnes Callard. 72. Classical Liberal Institute. 73. Shruti Rajagopalan's YouTube talk on constitutional amendments. 74. What I, as a development economist, have been actively “for” -- Lant Pritchett. 75. Can Economics Become More Reflexive? — Vijayendra Rao. 76. Premature Imitation and India's Flailing State — Shruti Rajagopalan & Alexander Tabarrok. 77. Elite Imitation in Public Policy — Episode 180 of The Seen and the Unseen (w Shruti Rajagopalan and Alex Tabarrok). 78. Invisible Infrastructure -- Episode 82 of Everything is Everything. 79. The Sundara Kanda. 80. Devdutt Pattanaik and the Stories That Shape Us -- Episode 404 of The Seen and the Unseen. 81. Y Combinator. 82. Space Fields. 83. Apoorwa Masuk, Onkar Singh Batra, Naman Pushp, Angad Daryani, Deepak VS and Srijon Sarkar. 84. Deepak VS and the Man Behind His Face — Episode 373 of The Seen and the Unseen. 85. You've Got To Hide Your Love Away -- The Beatles. 86. Caste, Capitalism and Chandra Bhan Prasad — Episode 296 of The Seen and the Unseen. 87. Data For India -- Rukmini S's startup. 88. Whole Numbers And Half Truths — Rukmini S. 89. The Moving Curve — Rukmini S's Covid podcast, also on all podcast apps. 90. The Importance of Data Journalism — Episode 196 of The Seen and the Unseen (w Rukmini S). 91. Rukmini Sees India's Multitudes — Episode 261 of The Seen and the Unseen (w Rukmini S). 92. Prosperiti. 93. This Be The Verse — Philip Larkin. 94. The Dilemma of an Indian Liberal -- Gurcharan Das. 95. Zakir: 1951-2024 -- Shruti Rajagopalan. 96. Dazzling Blue -- Paul Simon, featuring Karaikudi R Mani. 97. John Coltrane, Shakti, Zakir Hussain, Ali Akbar Khan, Pannalal Ghosh, Nikhil Banerjee, Vilayat Khan, Bismillah Khan, Ravi Shankar, Bhimsen Joshi, Bade Ghulam Ali Khan, Nusrat Fateh Ali Khan, Esperanza Spalding, MS Subbulakshmi, Lalgudi Jayaraman, TN Krishnan, Sanjay Subrahmanyan, Ranjani-Gayatri and TM Krishna on Spotify. 98. James Buchanan, Gordon Tullock, Israel Kirzner, Mario Rizzo, Vernon Smith, Thomas Schelling and Ronald Coase. 99. The Calculus of Consent -- James Buchanan and Gordon Tullock. 100. Tim Harford and Martin Wolf. 101. The Shawshank Redemption -- Frank Darabont. 102. The Marriage of Figaro in The Shawshank Redemption. 103. An Equal Music -- Vikram Seth. 104. Beethoven: Symphony No. 7 - Zubin Mehta and the Belgrade Philharmonic. 105. Pyotr Ilyich Tchaikovsky's violin concertos. 106. Animal Farm -- George Orwell. 107. Down and Out in Paris and London -- George Orwell. 108. Gulliver's Travels -- Jonathan Swift. 109. Alice in Wonderland and Through the Looking Glass -- Lewis Carroll. 110. One Day in the Life of Ivan Denisovich -- Aleksandr Solzhenitsyn. 111. The Gulag Archipelago -- Aleksandr Solzhenitsyn. 112. Khosla Ka Ghosla -- Dibakar Banerjee. 113. Mr India -- Shekhar Kapur. 114. Chalti Ka Naam Gaadi -- Satyen Bose. 114. Finding Nemo -- Andrew Stanton. 115. Tom and Jerry and Bugs Bunny. 116. Michael Madana Kama Rajan -- Singeetam Srinivasa Rao. 117. The Music Box, with Laurel and Hardy. 118. The Disciple -- Chaitanya Tamhane. 119. Court -- Chaitanya Tamhane. 120. Dwarkesh Patel on YouTube. Amit Varma and Ajay Shah have launched a new course called Life Lessons, which aims to be a launchpad towards learning essential life skills all of you need. For more details, and to sign up, click here. Amit and Ajay also bring out a weekly YouTube show, Everything is Everything. Have you watched it yet? You must! And have you read Amit's newsletter? Subscribe right away to The India Uncut Newsletter! It's free! Also check out Amit's online course, The Art of Clear Writing. Episode art: ‘Learn' by Simahina.

Jim talks with Glenn Loury about his recent memoir, Late Admissions: Confessions of a Black Conservative. They discuss the problem of self-regard, Glenn's mentorship under Thomas Schelling, his upbringing in the South Side of Chicago, his matriarch aunt Eloise, his best friend Woody, the one-drop rule, the social construction of race, the influence of his uncles, stealing a car for prom, the Illinois Institute of Technology, working at a printing plant, community college classes, discovering the life of the mind at Northwestern University, choosing MIT, macro- & microeconomics, separating from his wife, choosing a department to work in, getting the call from Harvard, walking the line between Economics & African-American Studies, modeling inequality in society, moving out of economic theory & into public intellectualism, "little essays," leading a double life, a torrid love affair ending in arraignment, being conservative, resisting the mournful recitation of historic victimization, a crack-cocaine addiction, resubmitting to the Christian faith, restoring his marriage, his wife's forgiveness, the arc of his political life, and much more. Episode Transcript Late Admissions: Confessions of a Black Conservative, by Glenn C. Loury The Glenn Show Glenn C. Loury is Merton P. Stoltz Professor of Economics at Brown University. He holds the B.A. in Mathematics (Northwestern) and the Ph.D. in Economics (M.I.T). As an economic theorist he has published widely and lectured throughout the world on his research. He is also among America's leading critics writing on racial inequality. He has been elected as a Distinguished Fellow of the American Economics Association, as a Member of the American Philosophical Society and of the U.S. Council on Foreign Relations, and as a Fellow of the Econometric Society and of the American Academy of Arts and Sciences.

Frank Lavin talks with Jason Hsu from the Kennedy School of Government, discussing semiconductors and Taiwan-China-U.S. relations. Appropriately, Jason's book recommendation is Thomas Schelling's, Arms and Influence, the classic work of deterrence theory and Sun Tzu's The Art of War.

In this episode of Navigating Major Programmes, Riccardo sits down with Daniel Armanios, BT Professor of Major Programme Management and Chair of Major Programme Management at University of Oxford, Saïd Business School. The pair discuss the importance of research, the type of valuable research and the post evaluation of major programmes."And so a second very cool question would be where do we want resilience in a major programme? I mean, obviously you want it within the program but do you want it in the selection process? Maybe not? Do you want it in the post validation where we don't do as well? Maybe not so maybe resilience is not great everywhere. And maybe it's really important in certain places.  I've been really thinking about this a lot because it's a really visceral fundamental point. What is it we're actually doing and trying to achieve?" – Daniel ArmaniosDaniel's research and teaching integrates civil engineering and organizational sociology to better understand how organizations coordinate to build, manage, and maintain infrastructure systems. His findings inform efforts to advance sustainable development, entrepreneurship, and innovation, while also alleviating systemic and persistent inequities within such systems.Key Takeaways:The distinction of megaprojects and major programmesThe importance of transparent assumptions and data research in major programmesStudying major programmes at a component levelWhere do we want resilience in major programmes?If you enjoyed this episode, make sure and give us a five star rating and leave us a review on iTunes, Podcast Addict, Podchaser or Castbox.The conversation doesn't stop here—connect and converse with our LinkedIn community:Follow Navigating Major Programmes on LinkedInFollow Riccardo on LinkedInFollow Daniel Armanios on LinkedInDaniel Armanios' published workRead Riccardo's latest at wwww.riccardocosentino.comTranscript:Riccardo Cosentino 0:53Hello, everyone. Today here with Daniel Armanios. How are you doing Daniel?Daniel Armanios 1:01Hey, how are you, Riccardo? Pleasure to be here.Riccardo 1:03Daniel joins us today from Oxford. Could you introduce yourself a little bit for the listeners that might not be familiar with yourself?Daniel 1:12I'm the BT Professor and Chair of Major Programme Management at the Saïd Business School at the University of Oxford. I was formerly in a school of engineering, which I'm sure will be a fun discussion later on. I was an assistant and associate professor in the Department of Engineering and Public Policy at Carnegie Mellon University. I guess the best way very symmetrically, to understand myself and my research, I'm really an organizational theorist that studies how organizations coordinate to roll out to develop to maintain very large-scale initiatives, what some call major programmes, some call major projects, some called mega projects, I'm sure we can get to discussion of the nuances and differences. But essentially, I'm an organizational studies theorist that studies large-scale initiatives in engineering social programs and the like, and kind of published widely as a result.Riccardo  2:14I've come across you, as I was finishing my master's in Major Programme Management at Oxford, you were starting, your chair. And I've been very keen to be talking to you because obviously, we represent is such a big institution with so much gravitas in the major programme space, I was really looking forward to talking to you. So today, I think the overarching topic that I would like to cover today, I think is the importance of research in major programme and the importance of research in creating better outcomes for four major programmes. That's just the general theme, but I'm sure we can get into a more detailed conversation. From your perspective, why is research important to achieve better outcomes in major programme? Why can't the private sector and practitioners just get on with it? And then it's a bit of a leading question.Daniel 3:09There is attention always with major programmes, right? All of us, I mean, all of us who research it or those who put it in practice, especially since we don't often find ourselves in a position, practitioners, to manage large-scale major programmes, the temptation as we've seen from a lot of prior work is that this is such a unique thing and this is so it's so important and of you know, call it an n-of-one. And I think there is some aspects of every major programme that have nuance. But often, you know, when you're trying to start something, it's nice to know, where what we know systematically from prior things, and that just simply requires data analysis, right? How do you how can you empirically as best as you can, with data, collecting it, being transparent about your assumptions, transparent of what you found? Could that at least get us at a starting point, with a major programme we take on in the future? And so I think, empirically, it's quite important. That said, and maybe why there's difficulties is that there's also challenges with trying to do that data. I don't know if that's where we're gonna go next. But essentially, you know, a lot of this requires post evaluation of major programmes. And often, once you've delivered a major programme, you kind of want to be done and move on to the next one, but often that post hoc evaluation really matters. So if we take an empirically driven approach, it also fundamentally changes how we think of the entire major programme lifecycle, we're not just thinking about the planning, delivery, and then kind of the handover to the sponsor, whoever is going to operate the system, but also thinking post evaluation. What did it move? Did the needle move in certain ways? How can we learn from past? So it does require data. And then also the other challenge is as we build consensus for certain models and frameworks, there is a danger that we go flip the pendulum the complete other way, which is certain kinds of tools, techniques become the way to do things. And I think, at the same time, you want to balance between what were the conditions that allow those things to happen. So kind of long story short, we need an empirical basis by which to inform our decisions so that we truly know what is unique about the program we're managing versus what we know about the past, ideally, with comparative groups. But that means that we make sure that in our own major programme lifecycle we build in faculties and facilities and capacity to contribute existing data. And that requires a little different thinking about when the major programme, let's say, quote-unquote "ends". And at the same time, you know, to not throw the baby out with the bathwater, so to speak, you also want to be able to say, really not just the data you gather on the programme itself, but the conditions around it so we can see what kinds of tools, what kind of approaches work for what kind of conditions so that you can be both empirically informed, but also nuances to where those empirical data and insights match with the kind of context you're in. And that's a I think a wider conversation happening.Riccardo  6:44I want to dive into a couple of things that you mentioned. First of all, obviously, the uniqueness of program management, of major programmes are, obviously, they're so big that it's difficult to have two running in parallel or being able to test in practice these major programmes. However, what's your view on the fact that yeah, maybe the major programme is unique, and because it's big and complex, and it's very dependent on the region, and other factors, but the sub-elements of the programme are actually repeating across multiple programmes. So you know, you have stakeholders on every single programme, you have a project sponsor on every single programme, you have group of people, subcontractor, supply chain, I mean, those things are not unique. And so I think you talk about the conditions, I think that's part of that. So is there a way of studying major programmes at the component level, which I think is that what we tend to do in the MMPM is really break it down and applying knowledge to the single components. What's your view?Daniel 8:05I think, an emerging trend, which is typically up to this point, my read is when we think of large-scale initiatives or big things, let's say, there's we usually treat major programmes and let's say mega projects or major projects as synonymous terms and I think if you see where major programmes is going, they're increasingly more distributed. So if you look at major initiatives around cryptocurrency, blockchain, it's not like there's a central convener that can move things, right? If you think of modular infrastructure construction, you're literally fabricating skews one place, putting it another place. Right? And so you're dealing with a more distributed, more decentralized system. And I think that's now creating some interesting divergence between mega projects and major programmes because when you think of mega projects, you're thinking of it as a unitary, kind of whole, because at some point, even though there's multiple organizations, you'll convene at some centralized sites. That's at least the assumption. With major programmes, if you think of it, it's more of a portfolio, which precisely gets to your point, then it becomes interesting to think of two things. One is, let's break up the bit of components and see is there something we can learn repeatable within the components? But also, is there some nuance we can understand of how things link together in appropriate ways? Should we modularize as one argument is or should we think of it more holistically as a system? Now how do I land on what we can learn is I think the research to date of that resources available, it's often focused on the intended plan or outcome for the project. And usually it's the Iron Triangle- time, cost, scope or quality, if you will, and did it achieve that or not why or why not? Let's account for these overruns. So it's more about the ends, right? So I've said this was gonna be my end, let's check at the end of the project. Did we achieve it? Now, empirically, that has some really useful facets, which is, you kind of comparing a project to itself, which is really nice. You can do comparisons, you get a sense of maybe how to help with the planning, how to avoid some of what to build in. But the process of learning by which we could have reconciled some of these overruns is a bit more difficult. So I think there's a set of resources focusing on the ends, right, in learning, I think, where you're coming from, to understand what is repeatable, repeatable is a process. So I think where some other research is going and where I've been interested in is just chronically what people are doing over time. Can we find patterns? Is there a way to go about effective stakeholder engagement? Not did we get stakeholders' approval in the audit, and it's more of the outcome? It's more how did we go through it? Was it, you know, were there certain things you did at certain times bring in certain organizations? Was that effective as opposed to not because then at that point, you can give something really useful to the manager that they can actually act as opposed to? Here's outcomes you want to worry about, we know that there's going to be this potential slippage, let's account for it in the planning. But that doesn't get much information in the process. So I think there's a lot of potential empirical research to be done on can we come up with replicable methods, means, while also being mindful of, you know, some of the risks calamities that have happened from the ends. And then that way, you really understanding what's repeatable, and not just, you got something that was effective and here's the practice. But how did that unfold over time and change? So you can still be quite repeatable. But recognizing repeatability is a process, right? And so maybe there's process models we can do, looking at projects at different slices of the timeframe. And then we can think about what it is they're doing over time? And is there some sequences that we can learn that are repeatable, that go well, or when you start hitting a fall, and then that way, as a kind of final point, if we can do that, then perhaps we can even develop early warning signs, you know, always at this step two of the process, there is something where things derail, avoid them, and you can start seeing the early warning signs. And that way, I think you can still come up with something repeatable, but more in the means of something you can action, as opposed to just be aware that these things go there's slippage overall in the project plan for it, which is important. Don't get me wrong. But then we could develop a process by which are early warning signs to develop. And that gray area is a different kind of empirical approach. But in that sense, you could then sort of see what is repeatable? What's even automatable? If we talk about future trends and what are things you need to be like spinning time on the critical path to be careful on? So research on the means, I think would be where things I think should be going and are starting to go as opposed to just the outcomes.Riccardo  13:23I think you mentioned a couple of times is historically we always focus on the postmortem. And typically a postmortem on things that went badly. And so you have this back, back catalogue of project that went bad, but there's very few post mortem on project that went well, because ultimately if you went well, you don't have to, you don't have to worry about it. I think that has been the approach. And as you said, with this, I think the problem with major project or large, large ventures is that they're so time-consuming and so draining that when you're done, you're done. You just want to move on.Daniel 14:03I would say on this point, actually, this is where it gets really interesting in the research, to compare the trends and major programme research, mega project research of again, I see a distinct what's you know, it's in a class versus how entrepreneurship research is. So entrepreneurship research suffers almost from the exact opposite issue, which is, they always focus on the big successful ones. And the failures are kind of not known because they're kind of censored out of the population before you can really study them. Right? And so you have a kind of a success bias there. In major programmes, because the ones that keep going on they keep taking more cost of you get these kinds of epic failures that are doing. And I think it's really important to be mindful is why it could be that there's some very successful projects that did the same exact thing as some of the failures and didn't have that result. The same thing with success with ventures, maybe someone really failed trying everything and didn't work out. So, again, this gets back to the first problem we were talking about, which is, if we can compare success with failure and really address that kind of empirical bias, then we can really see what is common across all projects? And where are they really different? Where is it really unique this one, but we can't do that if we're not grounded on a similar project for which had a different outcome, but had a, you know, set of similar and different processes. That's why I think, again, focusing on the means and methods and conditioning, and hopefully with comparative cases that address, you know, the proclivities of what data we have, can really help us understand what's common across all of these, and what's really different. And then that way, we can be much more circumspect of that. So I absolutely agree.Riccardo  14:51You touched I think you, earlier you touched on you said the word conditions, right, the condition within the range of the major programme and I don't know if it's equivalent, but I refer often to it as a complexity, you know, we're dealing with a complex system. So sometimes we don't even fully understand the relations between, with between the conditions, because it's a complex system, by definition, which is, to me to be fair was a key concept in understanding an industry that had been part of for over 20 years, but couldn't quite understand why it couldn't, wasn't working the way it was supposed to. And yeah, the condition, the complexity, and really diving into those in order to understand and I really like your example where, you know, you might have the same condition but different outcomes. Because of and I think that's inherent with complexity, or complex system is just you don't fully understand the interrelations.Daniel 16:57This is why I think, in our programme and just in major programmes in general, there's an increasing consensus to treat this like a system. Right now, I think one of my colleagues at Oxford, Harvey Mahler, has been focusing on different forms of complexity. And what he basically says is there's complexity within the project as well, literally, what are the tasks and work to be done, the harder systems kind of structure, what is the routine that has to be done to do this thing. But then he says, the project, though, is in a wider environment, right? So you have regulations, politicians turning over at all points, you have socio-political what you would call I think, socio-political complexity. And then by the way, it's not like, if I look at it at times zero, the same form of complex emerges at time one, because when you run the system and loop it, all of sudden things emerge and change. And so there's, he would, argue emergent complexity. Now, what's interesting about what he's saying, If we tie it to the earlier part of our conversation, we're talking about means and outcomes, we still empirically largely focus on those within the project paths, right? So when we typically measure performance outcomes, we measure even means we're thinking, I'm delivering this project, how do I measure it? And how do I benchmark that? I think we're, empirically my research has been doing too, and speaking to the points that Harvey Mahler, complex and others have said, I've been thinking about how do I understand all the stakeholders, not just within the project orbit to get it done but intersected? And that's what's driven a lot of my research on understanding, take bridge infrastructure, how does that affect not just the users or the people that have to deliver the project, like the construction companies, etc? But how does it impact the communities that are intersected, right? A lot of them are displaced. A lot of them, you know, for us to have this road go through, I benefited being in the car, but some community had to be displaced to change the right of way for that path. And that's why I think of infrastructure as one subset of major programmes. We often write in our papers that it's an arena for both intended and unintended connectivity. Me using the infrastructure, me using major programme, that's an intended use. Me delivering on the major programme is an intended use. The community that's not having this system come for them may not be welcome, right? And what that means is we need to start thinking about how do we measure outcomes, not just cost, scheduling, scope, quality of project, how that changes over time, very important, but also thinking about equity concerns, thinking about what did the project do? Does it help me employ; does it help in employment? Does it help in innovation? Things that often you measure after the project is transplanted but there are things you could do in the middle. How many? What percentage of small businesses are you bringing into the project? Is it just the big conglomerates or small businesses, I mean, this you could do even within the project. And so we're and by the way, this is this is not just because the research is intrinsically interesting, which I find, but increasingly what we're finding even in our program, sponsoring agencies are saying we've sent executives to come learn, and train. And this is not just in the Master of Science in Major Programmes, but also Major Project Leadership Academy. The sponsors are increasingly saying, we need to demonstrate the benefits of these programs, the social value, and we don't have a language to do that we really need your help in developing it. And so now, it's not us just because we're excited about the research. But this is becoming increasingly mandated, especially from sponsoring agency, the agencies sponsor the projects, especially government. And so that's opening a really exciting terrain, I think, for research, but a very empirically challenging one, because there's not a really clear set of standards. Right? So how far away from the project do you need to look at it? How many? What kind of outcomes? Is it employment, is it innovation, is it entrepreneurship? What forms of social demography should we be looking at? Let's just take disadvantage as an example. Is it by income? Is it by gender identification? Is it by ethnicity? Is it by a combination? Maybe it's, maybe that's not, maybe it's not about disadvantage. Maybe it's about a critical occupation. Where are the certain craftsmen of a certain kind of background or expertise? Is that what we should be measuring? There's not really a standard. And so until we develop that, it's going to be very hard for us to find a way to our point, what's common across these or what's not if we can't even agree on the outcome. And kind of go back to the beginning part of this question. Essentially, what I'm saying is, when we think of complexity, and if we take Harvey Mahler and other people's work seriously, Andrew Davies, others, we have to think about not just complexity inside the project itself as a system, but in the wider environment, especially the connection point being sociopolitical emergent complexity, some of that comes out of nowhere, and usually, it's outside of the project where you didn't have your lens placed. And so, you know, that kind of, kind of approach, it's early days, it's early days. I've been one of the people trying to advance and pioneers himself, even how to use your existing major programmes to sense where these disadvantage gaps are, we have a paper just came out, I think, in December actually, just starting to think, how do we even try to solve this problem? We know it's a problem, how do we try to come up with early stages to solve it?Riccardo  22:46What you just enunciated and from my learning, if we can see the major programmes, as you said, it's a system of systems. And ultimately, it's a system of systems goes through several phases, right? You got the planning, you got implementation, you got operation. And I think considering major programmes as systems or system of systems allows you to provide resilience to the major programme, right? Because ultimately, that's what, you know, these are very fragile things in the sense that, you know, you got all these external forces, that trying to influence, you know, the system, the political system changes every four years, right? And the major programme is supposed to be set up to survive the political system. So how do you go about creating that resiliency, and then you got, you know, you move from design, sort of a planning phase to design to construction, and, again, that I'm just taking one item, which is the political system, you know, it probably changes three times. And the budget program is supposed to be designed, at least that's what I've learned that it's supposed to be designed to survive that, because the cost is so high, that you can't have those influences and, you know, I might be controversial, but like, you know, it just two in my mind, it's, you know, there was lacking some of that resilience, because it didn't survive the political the various political cycles, and maybe that was not the only reason but certainly was one of the reasons you know, you have a changing government changing priorities and, and you if you haven't laid the groundwork, you know, the major problem might suffer.Daniel 24:37This provides a couple of interesting provocations one, which ties into our discussion of what we can learn empirically. I mean, it'd be really interesting to see so if you have system's systems, they intersect with each other, undoubtedly, what ends up happening is sometimes your cognitive focus is on one layer of the system, and you take for granted others which could come to your both your benefit in terms of focus, but also your detriment. And so there's kind of two questions that come from that. One is, is our cognitive awareness or salience of different parts of the system? Is there a way to do that, which kind of balances, I can't do everything, I can't pay attention to everything. And at the same time, I need to be mindful of interdependencies, and maybe a way to dynamically understand that maybe at a certain phase of the program, I focus on this layer. And another one, I focus on another one. That's one aspect. Another aspect that can be interesting is just treating the natural seeing if we can, instead of using the gates that you have to usually typically pass on a project (inaudible) formal. Is there something we learned about if we look at the systems or interlinkages? Is there a certain way in which the systems ebb and flow that there's some kind of clear phase changes just from the data? Oh, at this phase change, we shifted this way this was effective versus that way. Now, what that means, though, and I'm hoping from this podcast, what comes out of it is major programme managers willing to let researchers from the beginning, just be with them in the project and follow along. Right? And there's some opportunities, I think some are enterprising and doing this. Now, on the other hand, how do you then balance as a researcher delivering insights and findings that are both beneficial, but also say there's some detrimental issues in a way that your point acknowledges the political context? Because the problem is I think major programme managers want to know when things are going wrong and when things are improving. But if it becomes clear publicly something has gone wrong, then they're worried about the pressure they're going to get from constituents, policymakers saying, how did you, how come yet again, you're wasting money on x? But then what that does is it creates on the other side, a chilling effect that no one really wants to know when things are going, right. I mean, privately they do. Publicly, they don't. So even to do that kind of work, we're gonna have to think of a new platform, almost like I've been playing with this idea, kind of taking this model from Kiva, which is, you know, you want to bring people that needs support with people that match. I'm wondering if you could do the same thing with research, say, either policymakers or major programme managers have data. It's anonymized enough where it doesn't go back then. But enough where the research has enough detail and the researchers need data to do projects, they get to track them. And there's some way to anonymously reveal the results. Maybe there's some kind of mechanism or matching that would be for quantitative data. But for process models, you need usually qualitative data. So to answer the question (inaudible) is there some way to cognitively pay attention to different systems layers is there some natural phase changes would need access from the beginning of the project all the way to the end so you can actually match, chronicle these sequences. And also, there's some risks to it, you don't know as you're doing it, whether this project will succeed or not. Maybe you're doing it and it fails and you have a bunch of failures. And then you're learning different forms of failures, that's fine, too. But it requires also some mechanism by which practitioners feel comfortable and psychologically safe enough that they can allow researchers to come through who would still want to publish these general best practice insights, but in a way that separates them from kind of unintended consequences or pressures from that. The second point I'll make, which I think is really interesting, your use of the word resilience because I remember, I'm also you want to build kind of systems or major programmes to be resilient to these ebbs and flows. At the same time, if we take the whole kind of ecosystem or institutional perspective of in which the major programme is situated, you start having to ask yourself, what is the major programme really delivering? Is it entrenching existing interests or not? And why do I say this? I remember I was on a panel or as moderating a panel with Shalanda Baker, who is the, was advancing a lot of the energy justice initiatives at the DOE, really well-regarded developing the policies for the U.S. especially around energy. And I remember asking the president, how do you make, how do we make it more resilient? And she said something I think was so profound, I've been thinking about it daily, almost. She said, “I actually don't want these to be resilient. And I said why? She said because inequity, structural inequity is one of the most resilient things. And I thought that was so interesting because then you start asking yourself, yes, you want the programme itself to be resilient to deliver things. But if you start asking yourself, what is it we're asking these programmes to deliver? Is it really creating the kind of change we want or not? You then start asking, do you want the whole system of even selecting these projects to be resilient? I think that's quite interesting because if you think about it, structural inequities last over time. I mean, to give an example, very common example. We build infrastructure, understandably so to last as much as possible. So take a typical bridge. You know a bridge, the life cycle's what, 50 to 70 years, let's say? Imagine who was in the room in 19- let's say -50s, 1970s making those decisions, right? At best, you're using engineers who are looking at the best state of the practice, urban planners, the most well-intentioned, are looking at the best practice of the time usually thinking about the project itself. So obviously, communities are not in the room, even if it's well attended to because they don't think this is what matters at the time.Daniel 30:21At worst, you're intentionally putting people in the room that are going to do something with an agenda. Now, fast forward seventy years later, that bridge has housing next to it, has gas lines next to it, has electricity next to it, is completely locked in, and you as an engineer, you as a community worker, you as even as an anthropologist know certain people should have been in the room and we should change the practices, etc. It's really hard to revert because you would have to unravel all of those connections. I mean, to give a very simple, less controversial example. There was a bridge in Kentucky that they wanted to unravel the spaghetti junctions that led to it right. And the reason was that we now know from traffic planning that spaghetti junctions are not always the best way to deal with traffic, and they want to unravel it. To do that they would have had to remove all the houses, gasoline, such it would have added $2 billion to the project. You're dealing with a financial crisis; you're dealing with increased pressure from government to reduce costs. That's one of the first things to go. So they just worked within the existing footprint. And with that very rational decision, you've essentially kept an outdated process, outdated project in further perpetuity. Right? And so I think people when they argue these social challenges, I think, if they were so overt, in a program, those are the easier to deal with I think the fact that makes it so pernicious is it's absolutely rationalizable, you know, I'm focusing on one of the most famous studies actually of discrimination racism to get into it is by Thomas Schelling was a Nobel Prize winner in economics. And basically, argued was that most people argue the reason you have these things is that one group hates the other group, very reasonable conclusion. But he shows if I even have a preference, let's say he created like some cells and he said, I have a house and I just want half of the people around me to be like me, and think of yourself at a party right you go you want to build rapport, there's a real attraction for what we call homophily, finding similarity. And he shows if you run a similar simulation, just I want to be near people I like, you will get segregation. So it can, it doesn't have to be over perniciousness, it's you're doing the best things you can at the time. Right? And it perpetuates. Take another product, this is why it gets so fundamental visceral at this point, take a call for proposals, just to make this thing. So you have a call for proposals for contractors, let's say for a project, right, typical practice. And what are you going to typically do, you're going to go to people that have prior experience in doing this work. I mean, you need to trust that you don't want to be the one taking risk. Well, obviously that's going to already predispose the project to people with a lot of background. So anyone trying to get into the door, we've already just from the process, a very rational process, by the way, there's nothing wrong with this, you're already excluding certain groups, right? And then, you know, let's say another one, even innovation, let's say I'm a group that's doing A and I want to bring in B, well, a natural process, even as a reviewer as a project manager, well I know A, I can't say anything about B, so you refuse to review the proposal, anything else because you don't know anything about B. If everyone does that in a profession, then B will never see the light of day not because B has no merits, but no one feels equipped to do anything about it. And so then you can start seeing how innovations can get stifled. So to kind of make a long story short, I mean, we talked about the need for discussing resilience at the project level, different phase changes, maybe linkages across this and what to do, and then what that does, and also potentially, how to work practitioners working with researchers to make access possible in a way that kind of allows the findings to be unfiltered at the same time reconciles these programs in a system and then falling from that point. I think we need to be reflective of what is it we're trying to really deliver. I mean, it's not just the program, it's towards some outcome, and is that outcome, something that needs to be revised and changed? And so a second very cool question would be where do we want resilience in a major programme? I mean, obviously, you want it within the program, but do you want it in the selection process? Maybe not? Do you want it in the post validation where we don't do as well? Maybe not so maybe resilience is not great everywhere. And maybe it's really important in certain places. I think this is a really (inaudible) kind of push has been really I've been really thinking about this a lot, because it's a really visceral fundamental point. What is it we're actually doing and trying to achieve?Riccardo  35:12As a major programme practitioner, the major programme is at the center, right? That's where I put it. And that's a very centric view of, you know, building resilience, because the major programme for me or for practitioners, and even academic to a certain degree is the core. But you're right, societally, from a societal standpoint, it might not be, you know, the lack of resilience might actually be a positive thing because it afforded the conversation, the changes on something that, you know, is gonna last for 50, 70, 100 years and so you do want those conversations to be fluid and not to be stuck. No, I love it. I think you just gave me a new perspective that I probably gonna be thinking about every day, like you, now.Daniel 36:02It's also thinking that it's a conduit, right? It could be central but it's a conduit to some end. And then you have to ask yourself, is that the end we want to achieve? Right? So a lot of our grand challenges need to be achieved at scale. It's a conduit, and I guess we're thinking we focus so much on making the conduit good and resilient. The question is, is the end where we want to go? It's interesting.Riccardo  36:24It'd be interesting for some of the listeners to understand what the new trends in major programme are research? What does Oxford see as the new trends?Daniel 36:41I don't want to speak necessarily, for Oxford, but what I've seen is as an N of 1 faculty member there is I think, there is a real interest of and I think it's because of sponsors asking for it. I think also the research and we've discussed a lot of it is major programmes in the societal context. So major programmes in society, what is it we're doing? To ensure kind of social mobility? What is it we're doing to ensure outcomes for communities? I think it's a big area, not much research on. And I kind of think of it as, you know, fundamentally, you're doing major programs to uplift communities to better something. So in some sense, by definition, a lot of major programmes are to help the trailing edge. For those that are already at the at the leading edge, they often already have the research and other things, I mean, the means to do some. So often, a lot of big major programmes, infrastructure, social programs, are sometimes at the trailing edge. How do we understand that? How do we do that? I think the other one is now we're going to the leading edge is how are we going to deal with a lot of new technologies? I mean, one of the historical issues in a lot of our industries is that they've been in transient to change or innovation. And I guess the age-old question, it's kind of a timeless question is, is this new technology, whatever it is, AI, you know, and specific forms of AI like ChatGPT, or generative learning, generative models, additive manufacturing, modular infrastructure construction or modern methods of construction in general? Are these just the fact of the week? And they're not really changing how we do things? Or are they fundamentally changing things? And I think we have that kind of existential question all the time. I think another area is, personally, tools that address what I call the collapse time cycle of major programmes. There's an interesting tension in major programming the following: major programmes last, you know, take, five plus, six plus, 10 plus years. So you have to plan and you can't end they're big. So you can't just go off the hip, you have to have a plan. I mean, you can't, like I know we've been talking about process, but that can't have, that can't be rudderless because you're dealing with very big projects. So you have to have some kind of plan some anchor. At the same time, and this is where I think the grand challenges come not just as an end, but also as an input is the climate changing, right? So 5, 10 years from now, the climate is going to be so different. And by the way, the projects I'm building now, if we want to hit even half emissions by 2030, netzero 2050, basically, the projects I'm planning today, when they roll out, have to hit half emissions, at least, right? And they have to do it in a climate that's changing. I mean, that's if you think it's an insane proposition. But that's the task, right? So now I'm thinking, how can we develop tools? How can we use these technologies not just as how they're going to disrupt an industry but can we use them fundamentally, to help kind of build anticipatory heuristics to manage that? And this is where I think things like the trends that are happening on digital twins, augmented reality could be quite interesting. Because if I can help people see a digital twin and see what it could look like if flooding happens, or if I can show how the fluid dynamics in terms of heat of a server changes with temperature change, even if it's not perfect if I can get people in that mindset, my view is that can allow them to anticipate problems that wouldn't have happened before. So I think there's a really nice frontier of what are the tools and techniques, not just to coordinate like, you know, Arup, Acom, Jacobs, Matt McDonnell, Acadia, all of these groups have these like really nice digital twin systems to kind of help coordinate to great scaffolding, I like to call it but also thinking, how do I use that to kind of help people anticipate where things are happening, not that it's going to be perfect, but at least be aware so that when this happens, they're kind of mindful of it? And so I think that's another kind of really interesting trend. And to double click on the program society, one, I think, like we discussed, how are we going to have standards by which to assess for different infrastructure systems, how we're going to incorporate these kinds of community factors, outcomes, processes, how we're going to track them? Because right now, I mean, it's such a pressing issue, at least in the context I look at, I mean, look at leveling up in the UK, they're asking for quantifiable metrics to do it. The Department of Transportation in the U.S. has now made it as part of an executive order actually writ large across the U.S. government, the department (inaudible) are asking, can you come up with equity-based frameworks, etc., because they're asked to do it. And it's coming to a head because district attorneys, county attorneys are putting in Civil Rights Act claims against infrastructure, if they feel it's disadvantaging certain groups, there's literally cases right now going on. And because there's not a standard, what I find usually, I'm not saying it's always the case, but my opinion, when you don't have a standard for something, it usually settles out of court, because no one's sure where the courts gonna land. And so then you never get to, there's no way to build precedents to address the issue. And they always get settled out of court for kind of esoteric means for which we can't understand. And so we need to find ways to build that in. And ideally, I mean, my dream would be that this is directly incorporated in certifications for different groups, like associates or project management certifications, engineering, have, you know, they have chartered engineering in U.K. Professional Engineering licenses in the U.S. that this is actually part of their exams, like you have to have a kind of a social modular equity module where you think through this, but we don't have the research body yet. And then I think the last point, in terms of even just understanding trends, the way I think of me as a researcher, I try to ask myself, what's going to matter three to five years from now, the reason I say that is because when a practitioner comes now with a problem, by the time I can find the empirical base, the database to do it, I could come up with an answer, but I just worry, it's too late. Right? The thing is, the train has already passed, right? But if I could think of what's going to matter five years from now, and take that bet, as a researcher, then I can build the basis by which all of a sudden a lot of people come. And that's how my infrastructure and equity work started. I think equity is going to matter hugely. But it started five years ago, when I started seeing the murmurings of it in certain governments. And people thought it was crazy at the time. I mean, engineers were saying, Why is engineers care about this? And I understand why because it's like, they're focusing on the delivery of the brick-and-mortar project. This is not the not an indictment on the profession. It's their focus, right. And so when I finally built it, all of a sudden, then you had some high profile cases coming in, you have administrations focusing on equity. And all of a sudden, we're one of the few games in town because we spent time doing it. But it's a bet. I mean, there's other bets I've taken where people didn't care, right? So I think with these trends, just take them with, these are best of what's going to matter, five, three to five years from now, so that we're ready to come up with answers. So to kind of summarize major programmes in society, what are the standards we're going to use by which to do that? I think understanding various disruptive technologies, are they really changing things are not in terms of the industry, or even the major programme as a whole? And then we're flipping it? Can we use technologies to help us reassess fundamental, timeless questions about this time collapse timescale? Perhaps even upskilling for the new workforce we're going to need? Could we combine augmented reality with cognitive science understand what's activated in someone's brain when they're doing certain tasks? Could that help us build a whole new workforce, especially those transitioning from one form of energy to another? So these are the kinds of things that excite me, besides often, the age old questions of how do we understand successful projects? How do we understand to deliver things on budget, on time with benefits? I think those are always going to be there. But these are kind of new trends. I see.Riccardo  44:55  Yeah. I, certainly as a practitioner, not the things I think about it regularly so that's very stimulating. So we're coming to an end but before we conclude, we, you know, we can have you on the podcast and now talk about a little bit about the MMMPM programme, the Major Programme Leadership Academy, especially because, especially with a Major Project Leadership Academy, Major Programme Leadership Academy is in no, in Canada, we started to talk more and more about the need for having capable owners and having counterparts to the private sector, they're able to engage, engage in major programmes. And so, you know, anything you can share with the listeners about, you know, the MMPM, also the MMPLA and the benefits that brings to major programmes. Daniel 46:02I think, and I say this in the context of there's some really other fascinating programs coming along, that are really pushing this, I think, in general, there should be more of these in general, because there's such a demand for people that can do this stuff, that I think the pie is only going to get bigger of need. And so I don't, you know, I want to also preface that I don't think you know, our way is the only way, I tend to be very excited about it, but at the same time, there's others, I think most of the programmes, just to put it this in the context, I think of two things that are really important about the masters of major programme management philosophically. One is it's major programmes as a social science, really, from an organizational systems perspective, but other frameworks. Now, why do I say this is because there's quite a few other programmes, very important, very crucial in advancement, but are more from a civil engineering construction side, typically. So they either focus on the construction industry, and they get into the more technical details of how do you schedule in a certain way? How do you deal with contracting in this way, etc. And we cover some of that. But I think where we come into, is looking at it from a social science perspective, and maybe give you a new nuance about not just the hard side of things, but the softer side. And why do I say that is because it then influences the second philosophical point is that the kind of the kind of students, the kind of people we attract, are really what I call reflective practitioners. They're getting practical insights from this program, but it's through taking a step back from their experience, and thinking, oh, wow, this is a new insight, how could I have rethought this point. And that reflection brings a lot of practical value, new tools of oh my gosh, if I did this in this project, it's sometimes even they're doing it at the time. And so what that means is the kind of students we usually attract. To do that, well, you need people with a wide body of experience to leverage from so our students are actually the most experienced in Oxford. The average levels of experience is usually 15 years, that doesn't mean everybody has 15 years. But to give you a sense of experienced, average age is usually 40 plus, and we get a wide set of people, because when you think of major programmes as a social science, you're thinking about the organizations and systems underlying it. The major programme for which that could apply could be everything from infrastructure to social programmes, welfare programmes, even programmes designed in areas of extreme complexity and conflict, right? And so that's what I think the MSc in general, and you know, the kinds of things we discuss and look up and there's things on the website, but we focus on design, how do you design these fundamentally, they're temporary, but they have to fit with a sponsoring organization or set of organization that are permanent? And how do you balance that? How do you find the right people to fit with that? So on? The second one is around risk, like how do you think about risk? from a project perspective? How do you come up with ways to inform how you think of risk, and then even does the values that you place on a project that change how you kind of calculate things for risk? The next one systems, right? If you think of major programmes as entirely components, how do you think through that, etc. The fourth one's around stakeholder management, how do you manage stakeholders deliver things, which leads into the commercial leadership aspect, because usually, when you're kind of linking with stakeholders, once you've kind of reached some sets of agreements, the idea is you want to formalize, have a mechanism some way to do that. Then we have a research methods class, because you do a dissertation part of the reflection process is take something you're really passionate about for three to four months, and really think even more deeply of the literature and how it helps inform practical insights. And we have performance leadership, how do you lead these kinds of complex unwielding projects that steer them towards the outcomes you're interested in? And then we think of them in a globalized context. I think there's going to be some interesting changes coming up in the horizon that are exciting and happy to talk about it at a certain point but I think this is the general architecture to date. The major project leadership academy, similar orientation, but the kind of the kind of leaders we're dealing with is a bit different, right? This is, this is a programme that's been mandated by the infrastructure projects authority in the U.K. and essentially a few years back, there was concern of all the overage in major projects, and he said, can we develop some kind of training that can help us stop that. And so the idea there is, my understanding is virtual because I, Paul Chapman leads that programme, so I don't want to speak fully on this but my sense of the program is that the idea is that you have this major project portfolio from the government that has a certain any project of a certain level is part of that portfolio. And the leaders from that programme have to go through MPLA. And it's very focused on kind of leadership of yourself. What are the things you're strong at? Where do you need help that kind of notion of incomplete leader? How do you think about again, Matt, leading this in a temporary organization? How do you build the fits together? Commercial leadership, right? How do you contract correctly? How do you establish boundaries for which this programme was going to operate? And then technical leadership, what are the kinds of competencies, specialties you need to deliver. And there's different modules for that, at the end, there's an assessment of every leader, they present an oral presentation, and there's an assessment of whether they can meet the challenges of managing a project in that portfolio. So there's a much more there's these are leaders that are either managing these kinds of major projects now looking to the next one. And, and it's very much with the U.K. government's lens in mind, I think there's real ability, if of interest, to expand this to a variety of other country contexts. I think there could be other versions of MPLA, for all sorts of countries. And so I know, there's keen interest on that we've done that in the past. So if there are leaders in Canada, leaders in other places that want to do this, this is very possible, in fact I think we're very excited by this possibility because we know the U.K. is not the only one with these challenges. And at the same time, we know that these kinds of programmes, while it has a very clear core that's very effective. Also, by the way, they do 360s at the beginning and at the end of the project with both their superior subordinates, lateral peers to kind of and we try to see how did they change over time? Do they get a better sense of who they are? What did they learn? And so it's a very individual journey through a major project that you are managing, usually, in the U.K. Government at a certain level band, that's why it's this programme. And I would, I would love to see, where does this transport? I mean, could you do it in the U.S.? Could you do it in Canada? Could you do it in Germany? Could you do it in France, could you do it in New Zealand, Nigeria, Ghana, right? I mean, this is I think this is a real, it's a really effective model. It seems to have made a dent in these overruns. I mean, surely we still have overruns, this happens. But I think it's really reduced that. And so and in fact, a lot of now government officials that moved up in the organization. I've come out of that program. And I think, in terms of future, what I'm hoping with the program, personally, is I'm trying that the pitch I'm giving to corporations, especially is often when they're looking for sea level promotions, or, you know, chief level promotions, they're often looking for kind of a really amazing functional champion, one of the functions to bring them above. Now, the challenge is you hit this conundrum, right? The stuff that's made them really effective in their function is not what's going to make them strong as an executive, they all of a sudden go from this to like broadening out, and they and so you get this chasm that always happens, where you jump them up to that level and everything they did well, which is deliver really important specialist competencies. Now they have to manage things they don't are not experts in. So the pitch I've been trying to make sure if corporation understands is if you want to find the grooming ground for where you're going to find some really promising C-level appointments, look at those who are managing major programmes. They usally are getting to manage those programmes that are more mid-level earlier stage in career. They have a talent they've come in, that's why they're there. But all of a sudden, they're foisted with I gotta manage this billion-pound billion Canadian dollar billion dollar plus programme or even just really highly complex programme, and I gotta manage all sorts of different parts, all sorts of multiple disciplines. And if they're good at that, why can't they be a CTO, a COO, a CEO, that's what they're doing daily. And so I've been the pitch I've been trying to make for these programmes is you should be looking to bring your major programme leaders that you're thinking you want to groom for C-level, they should come to our programme because we will get we will take what they're already doing, give them a new kind of more generalized perspective with a bit of reflection on their own experience, and they'll come back they're ready to go. And I think this is something because you know, this takes some translation for people to understand what major programme is but that's the way to tell them is you're getting people who are already proficient in having a really deep expertise, and how to manage that expertise with a bunch of other functions, which is very unique. And so why not invest in those kinds of people because they could be your next C-level talents. And that's a pitch I use for this MMPM. I think MMPLA you could say the same thing. I mean, people are going back and forth in and out of private public sector. Yeah, so that's kind of how I see it. The slight, slight differences, but the same kind of orientation and motivation in mind.  Riccardo  55:37Yeah, if I can just had I mean, we, it was a few years back when it kind of dawned on me, this is before I did MMPM, but, you know, somebody, we were talking about $5 billion project, and somebody said, well, you know, it's a billion over five years, that's a billion a year, that's, that's a medium sized business, right? I mean, you're running a medium-sized business with that type of turnover. So yeah, I mean, the skills, the skills are there. If you're a project director or something like that, you probably have the traits or you're getting the experience that a CEO will get.Daniel 56:15There's an interesting problem in entrepreneurship. To your point, you're managing a small business, it's quite fleeting, if you think of it, it's almost like a small venture, right? I mean, not a small venture, but it's, let's say, a venture that's hit, you know, at least in terms of valuation, maybe a later stage Series C, private equity, maybe Series B, depending on whether it's a unicorn or not. And so essentially, that's what you're doing. And if you think of a startup, it's kind of temporary. I mean, most of them don't last beyond five years. And so, you know, there's a big challenge in entrepreneurship to your point, which is you found this amazing product. And now you want to grow a business out of it. And there's a massive chasm, so they even call they have a word for it's called valley of death. Yes. And I was thinking, the way we think about major programmes, we're thinking about how do you professionalize and scale something big quickly? To me, instead of thinking of startup canvas, lean startup, etcetera, those are valid ideas and insights, but they're really predicated on certain sectors. I mean, who else better to kind of solve that gap than major programme thinking? And I feel there's a really interesting gap to not just have major programmes in advancing its own right, but start speaking to other very prominent practical challenges. How do you scale a startup? That's about professionalizing your supply chain, professionalizing the structure of your organization, building coordination fast. I mean, who else would be prepared for that? In major programmes, I mean. That's a huge opportunity because it's a notoriously difficult problem. And what's nice about it, is even if you improve it, 2%, 3% that's all of a sudden, hundreds, maybe even thousands, tens of thousands of businesses that are now scaling, delivering jobs, the impact, even with just a small change in the needle is huge. And I think it's been too much thought about from an entrepreneurial perspective, which is, you know, product development driving this doing hypothesis tests, and they're not problematizing, that scaling approach. And I think that's where major programmes could have some really interesting impact and things we're actually discussing in the classroom as well, like, how do you then take that issue? Really nice translational opportunities as well, if you want.Riccardo  58:27I like it. It's really, really interesting concept. I might be thinking about that everyday too, also. Music: "A New Tomorrow" by Chordial Music. Licensed through PremiumBeat.DISCLAIMER: The opinions, beliefs, and viewpoints expressed by the hosts and guests on this podcast do not necessarily represent or reflect the official policy, opinions, beliefs, and viewpoints of Disenyo.co LLC and its employees.

What does this 1964 black comedy about a nuclear doomsday scenario tell us about the strategic thought surrounding potential nuclear war between the U.S. and the U.S.S.R. during the early years of the 1960s? How does the film play with the concept of a ‘doomsday machine' as described in the strategic literature of the day? How does the device described in the film reflect an option described by Leo Szilard, a key figure in the development of atomic weapons? How did economist/strategist Thomas Schelling's work influence Stanley Kubrick's script? How does the film explore the kind of thinking that backstops the notion of mutually assured destruction? How does the character Dr. Strangelove reflect Kubrick's desire to combine and satirize aspects of the real-world figures Wernher von Braun and John von Neumann.

On this episode of The DEB Show, host Debra Eckerling talks about wellbeing with authors/podcaster/speakers Howard Brown (Shining Brightly), Frank Love (Frank Relationships), and Kory M. Shrum (A Well Cared For Human). With the hustle and bustle of the holiday season, there's no better time to focus on your wellbeing.  Howard, Frank, and Kory shared their personal connections to the topic and their thoughts on why people find selfcare challenging. They also offered insights for reframing your mindset, as well as plenty of wellbeing tips. What is Wellbeing? - Kory: Having a really good relationship with yourself: your body, mind and emotions, creativity/spirit, and with other people - Frank: Inspiration, motivation, being a good child, parent, sibling, partner, friend - Howard: Self-care equals self love. Are you getting enough sleep, hydrating, taking care of yourself, so you can address everything else. If you can lift yourself up, you can take care of others Their Self-Care Practices - Howard: Finding his happy place/stress-free zone. His is on the basketball court. He also enjoys hiking, biking, meditation, and travel - Frank: Prints a 30-item checklist every night. This includes: get up, pray, 6 glasses of water, 3 exercise each day as part of self-care. In relationships, he goes through the 5 I's. Am I giving my time to my wife, am I loving, am I 100% in my relationship, do I demonstrate that I care about everything, am I intimately connected to my partner's history - Kory: Enjoys cultivating joy through creativity – painting, drawing, writing – and having loving connections in relationships. She focuses on her relationship with her mind and emotion (journaling, meditation), as well as her body (yoga, dance breaks) Goals - Kory: Figure out what you can say “No” to. Say “No” at least once a day/a few days a week. Choosing yourself will become a habit - Frank: Make a checklist with the basics you need to do each day - Howard: Track instances of kindness; find one act of kindness a day and write it down Final Thoughts - Kory: You're worthy of being taken care of, of taking care of yourself, and investing in yourself - Frank: Shares a quote by Thomas Schelling, “I've known since I was a child that bees can sting and then when they sting, they die. And nevertheless, they sting unable to explain to a bee that is stinging would merely hurt me, but would kill it. I behave with great respect toward bees.” It speaks to a higher level of love that we can give one another - Howard: If we can shine brightly a little bit each day for ourselves, for others, our neighbors and our community, I guarantee you the world will be a better place Learn More: Howard Brown: Shining Brightly.com Frank Love: FrankLove.com Kory M Shrum: KoryMShrum.com Debra Eckerling: TheDEBMethod.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Rose McDermott, Professor of International Relations at Brown University, argues that dominant theories of nuclear brinkmanship lack a nuanced understanding of the crucial factor of human psychology. She discusses the psychology of political leaders, the rational actor model, Thomas Schelling's notion of "threats that leave something to chance," the psychology of revenge, the coercive utility of nuclear weapons, and why nuclear deterrence may not be as stable as many people think, among other topics. Show NotesRose McDermott bioReid B.C. Pauly and Rose McDermott, “The Psychology of Nuclear Brinkmanship,” International Security 47, no. 3 (2023): pp. 9-51.James W. Davis and Rose McDermott, “The Past, Present, and Future of Behavioral IR,” International Organization 75, no. 1 (2022): pp. 147-177.Rose McDermott, Anthony C. Lopez, and Peter K. Hatemi, “'Blunt Not the Heart, Enrage It': The Psychology of Revenge and Deterrence,” Texas National Security Review 1, no. 1 (November 2017): pp. 68-88. Hosted on Acast. See acast.com/privacy for more information.

Why did the Middle East fall behind Europe despite being in a similar state in 1000 AD? How do modern authoritarians benefit from our tendency to falsify our preferences? Timur Kuran joins Amit Varma in episode 349 of The Seen and the Unseen to share his learnings from history -- and what they teach us about today. (FOR FULL LINKED SHOW NOTES, GO TO SEENUNSEEN.IN.) Also check out: 1. Timur Kuran on Twitter, Wikipedia, Google Scholar and Duke University. 2. Private Truths, Public Lies -- Timur Kuran. 3. The Long Divergence -- Timur Kuran. 4. Freedoms Delayed -- Timur Kuran. 5. You Will Know Them By Their Unpopular Views -- Bryan Caplan. 6. The Hindu Equilibrium -- Deepak Lal. 7. From Cairo to Delhi With Max Rodenbeck — Episode 281 of The Seen and the Unseen. 8. The Life and Times of Nilanjana Roy -- Episode 284 of The Seen and the Unseen. 9. James M Buchanan, Albert O Hirschman, Mancur Olson, Thomas Schelling and Kenneth Arrow. 10. The Logic of Collective Action -- Mancur Olson. 11. Micromotives and Macrobehavior -- Thomas Schelling. 12. Exit, Voice, and Loyalty -- Albert O Hirschman. 13. A Theory of Justice — John Rawls. 14. Anarchy, State and Utopia — Robert Nozick. 15. A Trump wave is on the way (2016) -- Glenn Reynolds. 16. It's Cascading Trump, It's Cascading Modi!  (2016) -- Amit Varma. 17. Instapundit -- Glenn Reynolds's blog. 18. Marginal Revolution. 19. Bari Weiss on Twitter, Substack and her own website. 20. VS Naipaul on Amazon. 21. Solomon Asch's experiments. 22. Irreversible Damage --  Abigail Shrier. 23. Luxury Beliefs. 24. Fixing Indian Education — Episode 185 of The Seen and the Unseen (w Karthik Muralidharan). 25. Education in India — Episode 77 of The Seen and the Unseen (w Amit Chandra). 26. Fund Schooling, Not Schools (2007) — Amit Varma. 27. The Beautiful Tree — James Tooley. 28. The Incredible Curiosities of Mukulika Banerjee — Episode 276 of The Seen and the Unseen. 29. The Pathan Unarmed — Mukulika Banerjee. 30. The Mystery of Capital — Hernando De Soto. 31. Belling the Cat. 32. Oppenheimer -- Christopher Nolan. 33. Censored -- Margaret E Roberts.. 34. The Art of Not Being Governed -- James C Scott. 35. Domination & the Arts of Resistance: Hidden Transcripts -- James C Scott. Amit Varma and Ajay Shah have launched a new video podcast. Check out Everything is Everything on YouTube. Check out Amit's online course, The Art of Clear Writing. And subscribe to The India Uncut Newsletter. It's free! Episode art: ‘Marketplace of Ideas' by Simahina.

Noam Chomsky joins Tyler to discuss why Noam and Wilhelm von Humboldt have similar views on language and liberty, good and bad evolutionary approaches to language, what he thinks Stephen Wolfram gets wrong about LLMs, whether he's optimistic about the future, what he thinks of Thomas Schelling, the legacy of the 1960s-era left libertarians, the development trajectories of Nicaragua and Cuba, why he still answers every email, what he's been most wrong about, and more. Read a full transcript enhanced with helpful links, or watch the full video.  Recorded February 27th, 2023 Other ways to connect Follow us on Twitter and Instagram Follow Tyler on Twitter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here. Photo credit: Duncan Rawlinson - Duncan.co

Episode 5: Notes and Sources  Vaughn Baltzly, "Publicized Goods, or the Promiscuity of the Public Goods Argument."   https://www.cambridge.org/core/journals/economics-and-philosophy/article/abs/concerning-publicized-goods-or-the-promiscuity-of-the-public-goods-argument/DE16529B673600C47C22B59C87D198A9 Bruce Benson, "Are Roads Public Goods?"https://www.springerprofessional.de/en/are-roads-public-goods-club-goods-private-goods-or-common-pools/11240720 Daniel Klein, KNOWLEDGE AND COORDINATION.   https://www.amazon.com/Knowledge-Coordination-Interpretation-Daniel-Klein/dp/019979412X/ref=sr_1_1?ie=UTF8&qid=1324300010&sr=8-1 Thomas Schelling, THE STRATEGY OF CONFLICT.  http://elcenia.com/iamapirate/schelling.pdf "Are Roads Really Public Goods?"  https://fee.org/articles/are-roads-really-public-goods/ "Economists on Federal Funding for NPR." National Public Radio, PLANET MONEY.  https://www.npr.org/transcripts/134863998 "The Fundamental Law of Road Congestion."  https://cityobservatory.org/the-fundamental-global-law-of-road-congestion/ "Tolls on the Connecticut Turnpike."   https://www.latimes.com/archives/la-xpm-1985-10-10-mn-15553-story.html If you have questions or comments, or want to suggest a future topic, email the show at taitc.email@gmail.com ! You can follow Mike Munger on Twitter at @mungowitz

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: "You'll Never Persuade People Like That", published by Zack M Davis on March 12, 2023 on LessWrong. Sometimes, when someone is arguing for some proposition, their interlocutor will reply that the speaker's choice of arguments or tone wouldn't be effective at persuading some third party. This would seem to be an odd change of topic. If I was arguing for this-and-such proposition, and my interlocutor isn't, themselves, convinced by my arguments, it makes sense for them to reply about why they, personally, aren't convinced. Why is it relevant whether I would convince some third party that isn't here? What's going on in this kind of situation? Why would someone think "You'll never persuade people like that" was a relevant reply? "Because people aren't truthseeking and treat arguments as soldiers" doesn't seem like an adequate explanation by itself. It's true, but it's not specific enough: what particularly makes appeal-to-persuading-third-parties an effective "soldier"? The bargaining model of war attempts to explain why wars are fought—and not fought; even the bitterest enemies often prefer to grudgingly make peace with each other rather than continue to fight. That's because war is costly. If I estimate that by continuing to wage war, there's a 60% chance my armies will hold a desirable piece of territory, I can achieve my war objectives equally well in expectation—while saving a lot of money and human lives—by instead signing a peace treaty that divides the territory with the enemy 60/40. If the enemy will agree to that, of course. The enemy has their own forecast probabilities and their own war objectives. There's usually a range of possible treaties that both combatants will prefer to fighting, but the parties need to negotiate to select a particular treaty, because there's typically no uniquely obvious "fair" treaty—similar to how a buyer and seller need to negotiate a price for a rare and expensive item for which there is no uniquely obvious "fair" price. If war is bargaining, and arguments are soldiers, then debate is negotiation: the same game-theoretic structure shines through armies fighting over the borders on the world's political map, buyer and seller haggling over contract items, and debaters arguing over the beliefs on Society's shared map. Strong arguments, like a strong battalion, make it less tenable for the adversary to maintain their current position. Unfortunately, the theory of interdependent decision is ... subtle. Although recent work points toward the outlines of a more elegant theory with fewer pathologies, the classical understanding of negotiation often recommends "rationally irrational" tactics in which an agent handicaps its own capabilities in order to extract concessions from a counterparty: for example, in the deadly game of chicken, if I visibly throw away my steering wheel, oncoming cars are forced to swerve for me in order to avoid a crash, but if the oncoming drivers have already blindfolded themselves, they wouldn't be able to see me throw away my steering wheel, and I am forced to swerve for them. Thomas Schelling teaches us that one such tactic is to move the locus of the negotiation elsewhere, onto some third party who has less of an incentive to concede or is less able to be communicated with. For example, if business purchases over $500 have to be approved by my hard-to-reach boss, an impatient seller of an item that ordinarily goes for $600 might be persuaded to give me a discount. And that's what explains the attractiveness of the appeal-to-persuading-third-parties. What "You'll never persuade people like that" really means is, "You are starting to persuade me against my will, and I'm laundering my cognitive dissonance by asserting that you actually need to persuade someone else who isn't here." When someone is desperate enou...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: "You'll Never Persuade People Like That", published by Zack M Davis on March 12, 2023 on LessWrong. Sometimes, when someone is arguing for some proposition, their interlocutor will reply that the speaker's choice of arguments or tone wouldn't be effective at persuading some third party. This would seem to be an odd change of topic. If I was arguing for this-and-such proposition, and my interlocutor isn't, themselves, convinced by my arguments, it makes sense for them to reply about why they, personally, aren't convinced. Why is it relevant whether I would convince some third party that isn't here? What's going on in this kind of situation? Why would someone think "You'll never persuade people like that" was a relevant reply? "Because people aren't truthseeking and treat arguments as soldiers" doesn't seem like an adequate explanation by itself. It's true, but it's not specific enough: what particularly makes appeal-to-persuading-third-parties an effective "soldier"? The bargaining model of war attempts to explain why wars are fought—and not fought; even the bitterest enemies often prefer to grudgingly make peace with each other rather than continue to fight. That's because war is costly. If I estimate that by continuing to wage war, there's a 60% chance my armies will hold a desirable piece of territory, I can achieve my war objectives equally well in expectation—while saving a lot of money and human lives—by instead signing a peace treaty that divides the territory with the enemy 60/40. If the enemy will agree to that, of course. The enemy has their own forecast probabilities and their own war objectives. There's usually a range of possible treaties that both combatants will prefer to fighting, but the parties need to negotiate to select a particular treaty, because there's typically no uniquely obvious "fair" treaty—similar to how a buyer and seller need to negotiate a price for a rare and expensive item for which there is no uniquely obvious "fair" price. If war is bargaining, and arguments are soldiers, then debate is negotiation: the same game-theoretic structure shines through armies fighting over the borders on the world's political map, buyer and seller haggling over contract items, and debaters arguing over the beliefs on Society's shared map. Strong arguments, like a strong battalion, make it less tenable for the adversary to maintain their current position. Unfortunately, the theory of interdependent decision is ... subtle. Although recent work points toward the outlines of a more elegant theory with fewer pathologies, the classical understanding of negotiation often recommends "rationally irrational" tactics in which an agent handicaps its own capabilities in order to extract concessions from a counterparty: for example, in the deadly game of chicken, if I visibly throw away my steering wheel, oncoming cars are forced to swerve for me in order to avoid a crash, but if the oncoming drivers have already blindfolded themselves, they wouldn't be able to see me throw away my steering wheel, and I am forced to swerve for them. Thomas Schelling teaches us that one such tactic is to move the locus of the negotiation elsewhere, onto some third party who has less of an incentive to concede or is less able to be communicated with. For example, if business purchases over $500 have to be approved by my hard-to-reach boss, an impatient seller of an item that ordinarily goes for $600 might be persuaded to give me a discount. And that's what explains the attractiveness of the appeal-to-persuading-third-parties. What "You'll never persuade people like that" really means is, "You are starting to persuade me against my will, and I'm laundering my cognitive dissonance by asserting that you actually need to persuade someone else who isn't here." When someone is desperate enou...

You believe in a lot of things. But what do you believe in the most?Go into the quiet security of your mind, and you will know that you value one of these more highly than the other four.GovernmentBusinessScienceFamilyDeity“American rates of religious affiliation have plummeted to their lowest point in the past 73 years. And nowhere are they lower than in knowledge-industry hubs like Silicon Valley, where high-skilled jobs are growing the fastest. If religion is in decline, I wondered, then what are Americans worshiping now? What has become our new religion? For many professionals, the answer is work. Work provides the identity, belonging, meaning and purpose that faith traditions once did.”– Carolyn Chen, NY Times, June 4, 2022“For thousands of years, our ancestors gazed at the world around us—the people and animals, the mountains and seas, the sun, moon and stars—and saw the divine. As the 19th Psalm puts it, ‘The heavens declare the glory of God, and the firmament shows his handiwork.' Even Isaac Newton saw a universe filled with purpose. In his masterwork, the Principia, he wrote: ‘This most beautiful system of the sun, planets, and comets, could only proceed from the counsel and dominion of an intelligent and powerful being.' Science advanced by leaps and bounds in the centuries following Newton, and scientists dialed back much of the God-talk. Many thinkers suggested that the universe runs like a mighty clockwork. Perhaps a creator was needed at the beginning, to set it going, but surely it now runs on its own. Einstein, who often spoke of God metaphorically, took a different tack. He rejected a personal deity, but saw a kind of pantheism—roughly, the identification of God with nature—as plausible.”– Dan Falk, Scientific American, July 27, 20211. Where do you place your highest confidence? Is it government?At one end of this spectrum, Communism believes that citizens should collectively own the means of production, distribution, and exchange which allocates products to everyone in the society. Karl Marx proposed a classless society in which everything would be shared by everyone.At the other end of the spectrum, Libertarianism says, “We, the members of the Libertarian Party, challenge the cult of the omnipotent state and defend the rights of the individual.” [LP.org) Ayn Rand famously proposed, “If government would just get out of the way, individual self-interest would create a better society!”To have confidence in government – or in the absence of government – is to believe in people. To have faith in people is Humanism. Is that where you have put your faith?2. Where do you place your highest confidence? Is it business, capitalism, free enterprise?“People create value and do good things when they have a profit motive.”“Capitalism creates jobs and provides a better lifestyle for everyone who participates. It is a virtuous cycle.”“Business people are problem solvers.”3. Where do you place your highest confidence? Is it science, medicine, technology?J.G. Ballard was enthusiastic about living in a technological society. He said, “Science and technology multiply around us. To an increasing extent they dictate the languages in which we speak and think. Either we use those languages, or we remain mute.”Napoleon Hill echoed J.G. Ballard. “Whatever the mind of man can conceive and believe, it can achieve.”But Thomas Schelling, winner of the Nobel Prize in Economic Sciences, disagreed with Napoleon Hill, saying, “The one thing a person cannot do, however brilliant they are, is write up a list of things that would never occur to them.”I like Thomas Schelling.Perhaps I am oversimplifying this, but my general feeling is that when we do a thing intuitively, we call it

Economist and public intellectual Glenn Loury joined Tyler to discuss the soundtrack of Glenn's life, Glenn's early career in theoretical economics, his favorite Thomas Schelling story, the best place to raise a family in the US, the seeming worsening mental health issues among undergraduates, what he learned about himself while writing his memoir, what his right-wing fans most misunderstand about race, the key difference he has with John McWhorter, his evolving relationship with Christianity, the lasting influence of his late wife, his favorite novels and movies, how well he thinks he will face death, and more. Read a full transcript enhanced with helpful links. Recorded January 11th, 2023 Other ways to connect Follow us on Twitter and Instagram Follow Tyler on Twitter Follow Glenn on Twitter Email us: cowenconvos@mercatus.gmu.edu Subscribe at our newsletter page to have the latest Conversations with Tyler news sent straight to your inbox. 

Stanley Kubrick parodia en Dr. Strangelove la crisis de los misiles de Cuba de 1962. Peter Sellers interpreta en ella al matemático que asesora a los americanos en un potencial conflicto nuclear con los rusos. El personaje está inspirado en el profesor John von Neumann, padre de la teoría de juegos. Dicen los manuales que la bomba atómica debería reducir la probabilidad de guerra, ahora que el coste es inasumible. Aunque los modelos no siempre se cumplen con humanos no siempre racionales.Este podcast está patrocinado por Equito App.Muchos españoles no pueden invertir en inmuebles porque los bancos exigen un capital alto antes de conceder un préstamo. Equito App llega para cambiar esto. Nuestra aplicación te permite invertir en inmuebles desde tan solo 100 euros, en menos de 2 minutos y con tu móvil, para recibir rentas cada mes. Equito tokeniza los activos inmobiliarios para hacerlos accesibles a todos. Es muy simple darte de alta: te descargas la app, le sacas una foto a tu DNI, eliges el tipo de inversión que quieres, firmas tu contrato desde el teléfono móvil y mandas el dinero. La inversión se realiza por transferencia bancaria, tarjeta de crédito o incluso criptomoneda. Con el código NB543, obtén 10 euros por una primera inversión de 200 y 30 euros por una de 500. Entra en Equito.app para conocer todos los detalles del proyecto.Índice:1.26. Un bonito consejo de José Luis Rodríguez Zapatero.11.26. Los buenos estudiantes mantienen la curiosidad.26.01. ¿Qué es la teoría de juegos?39.40. El dilema del prisionero.49.32. La tragedia de los comunes.1.13.17. 12 hombres sin piedad.1.17.30. El juego de la gallina con Puigdemont y Tsipras.1.40.56. ¿Teléfono rojo? Volamos hacia Moscú.1.54.15. El equilibrio de Nash en el lanzamiento de penaltis.Apuntes:Game theory: an applied introduction. José Luis Ferreira.La historia más lúdica jamás contada: Von Neumann. José Luis Ferreira.La historia más lúdica jamás contada: Schelling. José Luis Ferreira.La historia más lúdica jamás contada: Aumann. José Luis Ferreira.The strategy of conflict. Thomas Schelling.Theory of games and economic behavior. John von Neumann.How the mind works. Steven Pinker.The logic of life. Tim Harford.

(NOTAS Y ENLACES DEL CAPÍTULO AQUÍ: https://www.jaimerodriguezdesantiago.com/kaizen/144-cuanto-vale-una-vida/)¿Para qué sirven los días?Los días son el lugar donde vivimos.Llegan y nos despiertan.una y otra vez.Existen para que nos alegremos:¿dónde vivir, sino en los días?Y para responder a esa preguntavienen el sacerdote y el doctorcon sus largos abrigosque aletean por el campo.El otro día me crucé con este poema de Philip Larkin que me gustó y que se conectó en mi cabeza con algunas cosas que he leído y escuchado en los últimos meses. De esas que te llevan a reflexiones que no imaginaste. El de hoy es uno de esos capítulos. Si llevas tiempo escuchando kaizen, ya sabes que suceden de vez en cuando. Y es probable que te deje con más preguntas que respuestas, pero creo que eso es lo que hace que esas conexiones sean interesantes.Tendemos a pensar que los problemas matemáticos más difíciles son los que para ser resueltos necesitan esos garabatos extraños que alguna vez vimos en el colegio o en la universidad. Nos imaginamos gigantescas pizarras llenas de símbolos extraños como las letras griegas, las raíces cuadradas o las culebrillas que simbolizaban las integrales. Parecen conjuros escritos en un lenguaje demoníaco. Sin embargo, algunos de los problemas más difíciles no necesitan nada de eso. A veces, de hecho, la pregunta es simple y el cálculo sencillo, pero la respuesta es muy, muy, muy complicada. ¿Para qué sirven los días? O, por ejemplo, ¿cuánto vale una vida?Responder a esa pregunta tan fría y tan dura inevitablemente nos va a llevar a hablar de la muerte y de algunas cosas tristes, así que si no tienes un buen día a lo mejor quieres dejarlo para otro momento. Pero como hablábamos en aquel capítulo de la temporada pasada sobre las cosas que los nietos deberían saber, la muerte es parte de la vida. Y es también parte de la sociedad aunque a veces miremos para otro lado.

It was my great pleasure to speak once again to Tyler Cowen. His most recent book is Talent, How to Find Energizers, Creatives, and Winners Across the World.We discuss:how sex is more pessimistic than he is,why he expects society to collapse permanently,why humility, stimulants, intelligence, & stimulants are overrated,how he identifies talent, deceit, & ambition,& much much much more!Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here.Follow me on Twitter for updates on future episodes.More really cool guests coming up, subscribe to find out about future episodes!You may also enjoy my interviews of Bryan Caplan (about mental illness, discrimination, and poverty), David Deutsch (about AI and the problems with America's constitution), and Steve Hsu (about intelligence and embryo selection).If you end up enjoying this episode, I would be super grateful if you shared it. Post it on Twitter, send it to your friends & group-chats, and throw it up on any relevant subreddits & forums you follow. Can't exaggerate how much it helps a small podcast like mine.A huge thanks to Graham Bessellieu for editing this podcast and Mia Aiyana for producing its transcript.Timestamps(0:00) -Did Caplan Change On Education?(1:17) - Travel vs. History(3:10) - Do Institutions Become Left Wing Over Time?(6:02) - What Does Talent Correlate With?(13:00) - Humility, Mental Illness, Caffeine, and Suits(19:20) - How does Education affect Talent?(24:34) - Scouting Talent(33:39) - Money, Deceit, and Emergent Ventures(37:16) - Building Writing Stamina(39:41) - When Does Intelligence Start to Matter?(43:51) - Spotting Talent (Counter)signals(53:57) - Will Reading Cowen's Book Help You Win Emergent Ventures?(1:04:18) - Existential risks and the Longterm(1:12:45) - Cultivating Young Talent(1:16:05) - The Lifespans of Public Intellectuals(1:19:42) - Risk Aversion in Academia(1:26:20) - Is Stagnation Inevitable?(1:31:33) - What are Podcasts for?TranscriptDid Caplan Change On Education?Tyler Cowen   Ask Bryan about early and late Caplan. In which ways are they not consistent? That's a kind of friendly jab.Dwarkesh Patel   Okay, interesting. Tyler Cowen   Garrett Jones has tweeted about this in the past. In The Myth of the Rational Voter, education is so wonderful. It no longer seems to be true, but it was true from the data Bryan took from. Bryan doesn't think education really teaches you much. Dwarkesh Patel So then why is it making you want a free market?Tyler Cowen  It once did, even though it doesn't now, and if it doesn't now, it may teach them bad things. But it's teaching them something.Dwarkesh Patel   I have asked him this. He thinks that education doesn't teach them anything; therefore, that woke-ism can't be a result of colleges. I asked him, “okay, at some point, these were ideas in colleges, but now they're in the broader world. What do you think happened? Why did it transition together?” I don't think he had a good answer to that.Tyler Cowen   Yeah, you can put this in the podcast if you want. I like the free podcast talk often better than the podcast. [laughs]Dwarkesh Patel   Okay. Well yeah, we can just start rolling. Today, it is my great pleasure to speak to Tyler Cowen about his new book, “Talent, How to Find Energizers, Creatives, and Winners Across the World.” Tyler, welcome (once again) to The Lunar Society. Tyler Cowen   Happy to be here, thank you!Travel vs. HistoryDwarkesh Patel 1:51  Okay, excellent. I'll get into talent in just a second, but I've got a few questions for you first. So in terms of novelty and wonder, do you think travelling to the past would be a fundamentally different experience from travelling to different countries today? Or is it kind of in the same category?Tyler Cowen   You need to be protected against disease and have some access to the languages, and obviously, your smartphone is not going to work, right? So if you adjust for those differences, I think it would be a lot like travelling today except there'd be bigger surprises because no one else has gone to the past. Older people were there in a sense, but if you go back to ancient Athens, or the peak of the Roman Empire, you'd be the first traveller. Dwarkesh Patel   So do you think the experience of reading a history book is somewhat substitutable for actually travelling to a place? Tyler Cowen   Not at all! I think we understand the past very very poorly. If you've travelled appropriately in contemporary times, it should make you more skeptical about history because you'll realize how little you can learn about the current places just by reading about them. So it's like Travel versus History, and the historians lose.Dwarkesh Patel   Oh, interesting. So I'm curious, how does travelling a lot change your perspective when you read a work of history? In what ways does it do so? Are you skeptical of it to an extent that you weren't before, and what do you think historians are probably getting wrong? Tyler Cowen   It may not be a concrete way, but first you ask: was the person there? If it's a biography, did the author personally know the subject of the biography? That becomes an extremely important question. I was just in India for the sixth time, I hardly pretend to understand India, whatever that possibly might mean, but before I went at all, I'd read a few hundred books about India, and it's not like I got nothing out of them, but in some sense, I knew nothing about India. Now that I've visited, the other things I read make more sense, including the history.Do Institutions Become Left Wing Over Time?Dwarkesh Patel   Okay, interesting. So you've asked this question to many of your guests, and I don't think any of them have had a good answer. So let me just ask you: what do you think is the explanation behind Conquest's Second Law? Why does any institution that is not explicitly right-wing become left-wing over time?Tyler Cowen   Well, first of all, I'm not sure that Conquest's Second Law is true. So you have something like the World Bank which was sort of centrist state-ist in the 1960s, and by the 1990s became fairly neoliberal. Now, about what's left-wing/right-wing, it's global, it's complicated, but it's not a simple case of Conquest's Second Law holding. I do think that for a big part of the latter post-war era, some version of Conquest's Law does mostly hold for the United States. But once you see that it's not universal, you're just asking: well, why have parts? Why has the American intelligentsia shifted to the left? So that there's political science literature on educational polarization? [laughs] I wouldn't say it's a settled question, but it's not a huge mystery like “how Republicans act wackier than Democrats are” for example. The issues realign in particular ways. I believe that's why Conquest's Law locally is mostly holding.Dwarkesh Patel   Oh, interesting. So you don't think there's anything special about the intellectual life that tends to make people left-wing, and this issue is particular to our current moment?Tyler Cowen    I think by choosing the words “left-wing” you're begging the question. There's a lot of historical areas where what is left-wing is not even well defined, so in that sense, Conquests Law can't even hold there. I once had a debate with Marc Andreessen about this–– I think Mark tends to see things that are left-wing/right-wing as somewhat universal historical categories, and I very much do not. In medieval times, what's left wing and what's right wing? Even in 17th century England, there were particular groups who on particular issues were very left-wing or right-wing. It seems to me to be very unsatisfying, and there's a lot of fluidity in how these axes play out over real issues.Dwarkesh Patel   Interesting. So maybe then it's what is considered “left” at the time that tends to be the thing that ends up winning. At least, that's how it looks like looking back on it. That's how we categorize things. Something insightful I heard is that “if the left keeps winning, then just redefine what the left is.” So if you think of prohibition at the time, it was a left-wing cause, but now, the opposite of prohibition is left-wing because we just changed what the left is.Tyler Cowen    Exactly. Take the French Revolution: they're the historical equivalent of nonprofits versus 1830s restoration. Was everything moving to the left, between Robespierre and 1830? I don't pretend to know, but it just sure doesn't seem that way. So again, there seem to be a lot of cases where Conquest's Law is not so economical.Dwarkesh Patel   Napoleon is a great example of this where we're not sure whether he's the most left-wing figure in history or the most right-wing figure in history.Tyler Cowen 6:00Maybe he's both somehow.What Does Talent Correlate With?Dwarkesh Patel How much of talent or the lack thereof is a moral judgment for you? Just to give some context, when I think that somebody is not that intelligent, for me, that doesn't seem like a moral judgment. That just seems like a lottery. When I say that somebody's not hard working, that seems like more of a moral judgment. So on that spectrum, where would you say talent lies?Tyler Cowen   I don't know. My default is that most people aren't that ambitious. I'm fine with that. It actually creates some opportunities for the ambitious–– there might be an optimal degree of ambition. Well, short of everyone being sort of maximally ambitious. So I don't go around pissed off at unambitious people, judging them in some moralizing way. I think a lot of me is on autopilot when it comes to morally judging people from a distance. I don't wake up in the morning and get pissed off at someone in the Middle East doing whatever, even though I might think it was wrong.Dwarkesh Patel   So when you read the biographies of great people, often you see there's a bit of an emotional neglect and abuse when they're kids. Why do you think this is such a common trope?Tyler Cowen   I would love to see the data, but I'm not convinced that it's more common than with other people. Famous people, especially those who have biographies, on average are from earlier times, and in earlier times, children were treated worse. So it could be correlated without being causal. Now, maybe there's this notion that you need to have something to prove. Maybe you only feel you need to prove something if you're Napoleon and you're short, and you weren't always treated well. That's possible and I don't rule it out. But you look at Bill Gates and Mark Zuckerberg without pretending to know what their childhoods were like.  It sure sounds like they were upper middle class kids treated very well, at least from a distance. For example, the Collison's had great parents and they did well.Dwarkesh Patel   It could just be that the examples involving emotional neglect stuck out in my mind in particular.  Tyler Cowen   Yeah. So I'd really like to see the data. I think it's an important and very good question. It seems to me, maybe one could investigate it, but I've never seen an actual result.Dwarkesh Patel   Is there something you've learned about talent spotting through writing the book that you wish wasn't so? Maybe you found it disturbing, or you found it disappointing in some way. Is there something that is a correlate for talent that you wish wasn't? Tyler Cowen   I don't know. Again, I think I'm relatively accepting of a lot of these realities, but the thing that disappoints me a bit is how geographically clustered talent is. I don't mean where it was born, and I don't mean ethnically. I just mean where it ends up. So if you get an application, say from rural Italy where maybe living standards are perfectly fine–– there's good weather, there's olive oil, there's pasta. But the application just probably not that good. Certainly, Italians have had enough amazing achievements over the millennia, but right now, the people there who are actually up to something are going to move to London or New York or somewhere. So I find that a bit depressing. It's not really about the people. Dwarkesh Patel   When you do find a cluster of talent, to what extent can that be explained by a cyclical view of what's happening in the region? In the sense of the “hard times create strong men” theory? I mean at some point, Italy had a Renaissance, so maybe things got complacent over time.Tyler Cowen   Again, maybe that's true for Italy, but most of the talent clusters have been such for a long time, like London and New York. It's not cyclical. They've just had a ton of talent for a very long time. They still do, and later on, they still will. Maybe not literally forever, but it seems like an enduring effect.Dwarkesh Patel   But what if they leave? For example, the Central European Jews couldn't stay where they were anymore and had to leave.Tyler Cowen   Obviously, I think war can destroy almost anything. So German scientific talent took a big whack, German cultural talent too. I mean, Hungarian Jews and mathematics-–I don't know big of a trend it still is, but it's certainly nothing close to what it once was.Dwarkesh Patel   Okay. I was worried that if you realize that some particular region has a lot of talent right now, then that might be a one-time gain. You realize that India, Toronto or Nigeria or something have a lot of talent, but the culture doesn't persist in some sort of extended way. Tyler Cowen   That might be true for where talent comes from, but where it goes just seems to show more persistence. People will almost certainly be going to London for centuries. Is London producing a lot of talent? That's less clear. That may be much more cyclical. In the 17th century, London was amazing, right? London today? I would say I don't know. But it's not obvious that it's coming close to its previous glories. So the current status of India I think, will be temporary, but temporary for a long time. It's just a very big place. It has a lot of centres and there are things it has going for it like not taking prosperity for granted. But it will have all of these for quite a while–– India's still pretty poor.Dwarkesh Patel   What do you think is the difference between actual places where clusters of talent congregate and places where that are just a source of that talent? What makes a place a sink rather than a source of talent?Tyler Cowen   I think finding a place where people end up going is more or less obvious. You need money, you need a big city, you need some kind of common trade or linguistic connection. So New York and London are what they are for obvious reasons, right? Path dependence history, the story of making it in the Big Apple and so on. But origins and where people come from are areas that I think theory is very bad at understanding. Why did the Renaissance blossom in Florence and Venice, and not in Milan? If you're going back earlier, it wasn't obvious that it would be those places. I've done a lot of reading to try to figure this out, but I find that I've gotten remarkably not far on the question.Dwarkesh Patel   The particular examples you mentioned today–– like New York, San Francisco, London, these places today are kind of high stakes, because if you want to move there, it's expensive. Do you think that this is because they've been so talented despite this fact, or because you need some sort of exclusion in order to be a haven of talent?Tyler Cowen   Well, I think this is a problem for San Francisco. It may be a more temporary cluster than it ought to have been. Since it's a pretty recent cluster, it can't count on the same kind of historical path dependence that New York and Manhattan have. But a lot of New York still is not that expensive. Look at the people who work and live there! They're not all rich, to say the least. And that is an important part of why New York is still New York. With London, it's much harder, but it seems to me that London is a sink for somewhat established talent––which is fine, right? However, in that regard, it's much inferior to New York.Humility, Mental Illness, Caffeine, and Suits Dwarkesh Patel   Okay, I want to play a game of overrated and underrated with you, but we're going to do it with certain traits or certain kinds of personalities that might come in when you're interviewing people.Tyler Cowen   Okay, it's probably all going to be indeterminate, but go on.Dwarkesh Patel   Right. So somebody comes in, and they're very humble.Tyler Cowen   Immediately I'm suspicious. I figure most people who are going to make something of themselves are arrogant. If they're willing to show it, there's a certain bravery or openness in that. I don't rule out the humble person doing great. A lot of people who do great are humble, but I just get a wee bit like, “what's up with you? You're not really humble, are you?”Dwarkesh Patel   Maybe humility is a way of avoiding confrontation–– if you don't have the competence to actually show that you can be great. Tyler Cowen   It might be efficient for them to avoid confrontation, but I just start thinking that I don't know the real story. When I see a bit of arrogance, I'm less likely to think that it may, in a way, be feigned. But the feigning of arrogance in itself is a kind of arrogance. So in that sense, I'm still getting the genuine thing. Dwarkesh Patel   So what is the difference? Let's say a 15-year-old who is kind of arrogant versus a 50-year-old who is kind of arrogant, and the latter has accomplishments already while the first one doesn't. Is there a difference in how you perceive humility or the lack thereof?Tyler Cowen   Oh, sure. With the 50-year-old, you want to see what they have done, and you're much more likely to think the 50 year old should feign humility than the 15-year-old. Because that's the high-status thing to do–– it's to feign humility. If they can't do that, you figure, “Here's one thing they're bad at. What else are they bad at?” Whereas with the 15-year-old, maybe they have a chip on their shoulder and they can't quite hold it all in. Oh, that's great and fine. Let's see what you're gonna do.Dwarkesh Patel   How arrogant can you be? There are many 15 year olds who are really good at math, and they have ambitions like “I want to solve P ≠ NP” or “I want to build an AGI” or something. Is there some level where you just clearly don't understand what's going on since you think you can do something like that? Or is arrogance always a plus?Tyler Cowen   I haven't seen that level of arrogance yet. If a 15-year-old said to me, “in three years, I'm going to invent a perpetual motion machine,”  I would think “No, now you're just crazy.” But no one's ever said that to me. There's this famous Mark Zuckerberg story where he went into the VC meeting at Sequoia wearing his pajamas and he told Sequoia not to give him money. He was 18 at a minimum, that's pretty arrogant behavior and we should be fine with that. We know how the story ends. So it's really hard to be too arrogant. But once you say this, because of the second order effect, you start thinking: “Well, are they just being arrogant as an act?” And then in the “act sense”, yes, they can be too arrogant.Dwarkesh Patel   Isn't the backstory there that Mark was friends with Sean Parker and then Sean Parker had beef with Sequoia…Tyler Cowen   There's something like that. I wouldn't want to say off the top of my head exactly what, but there is a backstory.Dwarkesh Patel   Okay. Somebody comes in professionally dressed when they don't need to. They've got a crisp clean shirt. They've got a nice wash. Tyler Cowen How old are they?Dwarkesh Patel 20.Tyler Cowen They're too conformist. Again, with some jobs, conformity is great, but I get a little suspicious, at least for what I'm looking for. Though I wouldn't rule them out for a lot of things–– it's a plus, right?Dwarkesh Patel   Is there a point though, where you're in some way being conformist by dressing up in a polo shirt? Like if you're in San Francisco right now, it seems like the conformist thing is not to wear a suit to an interview if you're trying to be a software engineer.Tyler Cowen   Yeah, there might be situations where it's so weird, so over the top, so conformist, that it's actually totally non-conformist. Like “I don't know anyone who's a conformist like you are!” Maybe it's not being a conformist, or just being some kind of nut, that makes you interested again.Dwarkesh Patel   An overall sense that you get from the person that they're really content, almost like Buddha came in for an interview. A sense of wellbeing.Tyler Cowen   It's gonna depend on context, I don't think I'd hold it against someone, but I wouldn't take it at face value. You figure they're antsy in some way, you hope. You'll see it with more time, I would just think.Dwarkesh Patel   Somebody who uses a lot of nootropics. They're constantly using caffeine, but maybe on the side (multiple times a week), they're also using Adderall, Modafinil, and other kinds of nootropics.Tyler Cowen   I don't personally like it, but I've never seen evidence that it's negatively correlated with success, so I would try to put it out of my mind. I sort of personally get a queasy feeling like “Do you really know what you're doing. Is all this stuff good for you? Why do you need this?” That's my actual reaction, but again, at the intellectual level, it does seem to work for some people, or at least not screw them up too much.Dwarkesh Patel   You don't drink caffeine, correct? Tyler Cowen  Zero.Dwarkesh Patel Why?Tyler Cowen I don't like it. It might be bad for you. Dwarkesh Patel Oh really, you think so? Tyler Cowen People get addicted to it.Dwarkesh Patel    You're not worried it might make you less productive over the long term? It's more about you just don't want to be addicted to something?Tyler Cowen   Well, since I don't know it well, I'm not sure what my worries are. But the status quo regime seems to work. I observe a lot of people who end up addicted to coffee, coke, soda, stuff we know is bad for you. So I think: “What's the problem I need to solve? Why do it?”Dwarkesh Patel   What if they have a history of mental illness like depression or anxiety? Not that mental illnesses are good, but at the current margins, do you think that maybe they're punished too heavily? Or maybe that people don't take them seriously enough that they actually have a bigger signal than the people are considering?Tyler Cowen   I don't know. I mean, both could be true, right? So there's definitely positive correlations between that stuff and artistic creativity. Whether or not it's causal is harder to say, but it correlates. So you certainly should take the person seriously. But would they be the best Starbucks cashier? I don't know.How does Education Affect Talent?Dwarkesh Patel   Yeah. In another podcast, you've pointed out that some of the most talented people you see who are neglected are 15 to 17 year olds. How does this impact how you think? Let's say you were in charge of a high school, you're the principal of a high school, and you know that there's 2000 students there. A few of them have to be geniuses, right? How is the high school run by Tyler Cowen? Especially for the very smartest people there? Tyler Cowen   Less homework! I would work harder to hire better teachers, pay them more, and fire the bad ones if I'm allowed to do that. Those are no-brainers, but mainly less homework and I'd have more people come in who are potential role models. Someone like me! I was invited once to Flint Hill High School in Oakton, it's right nearby. I went in, I wasn't paid. I just figured “I'll do this.” It seems to me a lot of high schools don't even try. They could get a bunch of people to come in for free to just say “I'm an economist, here's what being an economist is like” for 45 minutes. Is that so much worse than the BS the teacher has to spew? Of course not. So I would just do more things like that.Dwarkesh Patel   I want to understand the difference between these three options. The first is: somebody like you actually gives an in-person lecture saying “this is what life is like”. The second is zoom, you could use zoom to do that. The third is that it's not live in any way whatsoever. You're just kind of like maybe showing a video of the person. Tyler Cowen   I'm a big believer in vividness. So Zoom is better than nothing. A lot of people are at a distance, but I think you'll get more and better responses by inviting local people to do it live. And there's plenty of local people, where most of the good schools are.Dwarkesh Patel   Are you tempted to just give these really smart 15-year-olds a hall pass to the library all day and some WiFi access, and then just leave them alone? Or do you think that they need some sort of structure?Tyler Cowen   I think they need some structure, but you have to let them rebel against it and do their own thing. Zero structure strikes me as great for a few of them, but even for the super talented ones, it's not perfect. They need exposure to things, and they need some teachers as role models. So you want them to have some structure.Dwarkesh Patel   If you read old books about education, there's a strong emphasis on moral instruction. Do you think that needs to be an important part of education? Tyler Cowen   I'd like to see more data. But I suspect the best moral instruction is the teachers actually being good people. I think that works. But again, I'd like to see the data. But somehow getting up and lecturing them about the seven virtues or something. That seems to me to be a waste of time, and maybe even counterproductive.Dwarkesh Patel   Now, the way I read your book about talent, it also seems like a critique of Bryan's book, The Case Against Education.Tyler Cowen   Ofcourse it is. Bryan describes me as the guy who's always torturing him, and in a sense, he's right.Dwarkesh Patel   Well, I guess more specifically, it seems that Bryan's book relies on the argument that you need a costly signal to show that you have talent, or you have intelligence, conscientiousness, and other traits. But if you can just learn that from a 1500 word essay and a zoom call, then maybe college is not about the signal.Tyler Cowen   In that sense, I'm not sure it's a good critique of Bryan. So for most people in the middle of the distribution, I don't think you can learn what I learned from Top 5 Emergent Ventures winners through an application and a half-hour zoom call. But that said, I think the talent book shows you my old saying: context is that which is scarce. And you're always testing people for their understanding of context. Most people need a fair amount of higher education to acquire that context, even if they don't remember the detailed content of their classes. So I think Bryan overlooks how much people actually learn when they go to school.Dwarkesh Patel   How would you go about measuring the amount of context of somebody who went to college? Is there something you can point to that says, “Oh, clearly they're getting some context, otherwise, they wouldn't be able to do this”?Tyler Cowen   I think if you meet enough people who didn't go to college, you'll see the difference, on average. Stressing the word average. Now there are papers measuring positive returns to higher education. I don't think they all show it's due to context, but I am persuaded by most of Brian's arguments that you don't remember the details of what you learned in class. Oh, you learn this about astronomy and Kepler's laws and opportunity costs, etc. but people can't reproduce that two or three years later. It seems pretty clear we know that. However, they do learn a lot of context and how to deal with different personality types.Dwarkesh Patel   Would you falsify this claim, though, that you are getting a lot of context? Is it just something that you had to qualitatively evaluate? What would have to be true in the world for you to conclude that the opposite is true? Tyler Cowen   Well, if you could show people remembered a lot of the facts they learned, and those facts were important for their jobs, neither of which I think is true. But in principle, they're demonstrable, then you would be much more skeptical about the context being the thing that mattered. But as it stands now, that's the residual. And it's probably what matters.Dwarkesh Patel   Right. So I thought that Bryan shared in the book that actually people don't even remember many of the basic facts that they learned in school.Tyler Cowen   Ofcourse they don't. But that's not the main thing they learn. They learn some vision of how the world works, how they fit into it, that they ought to have higher aspirations, that they can join the upper middle class, that they're supposed to have a particular kind of job. Here are the kinds of jerks you're going to meet along the way! Here's some sense of how dating markets work! Maybe you're in a fraternity, maybe you do a sport and so on. That's what you learned. Dwarkesh Patel   How did you spot Bryan?Tyler Cowen   He was in high school when I met him, and it was some kind of HS event. I think he made a point of seeking me out. And I immediately thought, “Well this guy is going to be something like, gotta keep track of this guy. Right away.”Dwarkesh Patel   Can you say more - what happened?Tyler Cowen   His level of enthusiasm, his ability to speak with respect to detail. He was just kind of bursting with everything. It was immediately evident, as it still is. Bryan has changed less than almost anyone else I know over what is now.. he could tell you how many years but it's been a whole bunch of decades.Dwarkesh Patel   Interesting. So if that's the case, then it would have been interesting to meet somebody who is like Bryan, but a 19 year old.Tyler Cowen   Yeah, and I did. I was right. Talent ScoutingDwarkesh Patel   To what extent do the best talent scouts inevitably suffer from Goodhart's Law? Has something like this happened to you where your approval gets turned into a credential? So a whole bunch of non-earnest people start applying, you get a whole bunch of adverse selection, and then it becomes hard for you to run your program.Tyler Cowen   It is not yet hard to run the program. If I needed to, I would just shut down applications. I've seen a modest uptick in bad applications, but it takes so little time to decide they're no good, or just not a good fit for us that it's not a problem. So the endorsement does get credentialized. Mostly, that's a good thing, right? Like you help the people you pick. And then you see what happens next and you keep on innovating as you need to.Dwarkesh Patel   You say in the book that the super talented are best at spotting other super talented individuals. And there aren't many of the super talented talent spotters to go around. So this sounds like you're saying that if you're not super talented, much of the book will maybe not do you a bunch of good. Results be weary should be maybe on the title. How much of talent spotting can be done by people who aren't themselves super talented?Tyler Cowen   Well, I'd want to see the context of what I wrote. But I'm well aware of the fact that in basketball, most of the greatest general managers were not great players. Someone like Jerry West, right? I'd say Pat Riley was not. So again, that's something you could study. But I don't generally think that the best talent scouts are themselves super talented.Dwarkesh Patel   Then what is the skill in particular that they have that if it's not the particular thing that they're working on?Tyler Cowen   Some intangible kind of intuition, where they feel the right thing in the people they meet. We try to teach people that intuition, the same way you might teach art or music appreciation. But it's not a science. It's not paint-by-numbers.Dwarkesh Patel   Even with all the advice in the book, and even with the stuff that isn't in the book that is just your inarticulable knowledge about how to spot talent, all your intuitions… How much of the variance in somebody's “True Potential” is just fundamentally unpredictable? If it's just like too chaotic of a thing to actually get your grips on. To what extent are we going to truly be able to spot talent?Tyler Cowen   I think it will always be an art. If you look at the success rates of VCs, it depends on what you count as the pool they're drawing from, but their overall rate of picking winners is not that impressive. And they're super high stakes. They're super smart. So I think it will mostly remain an art and not a science. People say, “Oh, genomics this, genomics that”. We'll see, but somehow I don't think that will change this.Dwarkesh Patel   You don't think getting a polygenic risk score of drive, for example, is going to be a thing that happens?Tyler Cowen   Maybe future genomics will be incredibly different from what we have now. Maybe. But it's not around the corner.Dwarkesh Patel   Yeah. Maybe the sample size is just so low and somebody is like “How are you even gonna collect that data? How are you gonna get the correlates of who the super talented people are?”Tyler Cowen   That, plus how genomic data interact with each other. You can apply machine learning and so on, but it just seems quite murky.Dwarkesh Patel   If the best people get spotted earlier, and you can tell who is a 10x engineer in a company and who is only a 1x engineer, or a 0.5x engineer, doesn't that mean that, in a way that inequality will get worse? Because now the 10x engineer knows that they're 10x, and everybody else knows that they're 10x, they're not going to be willing to cross subsidize and your other employees are going to be wanting to get paid proportionate to their skill.Tyler Cowen   Well, they might be paid more, but they'll also innovate more, right? So they'll create more benefits for people who are doing nothing. My intuition is that overall, inequality of wellbeing will go down. But you can't say that's true apriori. Inequality of income might also go up.Dwarkesh Patel   And then will the slack in the system go away for people who are not top performers? Like you can tell now, if we're getting better.Tyler Cowen   This has happened already in contemporary America. As I wrote, “Average is over.” Not due to super sophisticated talent spotting. Sometimes, it's simply the fact that in a lot of service sectors, you can measure output reasonably directly––like did you finish the computer program? Did it work? That has made it harder for people to get paid things they don't deserve.Dwarkesh Patel   I wonder if this leads to adverse selection in the areas where you can't measure how well somebody is doing. So the people who are kind of lazy and bums, they'll just go into places where output can't be measured. So these industries will just be overflowing with the people who don't want to work.Tyler Cowen   Absolutely. And then the people who are talented in the sectors, maybe they'll leave and start their own companies and earn through equity, and no one is really ever measuring their labor power. Still, what they're doing is working and they're making more from it.Dwarkesh Patel   If talent is partly heritable, then the better you get at spotting talent, over time, will the social mobility in society go down?Tyler Cowen   Depends how you measure social mobility. Is it relative to the previous generation? Most talent spotters don't know a lot about parents, like I don't know anything about your parents at all! The other aspect of spotting talent is hoping the talent you mobilize does great things for people not doing anything at all. That's the kind of automatic social mobility they get. But if you're measuring quintiles across generations, the intuition could go either way.Dwarkesh Patel   But this goes back to wondering whether this is a one time gain or not. Maybe initially they can help the people who are around them. Somebody in Brazil, they help people around them. But once you've found them, they're gonna go to those clusters you talked about, and they're gonna be helping the people with San Francisco who don't need help. So is this a one time game then?Tyler Cowen   Many people from India seem to give back to India in a very consistent way. People from Russia don't seem to do that. That may relate to the fact that Russia is in terrible shape, and India has a brighter future. So it will depend. But I certainly think there are ways of arranging things where people give back a lot.Dwarkesh Patel   Let's talk about Emergent Ventures. Sure. So I wonder: if the goal of Emergent Ventures is to raise aspirations, does that still work given the fact that you have to accept some people but reject other people? In Bayesian terms, the updates up have to equal the updates down? In some sense, you're almost transferring a vision edge from the excellent to the truly great. You see what I'm saying?Tyler Cowen   Well, you might discourage the people you turn away. But if they're really going to do something, they should take that as a challenge. And many do! Like “Oh, I was rejected by Harvard, I had to go to UChicago, but I decided, I'm going to show those b******s.” I think we talked about that a few minutes ago. So if I just crushed the spirits of those who are rejected, I don't feel too bad about that. They should probably be in some role anyway where they're just working for someone.Dwarkesh Patel   But let me ask you the converse of that which is, if you do accept somebody, are you worried that if one of the things that drives people is getting rejected, and then wanting to prove that you will reject them wrong, are you worried that by accepting somebody when they're 15, you're killing that thing? The part of them that wants to get some kind of approval?Tyler Cowen   Plenty of other people will still reject them right? Not everyone accepts them every step of the way. Maybe they're just awesome. LeBron James is basketball history and past a certain point, it just seems everyone wanted him for a bunch of decades now. I think deliberately with a lot of candidates, you shouldn't encourage them too much. I make a point of chewing out a lot of people just to light a fire under them, like “what you're doing. It's not gonna work.” So I'm all for that selectively.Dwarkesh Patel   Why do you think that so many of the people who have led Emergent Ventures are interested in Effective Altruism?Tyler Cowen   There is a moment right now for Effective Altruism, where it is the thing. Some of it is political polarization, the main parties are so stupid and offensive, those energies will go somewhere. Some of that in 1970 maybe went to libertarianism. Libertarianism has been out there for too long. It doesn't seem to address a lot of current problems, like climate change or pandemics very well. So where should the energy go? The Rationality community gets some of it and that's related to EA, as I'm sure you know. The tech startup community gets some of it. That's great! It seems to be working pretty well to me. Like I'm not an EA person. But maybe they deserve a lot of it.Dwarkesh Patel   But you don't think it's persistent. You think it comes and goes?Tyler Cowen   I think it will come and go. But I think EA will not vanish. Like libertarianism, it will continue for quite a long time.Dwarkesh Patel   Is there any movement that has attracted young people? That has been persistent over time? Or did they all fade? Tyler Cowen   Christianity. Judaism. Islam. They're pretty persistent. [laughs]Dwarkesh Patel   So to the extent that being more religious makes you more persistent, can we view the criticism of EA saying that it's kind of like a religion as a plus?Tyler Cowen   Ofcourse, yeah! I think it's somewhat like a religion. To me, that's a plus, we need more religions. I wish more of the religions we needed were just flat-out religions. But in the meantime, EA will do,Money, Deceit, and Emergent VenturesDwarkesh Patel   Are there times when somebody asks you for a grant and you view that as a negative signal? Let's say they're especially when well off: they're a former Google engineer, they wanna start a new project, and they're asking you for a grant. Do you worry that maybe they're too risk averse? Do you want them to put their own capital into it? Or do you think that maybe they were too conformist because they needed your approval before they went ahead?Tyler Cowen   Things like this have happened. And I asked people flat out, “Why do you want this grant from me?” And it is a forcing question in the sense that if their answer isn't good, I won't give it to them. Even though they might have a good level of talent, good ideas, whatever, they have to be able to answer that question in a credible way. Some can, some can't.Dwarkesh Patel   I remember that the President of the University of Chicago many years back said that if you rejected the entire class of freshmen that are coming in and accepted the next 1500 that they had to reject that year, then there'll be no difference in the quality of the admits.Tyler Cowen   I would think UChicago is the one school where that's not true. I agree that it's true for most schools.Dwarkesh Patel   Do you think that's also true of Emergent Ventures?Tyler Cowen   No. Not at all.Dwarkesh Patel   How good is a marginal reject?Tyler Cowen   Not good. It's a remarkably bimodal distribution as I perceive it, and maybe I'm wrong. But there aren't that many cases where I'm agonizing and if I'm agonizing I figure it probably should be a no.Dwarkesh Patel   I guess that makes it even tougher if you do get rejected. Because it wasn't like, “oh, you weren't a right fit for the job,” or “you almost made the cut.” It's like, “No, we're actually just assessing your potential and not some sort of fit for the job.” Not only were you just not on the edge of potential, but you were also way on the other edge of the curve.Tyler Cowen   But a lot of these rejected people and projects, I don't think they're spilling tears over it. Like you get an application. Someone's in Akron, Ohio, and they want to start a nonprofit dog shelter. They saw EV on the list of things you can apply to. They apply to a lot of things and maybe never get funding. It's like people who enter contests or something, they apply to EV. Nothing against non-profit dog shelters, but that's kind of a no, right? I genuinely don't know their response, but I don't think they walk away from the experience with some deeper model of what they should infer from the EV decision.Dwarkesh Patel   How much does the money part of Emergent Ventures matter? If you just didn't give them the money?Tyler Cowen   There's a whole bunch of proposals that really need the money for capital costs, and then it matters a lot. For a lot of them, the money per se doesn't matter.Dwarkesh Patel   Right, then. So what is the function of return for that? Do you like 10x the money, or do you add .1x the money for some of these things? Do you think they add up to seemingly different results? Tyler Cowen   I think a lot of foundations give out too many large grants and not enough small grants. I hope I'm at an optimum. But again, I don't have data to tell you. I do think about this a lot, and I think small grants are underrated.Dwarkesh Patel   Why are women often better at detecting deceit?Tyler Cowen   I would assume for biological and evolutionary reasons that there are all these men trying to deceive them, right? The cost of a pregnancy is higher for a woman than for a man on average, by quite a bit. So women will develop defense mechanisms that men maybe don't have as much.Dwarkesh Patel   One thing I heard from somebody I was brainstorming these questions with–– she just said that maybe it's because women just discuss personal matters more. And so therefore, they have a greater library.Tyler Cowen   Well, that's certainly true. But that's subordinate to my explanation, I'd say. There are definitely a lot of intermediate steps. Things women do more of that help them be insightful.Building Writing StaminaDwarkesh Patel   Why is writing skill so important to you?Tyler Cowen   Well, one thing is that I'm good at judging it. Across scales, I'm very bad at judging, so there's nothing on the EV application testing for your lacrosse skill. But look, writing is a form of thinking. And public intellectuals are one of the things I want to support. Some of the companies I admire are ones with writing cultures like Amazon or Stripe. So writing it is! I'm a good reader. So you're going to be asked to write.Dwarkesh Patel   Do you think it's a general fact that writing correlates with just general competence? Tyler Cowen   I do, but especially the areas that I'm funding. It's strongly related. Whether it's true for everything is harder to say.Dwarkesh Patel   Can stamina be increased?Tyler Cowen   Of course. It's one of the easier things to increase. I don't think you can become superhuman in your energy and stamina if you're not born that way. But I think almost everyone could increase by 30% to 50%, some notable amount. Dwarkesh Patel   Okay, that's interesting.Tyler Cowen   Put aside maybe people with disabilities or something but definitely when it comes to people in regular circumstances.Dwarkesh Patel   Okay. I think it's interesting because in the blog post from Robin Hanson about stamina, I think his point of view was that this is just something that's inherent to people.Tyler Cowen   Well, I don't think that's totally false. The people who have superhuman stamina are born that way. But there are plenty of origins. I mean, take physical stamina. You don't think people can train more and run for longer? Of course they can. It's totally proven. So it would be weird if this rule held for all these organs but not your brain. That seems quite implausible. Especially for someone like Robin, where your brain is just this other organ that you're gonna download or upload or goodness knows what with it. He's a physicalist if there ever was one.Dwarkesh Patel   Have you read Haruki Murakami's book on running?Tyler Cowen   No, I've been meaning to. I'm not sure how interesting I'll find it. I will someday. I like his stuff a lot.Dwarkesh Patel   But what I found really interesting about it was just how linked building physical stamina is for him to building up the stamina to write a lot.Tyler Cowen   Magnus Carlsen would say the same with chess. Being in reasonable physical shape is important for your mental stamina, which is another kind of simple proof that you can boost your mental stamina.When Does Intelligence Start to Matter?Dwarkesh Patel   After reading the book, I was inclined to think that intelligence matters more than I previously thought. Not less. You say in the book that intelligence has convex returns and that it matters especially for areas like inventors. Then you also say that if you look at some of the most important things in society, something like what Larry and Sergey did, they're basically inventors, right? So in many of the most important things in society, intelligence matters more because of the increasing returns. It seems like with Emergent Ventures, you're trying to pick the people who are at the tail. You're not looking for a barista at Starbucks. So it seems like you should care about intelligence more, given the evidence there. Tyler Cowen   More than who does? I feel what the book presents is, in fact, my view. So kind of by definition, I agree with that view. But yes, there's a way of reading it where intelligence really matters a lot. But it's only for a relatively small number of jobs.Dwarkesh Patel   Maybe you just started off with a really high priori on intelligence, and that's why you downgraded?Tyler Cowen   There are a lot of jobs that I actually hire for in actual life, where smarts are not the main thing I look for.Dwarkesh Patel   Does the convexity of returns on intelligence suggest that maybe the multiplicative model is wrong? Because if the multiplicative model is right, you would expect to see decreasing returns and putting your stats on one skill. You'd want to diversify more, right?Tyler Cowen   I think the convexity of returns to intelligence is embedded in a multiplicative model, where the IQ returns only cash out for people good at all these other things. For a lot of geniuses, they just can't get out of bed in the morning, and you're stuck, and you should write them off.Dwarkesh Patel   So you cite the data that Sweden collects from everybody that enters the military there. The CEOs are apparently not especially smart. But one thing I found interesting in that same data was that Swedish soccer players are pretty smart. The better a soccer player is, the smarter they are. You've interviewed professional basketball players turned public intellectuals on your podcast. They sound extremely smart to me. What is going on there? Why, anecdotally, and with some limited amounts of evidence, does it seem that professional athletes are smarter than you would expect?Tyler Cowen   I'm a big fan of the view that top-level athletic performance is super cognitively intense and that most top athletes are really extraordinarily smart. I don't just mean smart on the court (though, obviously that), but smart more broadly. This is underrated. I think Michelle Dawson was the one who talked me into this, but absolutely, I'm with you all the way.Dwarkesh Patel   Do you think this is just mutational load or––Tyler Cowen   You actually have to be really smart to figure out things like how to lead a team, how to improve yourself, how to practice, how to outsmart the opposition, all these other things. Maybe it's not the only way to get there, but it is very G loaded. You certainly see some super talented athletes who just go bust. Or they may destroy themselves with drugs: there are plenty of tales like that, and you don't have to look hard. Dwarkesh Patel   Are there other areas where you wouldn't expect it to be G loaded but it actually is?Tyler Cowen   Probably, but there's so many! I just don't know, but sports is something in my life I followed. So I definitely have opinions about it. They seem incredibly smart to me when they're interviewed. They're not always articulate, and they're sort of talking themselves into biased exposure. But I heard Michael Jordan in the 90s, and I thought, “That guy's really smart.” So I think he is! Look at Charles Barkley. He's amazing, right? There's hardly anyone I'd rather listen to, even about talent, than Charles Barkley. It's really interesting. He's not that tall, you can't say, “oh, he succeeded. Because he's seven foot two,” he was maybe six foot four tops. And they called him the Round Mound of Rebound. And how did he do that? He was smart. He figured out where the ball was going. The weaknesses of his opponents, he had to nudge them the right way, and so on. Brilliant guy.Dwarkesh Patel   What I find really remarkable is that (not just with athletes, but in many other professions), if you interview somebody who is at the top of that field, they come off really really smart! For example, YouTubers and even sex workers.Tyler Cowen   So whoever is like the top gardener, I expect I would be super impressed by them.Spotting Talent (Counter)signalsDwarkesh Patel   Right. Now all your books are in some way about talent, right? Let me read you the following passage from An Economist Gets Lunch, and I want you to tell me how we can apply this insight to talent. “At a fancy fancy restaurant, the menu is well thought out. The time and attention of the kitchen are scarce. An item won't be on the menu unless there's a good reason for its presence. If it sounds bad, it probably tastes especially good?”Tyler Cowen   That's counter-signaling, right? So anything that is very weird, they will keep on the menu because it has a devoted set of people who keep on ordering it and appreciate it. That's part of the talent of being a chef, you can come up with such things. Dwarkesh Patel   How do we apply this to talent? Tyler Cowen   Well, with restaurants, you have selection pressure where you're only going to ones that have cleared certain hurdles. So this is true for talent only for talents who are established. If you see a persistent NBA player who's a very poor free throw shooter like Shaquille O'Neal was, you can more or less assume they're really good at something else. But for people who are not established, there's not the same selection pressure so there's not an analogous inference you can draw.Dwarkesh Patel   So if I show up to an Emergent Ventures conference, and I meet somebody, and they don't seem especially impressive with the first impression, then I should believe their work is especially impressive. Tyler Cowen Yes, absolutely, yes. Dwarkesh Patel   Okay, so my understanding of your book Creative Destruction is that maybe on average, cultural diversity will go down. But in special niches, the diversity and ingenuity will go up. Can I apply the same insight to talent? Maybe two random college grads will have similar skill sets over time, but if you look at people on the tails, will their skills and knowledge become even more specialized and even more diverse?Tyler Cowen   There are a lot of different presuppositions in your question. So first, is cultural diversity going up or down? That I think is multi-dimensional. Say different cities in different countries will be more like each other over time.. that said, the genres they produce don't have to become more similar. They're more similar in the sense that you can get sushi in each one. But novel cuisine in Dhaka and Senegal might be taking a very different path from novel cuisine in Tokyo, Japan. So what happens with cultural diversity.. I think the most reliable generalization is that it tends to come out of larger units. Small groups and tribes and linguistic groups get absorbed. Those people don't stop being creative and other venues, but there are fewer unique isolated cultures, and much more thickly diverse urban creativity. That would be the main generalization I would put forward. So if you wanted to apply that generalization to talent, I think in a funny way, we come back to my earlier point: talent just tends to be geographically extremely well clustered. That's not the question you asked, but it's how I would reconfigure the pieces of it.Dwarkesh Patel   Interesting. What do you suggest about finding talent in a globalized world? In particular, if it's cheaper to find talent because of the internet, does that mean that you should be selecting more mediocre candidates?Tyler Cowen   I think it means you should be more bullish on immigrants from Africa. It's relatively hard to get out of Africa to the United States in most cases. That's a sign the person put in a lot of effort and ability. Maybe an easy country to come here from would be Canada, all other things equal. Again, I'd want this to be measured. The people who come from countries that are hard to come from like India, actually, the numbers are fairly high, but the roots are mostly pretty gated.Dwarkesh Patel   Is part of the reason that talent is hard to spot and find today that we have an aging population?  So then we would have more capital, more jobs, more mentorship available for young people coming up, than there are young people.Tyler Cowen   I don't think we're really into demographic decline yet. Not in the United States. Maybe in Japan, that would be true. But it seems to me, especially with the internet, there's more 15-year-old talent today than ever before, by a lot, not just by little. You see this in chess, right? Where we can measure performance very well. There's a lot more young talent from many different places, including the US. So, aging hasn't mattered yet. Maybe for a few places, but not here.Dwarkesh Patel   What do you think will change in talent spotting as society becomes older?Tyler Cowen   It depends on what you mean by society. I think the US, unless it totally screws up on immigration, will always have a very seriously good flow of young people that we don't ever have to enter the aging equilibrium the way Japan probably already has. So I don't know what will change. Then there's work from a distance, there's hiring from a distance, funding from a distance. As you know, there's EV India, and we do that at a distance. So I don't think we're ever going to enter that world..Dwarkesh Patel   But then what does it look like for Japan? Is part of the reason that Japanese cultures and companies are arranged the way they are and do the recruitment the way they do linked to their demographics? Tyler Cowen   That strikes me as a plausible reason. I don't think I know enough to say, but it wouldn't surprise me if that turned out to be the case.Dwarkesh Patel   To what extent do you need a sort of “great man ethos” in your culture in order to empower the top talent? Like if you have too much political and moral egalitarianism, you're not going to give great people the real incentive and drive to strive to be great.Tyler Cowen   You've got to say “great man or great woman ethos”, or some other all-purpose word we wish to use. I worry much less about woke ideology than a lot of people I know. It's not my thing, but it's something young people can rebel against. If that keeps you down, I'm not so impressed by you. I think it's fine. Let the woke reign, people can work around them.Dwarkesh Patel   But overall, if you have a culture or like Europe, do you think that has any impact on––Tyler Cowen   Europe has not woken up in a lot of ways, right? Europe is very chauvinist and conservative in the literal sense, and often quite old fashioned depending on what you're talking about. But Europe, I would say, is much less woke than the United States. I wouldn't say that's their main problem, but you can't say, “oh, they don't innovate because they're too woke”, like hang out with some 63 year old Danish guys and see how woke you think they are once everyone's had a few drinks.Dwarkesh Patel   My question wasn't about wokeism. I just meant in general, if you have an egalitarian society.Tyler Cowen   I think of Europe as less egalitarian. I think they have bad cultural norms for innovation. They're culturally so non-egalitarian. Again, it depends where but Paris would be the extreme. There, everyone is classified right? By status, and how you need to wear your sweater the right way, and this and that. Now, how innovative is Paris? Actually, maybe more than people think. But I still think they have too few dimensions of status competition. That's a general problem in most of Europe–– too few dimensions of status competition, not enough room for the proverbial village idiot.Dwarkesh Patel   Interesting. You say in the book, that questions tend to degrade over time if you don't replace them. I find it interesting that Y Combinator has kept the same questions since they were started in 2005. And of course, your co-author was a partner at Y Combinator. Do you think that works for Y Combinator or do you think they're probably making a mistake?Tyler Cowen   I genuinely don't know. There are people who will tell you that Y Combinator, while still successful, has become more like a scalable business school and less like attracting all the top weirdos who do amazing things. Again, I'd want to see data before asserting that myself, but you certainly hear it a lot. So it could be that Y Combinator is a bit stale. But still in a good sense. Like Harvard is stale, right? It dates from the 17th century. But it's still amazing. MIT is stale. Maybe Y Combinator has become more like those groups.Dwarkesh Patel   Do you think that will happen to Emergent Ventures eventually?Tyler Cowen   I don't think so because it has a number of unique features built in from the front. So a very small number of evaluators too. It might grow a little bit, but it's not going to grow that much. I'm not paid to do it, so that really limits how much it's going to scale. There's not a staff that has to be carried where you're captured by the staff, there is no staff. There's a bit of free riding on staff who do other things, but there's no sense of if the program goes away, all my buddies on staff get laid off. No. So it's kind of pop up, and low cost of exit. Whenever that time comes.Dwarkesh Patel   Do you personally have questions that you haven't put in the book or elsewhere because you want them to be fresh? For asking somebody who's applying to her for the grant? Tyler Cowen   Well, I didn't when we wrote the book. So we put everything in there that we were thinking of, but over time, we've developed more. I don't generally give them out during interviews, because you have to keep some stock. So yeah, there's been more since then, but we weren't holding back at the time.Dwarkesh Patel It's like a comedy routine. You gotta write a new one each year.Tyler Cowen That's right. But when your shows are on the air, you do give your best jokes, right?Will Reading Cowen's Book Help You Win Emergent Ventures?Dwarkesh Patel Let's say someone applying to emergent ventures reads your book. Are they any better off? Or are they perhaps worse off because maybe they become misleading or have a partial view into what's required of them?Tyler Cowen   I hope they're not better off in a way, but probably they are. I hope they use it to understand their own talent better and present it in a better way. Not just to try to manipulate the system. But most people aren't actually that good at manipulating that kind of system so I'm not too worried.Dwarkesh Patel   In a sense, if they can manipulate the system, that's a positive signal of some kind.Tyler Cowen   Like, if you could fool me –– hey, what else have you got to say, you know? [laughs]Dwarkesh Patel   Are you worried that when young people will encounter you now, they're going to think of you as sort of a talent judge and a good one at that so they're maybe going to be more self aware than whether––Tyler Cowen   Yes. I worry about the effect of this on me. Maybe a lot of my interactions become less genuine, or people are too self conscious, or too stilted or something.Dwarkesh Patel   Is there something you can do about that? Or is that just baked in the gig?Tyler Cowen   I don't know, if you do your best to try to act genuine, whatever that means, maybe you can avoid it a bit or delay it at least a bit. But a lot of it I don't think you can avoid. In part, you're just cashing in. I'm 60 and I don't think I'll still be doing this when I'm 80. So if I have like 18 years of cashing in, maybe it's what I should be doing.Identifying talent earlyDwarkesh Patel   To what extent are the principles of finding talent timeless? If you're looking for let's say, a general for the French Revolution, how much of this does the advice change? Are the basic principles the same over time?Tyler Cowen   Well, one of the key principles is context. You need to focus on how the sector is different. But if you're doing that, then I think at the meta level the principles broadly stay the same.Dwarkesh Patel   You have a really interesting book about autism and systematizers. You think Napoleon was autistic?Tyler Cowen   I've read several biographies of him and haven't come away with that impression, but you can't rule it out. Who are the biographers? Now it gets back to our question of: How valuable is history? Did the biographers ever meet Napoleon? Well, some of them did, but those people had such weak.. other intellectual categories. The modern biography is written by Andrew Roberts, or whoever you think is good, I don't know. So how can I know?Dwarkesh Patel   Right? Again, the issue is that the details that stick in my mind from reading the biography are the ones that make him seem autistic, right?Tyler Cowen   Yes. There's a tendency in biographies to storify things, and that's dangerous too. Dwarkesh Patel   How general across a pool is talent or just competence of any kind? If you look at somebody like Peter Thiel–– investor, great executive, great thinker even, certainly Napoleon, and I think it was some mathematician either Lagrangian or Laplace, who said that he (Napoleon) could have been a mathematician if he wanted to. I don't know if that's true, but it does seem that the top achievers in one field seem to be able to move across fields and be top achievers in other fields. I

Thomas Schelling brought a cross-disciplinary approach to nuclear strategy and the understanding of conflict. Professor Sir Lawrence Freedman, Emeritus Professor of War Studies at King's College London, joins us to discuss Schelling's pioneering work and reluctance to be seen as a game theorist. Thomas Schelling (1921–2016), was awarded the Nobel Prize in Economic Sciences for ‘having enhanced our understanding of conflict and cooperation through game-theory analysis'. Despite a reluctance to be seen as a game theorist and a distrust of pure mathematical modelling, he brought to the analysis of strategy concepts borrowed from economics, a discipline that had not previously played a role in military strategy-making. In the 1950s and 1960s, a revolution took place in the US strategic community: in the wake of the Second World War, systems analysis and operational research on the strategic bombing effort, civilians gained influence on defence policymaking. This was particularly true for nuclear strategy and international crisis management, on which Schelling focused his attention in the 1960s. Earlier, he had worked on the US Marshall Plan for the reconstruction of Europe, while later he would take a great interest in arms control. An advisor to successive UK governments, Professor Sir Lawrence Freedman was only in his early 30s when he published his Evolution of Strategy in 1981, and he was subsequently appointed Head of the then small Department of War Studies at King's College London. Under Sir Lawrence's leadership, the Department of War Studies grew to become a centre of excellence of worldwide renown that would educate future military leaders, civil servants, journalists and interested generalists from all parts of the globe.

In 1961, a group of American officials decided to play a game of war. Sitting around a table, they tried imagining a nuclear crisis - and how it could be resolved. The outcome of their thought experiment surprised them all, raising far reaching questions about the strength of America's nuclear strategy. Once nuclear weapons were unleashed into our world in the 1940s, it was obvious that a completely new set of rules of war had to be designed to prevent nuclear annihilation. In this episode, Matthew travels back to 1940s Santa Monica Beach to explore the origins of an idea that would become the guiding principle of nuclear strategy - deterrence. The threat posed by these new weapons had to be used to avoid war, not to start it. Matthew learns about the original think tank - the RAND corporation - where nuclear strategists first gave shape to nuclear deterrence and came up with ways to strengthen the credibility of the US government's deterrence strategy. The most bombastic thinker amongst them was Herman Kahn - the inspiration for Stanley Kubrick's Doctor Strangelove. Kahn's ideas were provocative in the way they urged leaders to consider just how many people they would be willing to kill in a nuclear war in order to make their nuclear threats appear credible. And as the 1960s progressed, the nuclear stockpile grew and tensions ratcheted up. The strategists gained more ground with successive US administrations, wargaming out scenarios in order to test the validity of deterrence. The ‘godfather of nuclear deterrence' and Nobel prize winning economist, Thomas Schelling, enters the frame just at the right time. Through Schelling's innovative work on nuclear deterrence, Matthew reflects on the importance of communication in nuclear crises. But in the 1980s, the Reagan administration played a new game. With a shocking outcome. Perhaps nuclear deterrence wouldn't always prevent war. Guests: Fred Kaplan - The national security columnist at Slate, the author writing about the history of nuclear strategy. Sir Lawrence Freedman - Emeritus professor of war at King's College London and nuclear strategy expert. Sharon Ghamari-Tabrizi - A historian of science and technology and the author of ‘The Worlds of Herman Kahn'. Graham Allison - Former dean of the Harvard Kennedy School of Government and nuclear expert. Paul Bracken - Professor of political science and business at Yale University and nuclear expert. A special thanks to Stephen Downes-Martin of the Connections War Gaming Conference for his generous help in sourcing archival footage of Thomas Schelling's keynote speech. Presenter: Matthew Syed Producer: Jake Otajovic Series Editor: Katherine Godfrey Researcher: Nadia Mehdi Sound Design: Rob Speight Theme tune by Ioana Selaru A Novel production for BBC Radio 4.

Welcome back friends and happy spring! (Or fall, as the case may be.) Today's show takes on a disarmingly simple question: What is language for? As in, why do we say things to each other? What do words do for us? Why do our languages label some aspects of the world, but not others? My guest today is Dr. Nick Enfield. He's Professor of Linguistics at the University of Sydney. Nick has authored or edited more than a dozen books on different aspects of human language and communication—books on word meaning, gesture, conversation, social interaction, the languages of Southeast Asia, and more. His latest book, just published by MIT press, is titled Language vs Reality: Why Language is Good for Lawyers and Bad for Scientists. In it, Nick argues that language is pretty awful at capturing reality—but actually that's fine, because capturing reality isn't the primary reason we use it. The real reason, in his view, is to coordinate with others. In this conversation, Nick and I flesh out this way of thinking about language as foremost a social coordination tool. Along the way, we talk about the two "reductions" that happen as brute reality gets transmuted into words. We discuss the economist Thomas Schelling and so-called Schelling maps. We talk about color words and plant names, salt and spoons, the insights of Benjamin Lee Whorf, the idea of “verbal overshadowing,” and a bunch of other phenomena and thinkers. As I say in the interview, Nick has one of the most expansive views of human language of anyone I know. He draws on anthropology, economics, primatology, developmental psychology, not to mention decades of his own fieldwork in Laos. That expansive—one might say, "many minded"—perspective is on full display here. Briefly, before we get to the conversation: if you have any ideas for future guests or topics—or want to lodge some criticisms—you can reach out to us at manymindspodcast@gmail.com. That's manymindspodcast@gmail.com. We're always eager to hear from listeners. Alright friends, now to my conversation with Dr. Nick Enfield. Enjoy!   A transcript of this episode will be available soon.   Notes and links 10:00 – Dr. Enfield's 2002 edited book on “ethnosyntax.” Here is a brief overview of serial verb constructions. 15:30 – Dr. Enfield has another book coming out later this year, with Jack Sidnell, titled Consequences of Language. 20:00 – The website of the influential semanticist Anna Wierzbicka, one of Dr. Enfield's early mentors. 22:45 – Roger Brown's classic 1958 paper ‘How shall a thing be called?' 24:30 – Daniel Dor's 2015 book, The Instruction of the Imagination. 25:40 – A popular article about the contributions of the economist Thomas Schelling. Another article on his notion of “focal points.” 37:00 – The classic treatment of color terms across languages is Berlin & Kay's 1991 book Basic Color Terms. 40:00 – Dr. Enfield spent a large portion of his early career at the MPI for Psycholinguistics. 44:45 – The classic treatment of plant names across cultures is Berlin's book, Ethnobiological Classification. 49:30 – Dr. Enfield has been documenting Kri, an indigenous language in Laos. 53:00 – The classic study on “verbal overshadowing” was done by Schooler & Engstler-Schooler in 1990. 58:20 – A classic paper by Krebs and Dawkins on signaling in nonhuman animals. 1:00:00 – The website of the influential (late) linguist Wallace Chafe. 1:08:30 – A widely-circulated 2013 paper by Dr. Enfield and colleagues on whether “huh” is a universal word. Spoiler: it seems to be. 1:10:00 - The researcher Jim Hurford has written several influential books on the evolution of language.   Dr. Enfield recommends: Origins of Human Communication, by Michael Tomasello Social Intelligence and Interaction, edited by Esther Goody Language, Thought, and Reality, by Benjamin Lee Whorf You can read more about Dr. Enfield's work at his website and follow him on Twitter.   Many Minds is a project of the Diverse Intelligences Summer Institute (DISI) (https://disi.org), which is made possible by a generous grant from the Templeton World Charity Foundation to UCLA. It is hosted and produced by Kensy Cooperrider, with help from assistant producer Cecilia Padilla. Creative support is provided by DISI Directors Erica Cartmill and Jacob Foster. Our artwork is by Ben Oldroyd (https://www.mayhilldesigns.co.uk/). Our transcripts are created by Sarah Dopierala (https://sarahdopierala.wordpress.com/). You can subscribe to Many Minds on Apple, Stitcher, Spotify, Pocket Casts, Google Play, or wherever you like to listen to podcasts. We welcome your comments, questions, and suggestions. Feel free to email us at: manymindspodcast@gmail.com. For updates about the show, visit our website (https://disi.org/manyminds/), or follow us on Twitter: @ManyMindsPod.

Los corresponsales de los 90 vivieron una buena vida. Remuneración excelente, rotación entre capitales y acceso a las altas esferas, con cenas privadas en casa del presidente. Miquel entró al mercado laboral al final de esa época, antes de que la irrupción de internet cerrara corresponsalías. Trabajó 3 años en Londres y 7 en Bruselas, cubriendo la actualidad para Expansión. Ahora, como subdirector de El Confidencial, lidera un proyecto riguroso y valiente, a pesar del cambio de modelo.Escucha el podcast en tu plataforma habitual:Spotify — Apple — iVoox — YouTubeArtículos sobre finanzas en formato blog:Substack Kapital — Substack CardinalApuntes:Troikoficciones. Miquel Roig.Todas las historias. Enric González.Memorias líquidas. Enric González.An essay on bargaining. Thomas Schelling.The wire. David Simon.Six big ideas. The Economist.Índice:0.28. Consejos para jóvenes periodistas.18.08. El día a día en la redacción de Expansión.36.02. Cómo consumir un Twitter menos politizado.39.15. No puedes ser independiente cuando no eres rentable.53.00. Las incentivos perversos de las métricas a corto plazo.1.11.12. Fiscalizar las mentiras del poder, repreguntando.1.24.40. Las respuestas guionizadas de Lagarde.1.28.04. ¿Qué es la prima de riesgo y por qué debería preocuparte?1.33.50. Las casas de apuestas pagan 7 euros por el Grexit.1.39.41. El juego de la gallina con Puigdemont y Varoufakis.1.52.30. Comunidad Europea del Carbón y del Acero.

Dr. J. Bruce Stewart, founder and CEO of Small World Solutions, joins Dr. Weaver to discuss the origins of systemic racism in America, the brain science behind unconscious bias and why unequal treatment diminishes the ability of both individuals and organizations to achieve their goals. In this Episode The reasons segregation persists The brain science behind unconscious bias The origins of systemic racism The importance of racial reconciliation Tips for addressing race and racism in the workplace Resources and Links Dr. J. Bruce Stewart Dr. Vanessa Weaver Small World Solutions Consulting Alignment Strategies Cultural Leadership: The New Chemistry of Leading Differently by Dr. J. Bruce Stewart NPR Poll about America’s attitudes about race Thomas Schelling’s chessboard exercise Harvard’s employee association test Follow Us on Social Media Alignment Strategies LinkedIn Facebook Diversity and Inclusion Television LinkedIn Facebook INROADS is a proud Workin’ It Out podcast partner. For more than 50 years, INROADS has provided students of color with leadership development training and paid internship opportunities with the country’s leading corporations.

In this episode, we left the polling booth to watch the movie Deterrence (2000) about a president fighting a nuclear war against Iraq while stranded in a diner during a snowstorm on the campaign trail. Are nuclear weapons useful to deter chemical, biological, or conventional weapon attacks? Can anyone stop an irrational president from ordering a nuclear strike? Is there a connection between the diner’s famous greasy chiliburger and the IBS News station? Tim Westmyer (@NuclearPodcast) and co-host Gabe answer these questions and more. Before we reach our positive control point, we recommend: -Kevin Pollak Chat Show (2009-2019) -Thomas Schelling, The Strategy of Conflict (1960) and Arms and Influence (1966) -The Contender (2002 movie) Check out our website, SuperCriticalPodcast.com, for more resources and related items. We aim to have at least one new episode every month. Let us know what you think about the podcast and any ideas you may have about future episodes and guests by reaching out at on Twitter @NuclearPodcast, GooglePlay, SoundCloud, TuneIn, Stitcher Radio, Facebook, SuperCriticalPodcast@gmail.com, and YouTube. Enjoy!

In the seventh of episode of PGP, Josh and Alex Lenser (SSP '21), a former air force officer specializing in nukes/missile defense, discuss U.S. nuclear force modernization, counterproliferation policy with regards to North Korea and Iran, and the difficulties of missile defense.

In The Myth of the Nuclear Revolution: Power Politics in the Atomic Age (Cornell University Press, 2020), Keir A. Lieber and Daryl G. Press tackle the central puzzle of the nuclear age: the persistence of intense geopolitical competition in the shadow of nuclear weapons. The book explains why the race to establish a nuclear deterrent can be destabilizing; how the condition of "mutual assured destruction" can unravel; and why some states threaten to wield the world's most destructive weapon against conventional threats. On the episode, I talked with Dr. Lieber and Dr. Press about the theoretical and policy implications of their work, the role of fear in international relations, and Thomas Schelling and his theory of a nuclear “taboo.” Dedicated listeners will also be treated to an important question. Which is better: "Dr. Strangelove" or "Failsafe?" John Sakellariadis is a 2020-2021 Fulbright US Student Research Grantee. He holds a Master's degree in public policy from the School of International and Public Affairs at Columbia and a Bachelor's degree in History & Literature from Harvard University. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/book-of-the-day

In The Myth of the Nuclear Revolution: Power Politics in the Atomic Age (Cornell University Press, 2020), Keir A. Lieber and Daryl G. Press tackle the central puzzle of the nuclear age: the persistence of intense geopolitical competition in the shadow of nuclear weapons. The book explains why the race to establish a nuclear deterrent can be destabilizing; how the condition of "mutual assured destruction" can unravel; and why some states threaten to wield the world’s most destructive weapon against conventional threats. On the episode, I talked with Dr. Lieber and Dr. Press about the theoretical and policy implications of their work, the role of fear in international relations, and Thomas Schelling and his theory of a nuclear “taboo.” Dedicated listeners will also be treated to an important question. Which is better: "Dr. Strangelove" or "Failsafe?" John Sakellariadis is a 2020-2021 Fulbright US Student Research Grantee. He holds a Master’s degree in public policy from the School of International and Public Affairs at Columbia and a Bachelor’s degree in History & Literature from Harvard University. Learn more about your ad choices. Visit megaphone.fm/adchoices

In The Myth of the Nuclear Revolution: Power Politics in the Atomic Age (Cornell University Press, 2020), Keir A. Lieber and Daryl G. Press tackle the central puzzle of the nuclear age: the persistence of intense geopolitical competition in the shadow of nuclear weapons. The book explains why the race to establish a nuclear deterrent can be destabilizing; how the condition of "mutual assured destruction" can unravel; and why some states threaten to wield the world’s most destructive weapon against conventional threats. On the episode, I talked with Dr. Lieber and Dr. Press about the theoretical and policy implications of their work, the role of fear in international relations, and Thomas Schelling and his theory of a nuclear “taboo.” Dedicated listeners will also be treated to an important question. Which is better: "Dr. Strangelove" or "Failsafe?" John Sakellariadis is a 2020-2021 Fulbright US Student Research Grantee. He holds a Master’s degree in public policy from the School of International and Public Affairs at Columbia and a Bachelor’s degree in History & Literature from Harvard University. Learn more about your ad choices. Visit megaphone.fm/adchoices

In The Myth of the Nuclear Revolution: Power Politics in the Atomic Age (Cornell University Press, 2020), Keir A. Lieber and Daryl G. Press tackle the central puzzle of the nuclear age: the persistence of intense geopolitical competition in the shadow of nuclear weapons. The book explains why the race to establish a nuclear deterrent can be destabilizing; how the condition of "mutual assured destruction" can unravel; and why some states threaten to wield the world’s most destructive weapon against conventional threats. On the episode, I talked with Dr. Lieber and Dr. Press about the theoretical and policy implications of their work, the role of fear in international relations, and Thomas Schelling and his theory of a nuclear “taboo.” Dedicated listeners will also be treated to an important question. Which is better: "Dr. Strangelove" or "Failsafe?" John Sakellariadis is a 2020-2021 Fulbright US Student Research Grantee. He holds a Master’s degree in public policy from the School of International and Public Affairs at Columbia and a Bachelor’s degree in History & Literature from Harvard University. Learn more about your ad choices. Visit megaphone.fm/adchoices

In The Myth of the Nuclear Revolution: Power Politics in the Atomic Age (Cornell University Press, 2020), Keir A. Lieber and Daryl G. Press tackle the central puzzle of the nuclear age: the persistence of intense geopolitical competition in the shadow of nuclear weapons. The book explains why the race to establish a nuclear deterrent can be destabilizing; how the condition of "mutual assured destruction" can unravel; and why some states threaten to wield the world’s most destructive weapon against conventional threats. On the episode, I talked with Dr. Lieber and Dr. Press about the theoretical and policy implications of their work, the role of fear in international relations, and Thomas Schelling and his theory of a nuclear “taboo.” Dedicated listeners will also be treated to an important question. Which is better: "Dr. Strangelove" or "Failsafe?" John Sakellariadis is a 2020-2021 Fulbright US Student Research Grantee. He holds a Master’s degree in public policy from the School of International and Public Affairs at Columbia and a Bachelor’s degree in History & Literature from Harvard University. Learn more about your ad choices. Visit megaphone.fm/adchoices

In The Myth of the Nuclear Revolution: Power Politics in the Atomic Age (Cornell University Press, 2020), Keir A. Lieber and Daryl G. Press tackle the central puzzle of the nuclear age: the persistence of intense geopolitical competition in the shadow of nuclear weapons. The book explains why the race to establish a nuclear deterrent can be destabilizing; how the condition of "mutual assured destruction" can unravel; and why some states threaten to wield the world’s most destructive weapon against conventional threats. On the episode, I talked with Dr. Lieber and Dr. Press about the theoretical and policy implications of their work, the role of fear in international relations, and Thomas Schelling and his theory of a nuclear “taboo.” Dedicated listeners will also be treated to an important question. Which is better: "Dr. Strangelove" or "Failsafe?" John Sakellariadis is a 2020-2021 Fulbright US Student Research Grantee. He holds a Master’s degree in public policy from the School of International and Public Affairs at Columbia and a Bachelor’s degree in History & Literature from Harvard University. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Mike talks about race, both in America generally and the legal system specifically. He uses the story of Italian internment in World War II to explore the idea of "otherness." Out of preferences and perceptions, as well as a history of identifying white culture with professionalism, the legal industry has created a context that's hostile to African Americans. Resolving that distance will only come after first owning our ugly history.   Episode Resources Connect with Mike Whelan  mike@lawyerforward.com  https://www.lawyerforward.com/ White Lawyering by Russell G Pearce: https://ir.lawnet.fordham.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=4073&context=flr  Why the US Needs Black Lawyers: https://www.theguardian.com/world/2015/may/11/why-the-us-needs-black-lawyers  Police killings can be captured in data. The terror police create cannot. https://www.vox.com/21292688/police-killings-data  Thomas Schelling, Micromotives and Macrobehavior: https://www.amazon.com/Micromotives-Macrobehavior-Thomas-C-Schelling/dp/0393329461/ 

In Episode 40, “This Too Shall Pass”, Blenheim Partners’ Gregory Robinson speaks to Mehrdad Baghai, Chairman of Alchemy Growth, a strategy advisory firm, and Global Chief Executive Officer of High Resolves, a not-for-profit organisation focused on immersive citizenship education programs for young people in Australia, Canada, the United States, Brazil and Mexico.Mehrdad is the author of New York Times bestseller As One, as well as international bestsellers The Alchemy of Growth and The Granularity of Growth. Mehrdad was previously an Executive Director of the CSIRO and worked at McKinsey and Company as a partner and co-leader of their worldwide Growth Practice.Greg and Mehrdad discuss leadership during a crisis, the difficult decisions that have to be made and dealing with the delicate topic of trust, especially now as we are in the midst of the coronavirus pandemic. Mehrdad also brings to light the unique growth opportunity brought about by a crisis; by looking within and asking the tough questions, we can rise with renewed maturity from this collective wakeup call.We hear about Mehrdad’s upbringing, and how his family moved from Iran to Canada to escape persecution. Drawing from his experiences, he had a keen interest in collective action, went on to study at Harvard University and worked with Nobel laureate Thomas Schelling. Mehrdad stresses the importance of messaging to inspire collective action, especially with the extraordinary circumstances we currently face.

Since the term was coined in 1956, artificial intelligence has been a kind of mirror that tells us more about our theories of intelligence, and our hopes and fears about technology, than about whether we can make computers think. AI requires us to formulate and specify: what do we mean by computation and cognition, intelligence and thought? It is a topic rife with hype and strong opinions, driven more by funding and commercial goals than almost any other field of science...with the curious effect of making massive, world-changing technological advancements even as we lack a unifying theoretical framework to explain and guide the change. So-called machine intelligences are more and more a part of everyday human life, but we still don’t know if it is possible to make computers think, because we have no universal, satisfying definition of what thinking is. Meanwhile, we deploy technologies that we don’t fully understand to make decisions for us, sometimes with tragic consequences. To build machines with common sense, we have to answer fundamental questions such as, “How do humans learn?” “What is innate and what is taught?” “How much do sociality and evolution play a part in our intelligence, and are they necessary for AI?”This week’s guest is computer scientist Melanie Mitchell, Davis Professor of Complexity at SFI, Professor of Computer Science at Portland State University, founder of ComplexityExplorer.org, and author or editor of six books, including the acclaimed Complexity: A Guided Tour and her latest, Artificial Intelligence: A Guide for Thinking Humans. In this episode, we discuss how much left there is to learn about artificial intelligence, and how research in evolution, neuroscience, childhood development, and other disciplines might help shed light on what AI still lacks: the ability to truly think.Visit Melanie Mitchell’s Website for research papers and to buy her book, Artificial Intelligence: A Guide for Thinking Humans. Follow Melanie on Twitter.Watch Melanie's SFI Community Lecture on AI.Join our Facebook discussion group to meet like minds and talk about each episode.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedInMore discussions with Melanie:Lex FridmanEconTalkJim RuttWBUR On PointMelanie's AMA on The Next Web

Robert Frank on TwitterHis new book “Under the Influence: Putting Peer Pressure to Work”His choice of influential book is Nobel Prize winner Thomas Schelling’s “Micromotives and Macrobehavior”Center for Advanced Study in the Behavioral Sciences@casbsstanford on twitter

Whether or not you think you hold them, stereotypes shape the lives of everyone on Earth. As human beings, we lack the ability to judge each situation as unique and different…and how we group novel experiences by our past conditioning, as helpful as it often is, creates extraordinary complications in society. As modern life exposes us to an increasing number of encounters with the other in which we do not have time to form accurate models of someone   or some place’s true identity, we find ourselves in a downward spiral of self-reinforcing biases — transforming how we practice law enforcement, justice, and life online. Our polarized, irrational world calls for an intense look at what it will take to humanize each other — at traffic stops, in court, on social media, and anywhere our doubt about an unfamiliar face can lead to tragic consequences.This week’s guest is Rajiv Sethi, Professor of Economics at Columbia University and External Professor at the Santa Fe Institute. In this episode, we discuss how biases in our attention and cognition lead to unfair outcomes on the streets and on the Web, and where we can look for hope in countervailing strategies.Visit our website for more information or to support our science and communication efforts.Join our Facebook discussion group to meet like minds and talk about each episode.Shadows of Doubt: Stereotypes, Crime, and the Pursuit of Justice by Brendan O’Flaherty & Rajiv Sethi (Harvard University Press).Rajiv’s Website.Albert Kao & Iain Couzin on collective intelligence and modular societies.Aumann’s agreement theorem.“We can’t disagree forever” (Geanakopolos & Polemarchakis).Raissa D’Souza on the Collapse of Networks.Geoffrey West on scaling laws and cities.Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

Before the 1960s, American politicians had never paid much attention to economists. But as the post-World War II boom began to sputter, economists gained influence and power. In The Economists' Hour, Binyamin Appelbaum traces the rise of the economists, first in the United States and then around the globe, as their ideas reshaped the modern world, curbing government, unleashing corporations and hastening globalization. Some leading figures are relatively well-known, such as Milton Friedman, the elfin libertarian who had a greater influence on American life than any other economist of his generation, and Arthur Laffer, who sketched a curve on a cocktail napkin that helped to make tax cuts a staple of conservative economic policy. Others stayed out of the limelight, but left a lasting impact on modern life: Walter Oi, a blind economist who dictated to his wife and assistants some of the calculations that persuaded President Nixon to end military conscription; Alfred Kahn, who deregulated air travel and rejoiced in the crowded cabins on commercial flights as the proof of his success; and Thomas Schelling, who put a dollar value on human life. Their fundamental belief? That government should stop trying to manage the economy. Their guiding principle? That markets would deliver steady growth, and ensure that all Americans shared in the benefits. But the Economists' Hour failed to deliver on its promise of broad prosperity. And the single-minded embrace of markets has come at the expense of economic equality, the health of liberal democracy, and future generations. Have a money question? Email me here. Please leave us a rating or review in Apple Podcasts. Connect with me at these places for all my content: https://www.jillonmoney.com/ https://twitter.com/jillonmoney https://www.facebook.com/JillonMoney https://www.instagram.com/jillonmoney/ https://www.youtube.com/c/JillSchlesinger https://www.linkedin.com/in/jillonmoney/ https://www.stitcher.com/podcast/jill-on-money https://apple.co/2pmVi50 "Jill on Money" theme music is by Joel Goodman, www.joelgoodman.com.

To mobilize America’s intellectual resources to meet the security challenges of the post–9/11 world, US Secretary of Defense Robert M. Gates observed that “we must again embrace eggheads and ideas.” But the gap between national security policymakers and international relations scholars has become a chasm. In Cult of the Irrelevant: The Waning Influence of Social Science on National Security (Princeton University Press, 2019), Michael Desch traces the history of the relationship between the Beltway and the Ivory Tower from World War I to the present day. Recounting key Golden Age academic strategists such as Thomas Schelling and Walt Rostow, Desch’s narrative shows that social science research became most oriented toward practical problem-solving during times of war and that scholars returned to less relevant work during peacetime. Social science disciplines like political science rewarded work that was methodologically sophisticated over scholarship that engaged with the messy realities of national security policy, and academic culture increasingly turned away from the job of solving real-world problems In the name of scientific objectivity, academics today frequently engage only in basic research that they hope will somehow trickle down to policymakers. Drawing on the lessons of this history as well as a unique survey of current and former national security policymakers, Desch offers concrete recommendations for scholars who want to shape government work. The result is a rich intellectual history and an essential wake-up call to a field that has lost its way. Beth Windisch is a national security practitioner. You can tweet her @bethwindisch. Learn more about your ad choices. Visit megaphone.fm/adchoices

To mobilize America’s intellectual resources to meet the security challenges of the post–9/11 world, US Secretary of Defense Robert M. Gates observed that “we must again embrace eggheads and ideas.” But the gap between national security policymakers and international relations scholars has become a chasm. In Cult of the Irrelevant: The Waning Influence of Social Science on National Security (Princeton University Press, 2019), Michael Desch traces the history of the relationship between the Beltway and the Ivory Tower from World War I to the present day. Recounting key Golden Age academic strategists such as Thomas Schelling and Walt Rostow, Desch’s narrative shows that social science research became most oriented toward practical problem-solving during times of war and that scholars returned to less relevant work during peacetime. Social science disciplines like political science rewarded work that was methodologically sophisticated over scholarship that engaged with the messy realities of national security policy, and academic culture increasingly turned away from the job of solving real-world problems In the name of scientific objectivity, academics today frequently engage only in basic research that they hope will somehow trickle down to policymakers. Drawing on the lessons of this history as well as a unique survey of current and former national security policymakers, Desch offers concrete recommendations for scholars who want to shape government work. The result is a rich intellectual history and an essential wake-up call to a field that has lost its way. Beth Windisch is a national security practitioner. You can tweet her @bethwindisch. Learn more about your ad choices. Visit megaphone.fm/adchoices

To mobilize America’s intellectual resources to meet the security challenges of the post–9/11 world, US Secretary of Defense Robert M. Gates observed that “we must again embrace eggheads and ideas.” But the gap between national security policymakers and international relations scholars has become a chasm. In Cult of the Irrelevant: The Waning Influence of Social Science on National Security (Princeton University Press, 2019), Michael Desch traces the history of the relationship between the Beltway and the Ivory Tower from World War I to the present day. Recounting key Golden Age academic strategists such as Thomas Schelling and Walt Rostow, Desch’s narrative shows that social science research became most oriented toward practical problem-solving during times of war and that scholars returned to less relevant work during peacetime. Social science disciplines like political science rewarded work that was methodologically sophisticated over scholarship that engaged with the messy realities of national security policy, and academic culture increasingly turned away from the job of solving real-world problems In the name of scientific objectivity, academics today frequently engage only in basic research that they hope will somehow trickle down to policymakers. Drawing on the lessons of this history as well as a unique survey of current and former national security policymakers, Desch offers concrete recommendations for scholars who want to shape government work. The result is a rich intellectual history and an essential wake-up call to a field that has lost its way. Beth Windisch is a national security practitioner. You can tweet her @bethwindisch. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this classic GameTek, Geoff talks about the work of Thomas Schelling.

Ein Gespräch mit Oliver Beige über dynamische Prozesse in der Mikroökonomik: Über Einfluss, Ideenpropagation und Nachbarschaftseffekte. Oliver Beige und Gudrun Thäter haben sich online über die große gemeinsame Schnittmenge im Musikgeschmack gefunden. Obwohl Oliver in Berlin lebt und Gudrun in Karlsruhe ist es schon vorgekommen, dass sie im gleichen Konzert waren ohne das rechtzeitig zu bemerken, weil sie sich persönlich noch nicht kannten. Im vergangenen Jahr fand Gudrun dann interessante Überlegungen zur aktuellen Anwendbarkeit der Ideen und Modelle von Malthus, die Oliver veröffentlicht hatte. Diese erwiesen sich als spannende Lektüre für die Studierenden der Modellbildungsvorlesung, die Gudrun gerade hielt. Damit war der Plan geboren, dass man sich nicht nur unbedingt einmal persönlich kennenlernen müsste, sondern bei nächster Gelegenheit auch für den Podcast einmal unterhalten sollte. Diese Gelegenheit bot sich im Juli 2017 nach einem Freiluftkonzert in der Kulturbrauerei in Berlin. Oliver ist Ökonom. Er hat 1993 in Karlsruhe sein Diplom in Wirtschaftsingenieurwesen erworben und sich anschließend in den Staaten umgesehen. Dort hat er 1997 einen Master of Business Administration (University of Illinois) abgeschlossen und sich schließlich im Rahmen seiner Promotion an der UC Berkeley mit der mathematischen Modellierung von Ideenpropagation und Entscheidungsprozessen in Netzwerken beschäftigt. Er hat dabei auch zwei Wellen von Innovation im Silicon Valley hautnah miterlebt. Was so einfach und grundlegend klingt ist tatsächlich eine sehr schwierig zu beantwortende Frage: Wie beeinflussen sich Mitglieder in einer Gruppe gegenseitig beim Finden von Entscheidungen? Während Soziologen gerne über gruppendynmische Prozesse diskutieren, arbeiten Ökonomen traditionell unter der vereinfachten Annahme, dass Entscheidungen als unabhängig voneinander getroffen werden - gestützt auf einer rein rationalen, isolierten Nutzenkalkulation. Erst seit Kurzem wird diese Annahme in der Ökonmie durch neue Modelle in Frage gestellt. Was jedoch modellhaft einen Zugang zum dynamischen Entscheidungsprozess in einer Gruppe verschaffen kann - in dem natürlich ganz viel Rückkopplung eingebaut werden muss - sind neuronale Netze - z.B. die Boltzmann-Maschine. Diese hatte Oliver in Karlsruhe kennen- und schätzen gelernt. Sie bilden ein stochastisches Feedback-Netzwerk, in dem man auch untersuchen kann, wie man zu einem Equilibrium kommen kann. Wie läuft denn so eine kollektive Entscheidung ab? Vorab hat jede/r in der Gruppe Präferenz - z.B. für einen bestimmten Film, den er oder sie gern in Begleitung anderer in der Gruppe sehen würde. Darüber wird gesprochen und schließlich teilt sich die Gruppe auf in Untergruppen, die im Kino den gleichen Film sehen. Im Gespräch werden die Präferenzen der anderen jeweils gewichtet in die eigene Entscheidung einfließen. Mathematisch wird das ausgedrückt in einer Nutzenfunktion, deren Wert maximiert wird. In der evolutionären Spieltheorie kann dieses dann als ein stochastischer Prozess modelliert werden, der mittels einer Potentialfunktion die Meinungsbildung der Gruppe als Equilibriumspfad darstellt. Von einem mehr abstrakten Level stellen sich auch die Fragen an ein so gewonnenes Equilibrium: a) Sind die Entscheidungen für die Gruppe die besten? b) Inwieweit beeinflusst die Struktur des sozialen Netzwerkes die Gruppenentscheidung? c) Kann die Gruppendynamik dazu führen, dass einzelne Mitglieder entgegen ihrer Präferenzen entscheiden (und damit das Axiom der offenbarten Präferenzen verletzen)? Zur Darstellung dieser Prozesse wandelte Oliver den traditionellen Entscheidungsbaum unter Ausnutzung der Markow-Eigenschaft in einen Entschediungsgraphen um. Dies war damals ein komplett neuer Ansatz und hat sich auch im großen Maßstab bis heute nicht durchgesetzt. Neu an der Arbeit war auch, dass zum ersten Mal im Zusammenhang der Netzwerkeffekte die Struktur des Netzwerkes betrachtet wurde. In der ursprünglichen Konzeption in der Arbeit von Michael Katz und Carl Shapiro wurde die Heterogenität des Netzwerkes noch explizit ausgeschlossen. Wie wichtig Nachbarschaftseffekte sind, weiß man in der Innovationsökonomik aber schon seit Zvi Griliches die schrittweise Verbreitung des ertragreicheren Hybridmaises in den USA über Mundpropaganda erforscht hatte. Diese Form der Ideenpropagation ist auch ein wichtiger Baustein in Jared Diamonds "Guns, Germs, & Steel" (das den Pulitzerpreis gewann). Großen Einfluss auf Olivers Arbeit haben die Arbeiten des Pioniers der Spieltheorie Thomas Schelling (Nobelpreisträger 2005), der so wichtige Begriffe wie Nachbarschaftseffekte, kritische Masse und das Konzept des Tipping points einführte. Heute setzt Oliver seine Kenntnisse über dynamische Prozesse bei Entscheidungen über Investitionen in Startups, insbesondere im Bereich der verknüpften Mobilität und der Verbreitung neuer Technologien wie z.B. Blockchain, ein. Literatur und weitere Informationen J. Diamond: Guns, Germs, and Steel. W.W. Norton, 1997. Thomas Schelling, Nobelist and game theory pioneer, 95 . Nachruf, Harvard Gazette 14.12.2016. O. Beige: Resurrecting Malthus and Ricardo Medium, 2016. O. Beige: Essays on Preference and Influence Dissertation an der University of California, Berkeley, 2006. J. H. Conway: Game of life Podcasts P. Stursberg: Social Choice, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 129, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2017. V. Caspari: Perfekte Gleichgewichte, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 61, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2015. K. Cindric: Kaufverhalten, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 45, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2015. S. Ritterbusch: Digitale Währungssysteme, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 32, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2014.

Sir Lawrence Freedman is Professor of War Studies at King’s College and the author of numerous books and publications to include Strategy: A History. In Episode 7 of the PME podcast, we talk about strategy. What is strategy and what it is not? We trace its historical roots, and discuss how traditional views of strategy still apply or do not apply to today’s conflicts and future conflicts. Additionally, Sir Lawrence Freedman was elected a Fellow of the British Academy in 1995 and awarded the CBE (Commander of the British Empire) in 1996. He was appointed Official Historian of the Falklands Campaign in 1997. He was awarded the KCMG (Knight Commander of St Michael and St George) in 2003. Finally, he was appointed in June 2009 to serve as a member of the official inquiry into Britain and the 2003 Iraq War.   Key Takeaways:  “Everyone has a plan until they get punched in the mouth.” -Mike Tyson •What is strategy? Strategy involves making decisions when faced with “other people with their own plans and interests.” It requires flexibility. Planning is part of strategy, but having a plan is not a strategy. “Problem solving” is a critical component of strategy.  •In late nineteenth century Germany, Field Marshall Helmuth von Moltke the elder was chief of staff of the Prussian Army. He was a student of Carl von Clausewitz. As a military strategist, Von Moltke emphasized the importance of flexibility. He said, “A plan breaks down on first contact.” Additionally, Field Marshall Helmuth von Moltke saw military strategy as a “system of expedients.” •Sir Lawrence Freedman explains that American foreign policy doctrine (i.e. the Nixon Doctrine, Carter Doctrine, Bush Doctrine, Obama Doctrine) is a means of “signaling” to the world America’s priorities or intentions. It is part of strategy, but it is not itself a strategy. •Sun Tzu’s influence on strategy was based on the idea of “cunning and cleverness.” However, the flaw in strategy based on cunning is when both sides attempt to use cunning and cleverness against each other. We talk about whether countries like Russia and China use Sun Tzu’s methods to achieve their strategic aims. What are Russia’s interests? A conversation I had with Steven Lee Myers on his book The New Tsar is worth checking out to discover more on that topic. •Thucydides was the first “realist” historian. He wrote a book about the Peloponnesian War between Sparta and Athens in a book appropriately called The History of the Peloponnesian War. Sir Lawrence Freedman sites a book called Destined for War by Graham Allison. Destined for War describes a theory called the “Thucydides Trap” which is based on the idea that one power becomes fearful of the rise of another power and postulates that this might have ramifications for current and future relations between the United States and China. However, Sir Lawrence Freedman sees some flaws in this theory, and thinks that Thucydides had an interest in trying to preserve the reputation of Pericles. •We discuss whether there is something “elemental” to strategy. Sir Lawrence Freedman looked at chimpanzees and how they form coalitions and act in strategic ways. Winston Churchill (not a chimpanzee rather British Prime Minister) was good at applying the chimp-like principles of creating coalitions to achieve his strategic aims. Churchill sought to partner with President Roosevelt to get the U.S. on the allied side. When Hitler invaded the Soviet Union, Churchill sought to build a coalition with Joseph Stalin and the Soviet Union to fight the Nazis.  •We talk about Napoleon and the development of strategy during the Enlightenment. Much like Thucydides rejection of the gods, the enlightenment rejected superstitious beliefs and emphasized what reason and human rationality could do to achieve strategic aims.    •Two critical 19th Century strategic thinkers include the Swiss officer, Antoine Henri Jomini, and the Prussian military theorist, Carl Von Clausewitz. Jomini emphasized the “decisive battle” strategy. Meanwhile, Clausewitz focused on the policy objectives of war. Clausewitz is also famous for developing the term “fog of war,” which refers to the uncertainty and the chaos of war when it involves two competing wills, each one trying to impose itself on the other. •The challenges of military strategy. How do wars end? We talk about “The Clausewitzian Challenge”, and I allude to my previous conversation with Gideon Rose, editor of Foreign Affairs, who wrote an excellent book called How Wars End. •Finally, we talk about the strategy of the Cold War and nuclear deterrence. Sir Lawrence Freedman mentions a strategist named Thomas Schelling, who is worth studying for his contributions to strategy based on Game Theory. We discuss the “rationality of irrationality,” which refers to the necessity to use caution based on a fear that one side might act out of irrationality. •Sir Lawrence Freedman has a new book coming out in the Fall of 2017 called, The Future of War: A History. The book focuses on how people in the past have tried to predict changes in warfare and predict the future of war.         For more information: Check out www.professionalmilitaryeducation.com Be sure to follow Sir Lawrence Freedman on Twitter @LawDavF and check out the book, Strategy: A History   Books and Resources mentioned in the podcast: Strategy: A History The New Tsar The History of the Peloponnesian War Destined for War On War The Art of War     HELP SPREAD THE WORD! If you like this interview and want to hear others, be sure to subscribe in iTunes. Support the show with written reviews, share on social media, and through word of mouth. For any requests for additional shows or guests, e-mail me: tim@professionalmilitaryeducation.com   Thanks for listening!

A few months ago, Tyler asked Patrick Collison, CEO of Stripe, to be on the show. Patrick agreed, but only under the condition that the be the one to do the interviewing. Thus, what follows is the conversation Patrick wanted to have with Tyler, not the one you wanted to have. Happily Patrick stayed true to the spirit of Conversations with Tyler, and their dialogue covers a wide range of topics including the the benefits of diverse monocultures, the state of macroeconomics, Donald Trump, the amazing economics faculty at GMU, Peter Thiel, Brian Eno, Thomas Schelling, why Twitter is underrated, and — most pressing of all — why Marginal Revolution is so strange looking. Transcript and links Follow Patrick on Twitter Follow Tyler on Twitter More CWT goodness: Facebook Twitter Instagram Email

I have never come across a mind quite like Tyler Cowen’s. The George Mason economist, and Marginal Revolution blogger, has an interesting opinion on, well, everything. He’s a genuine polymath who can talk knowledgeably about more subjects than I even know exist.So coming in to this interview, I had a simple plan: ask Cowen for his thoughts on as many topics as possible. And I think it worked out pretty well. We discuss everything from New Jersey to high school sports to finding love to smoked trout to nootropics to Thomas Schelling to Ayn Rand to social media to speed reading strategies to happy relationships to the disadvantages of growing up in Manhattan. And believe me when I say that is a small sampling of the topics we cover. We also talk about Tyler’s new book, “The Complacent Class,” which argues, in true Cowenian fashion, that everything we think we know about the present is wrong, and far from being an age of rapid change and constant risk, we have become a cautious, even stagnant, society. This as information dense a discussion as I’ve hosted on this podcast. I took a lot away from it, and I think you will too. Books:-Autobiography of John Stuart Mills-Derek Parfitt’s Reasons and Persons-Fisher Black’s On Business Cycles  Learn more about your ad choices. Visit megaphone.fm/adchoices

In the second part of our two part Star Trek marathon, we continue to boldly overanalyze what no one had deemed necessary to overanalyze before: the nuclear nonsense in the fail Star Trek spin-off pilot, Assignment: Earth. Why did countries think it was a good idea to put nuclear weapons in space? What does it look like when a nuke goes off in the vacuum of space? Is it still okay these days to name your cat Isis? With the continued help of our special guest and Star Trek enthusiast Gabe, we answer these questions and more. Before we attempt a gravitational slingshot maneuver around the sun to travel back in time, we recommended reading Outer Space in World Politics, an edited volume organized by Joseph Goldsen in 1963, especially the chapter by renowned nuclear deterrence scholar, Thomas Schelling, on "The Military Use of Bombardment Satellites." Also, check out Space Weapons, Earth Wars, a RAND Study prepared for the USAF in 2002 by Bob Preston, Dana Johnson, Sean Edwards, Michael Miller, Calvin Shipbaugh, http://www.rand.org/content/dam/rand/pubs/monograph_reports/2011/RAND_MR1209.pdf. Other sources to check out include: -Space Nuclear Weapon Test, https://www.youtube.com/watch?v=RKvvrNrCOnw -Space Nuclear Weapon Detonation EMP Electromagnetic Pulse, https://www.youtube.com/watch?v=lpTvqLfvLKU -The Unexpected Effects of Nukes in Space, https://www.youtube.com/watch?v=TRhStl7SQnM -Minuteman 1 ICBM Air Launch, https://www.youtube.com/watch?v=It7SQ546xRk -ICBM Basing Modes, ArmsControlWonk, June 12, 2012, http://www.armscontrolwonk.com/archive/205397/icbm-basing-modes/ -Robert Richardson, "Rocket Blitz From the Moon," Collier's Weekly, October 28, 1948, https://www.unz.org/Pub/Colliers-1948oct23-00024 We aim to have at least one new episode every month. Let us know what you think about the podcast and any ideas you may have about future episodes and guests by reaching out at on Twitter @NuclearPodcast, Facebook, GooglePlay, YouTube, or SuperCriticalPodcast@gmail.com.

Thomas C. Schelling, PhD Harvard economics, 1951, was on the Faculty of Yale University 1953-57, spent 1958-59 at the RAND Corporation, 1959-90 at Harvard, Department of Economics, Centre for International Affairs, and John F. Kennedy School of Government, and 1990-2005 at the University of Maryland’s Department of Economics and School of Public Policy. He was a fiscal analyst at the US Bureau of the Budget, 1945-46, did graduate work at Harvard, 1946-48, was in the Marshall Plan Mission to Denmark 1948-49, the European Office of the Marshall Plan, Paris, 1949-50, the White House Foreign Policy Staff, 1950-51, and the Executive Office of the President (foreign aid programs), 1951-53. His main theoretical interests have been bargaining, conflict and cooperation, racial segregation and techniques of self-management. His main policy interests have been nuclear weapons, the limitation of war, climate change, foreign aid and tobacco. From 1983-1989 he was founding director of the Institute for the Study of Smoking Behaviour and Policy at Harvard University. His major books are The Strategy of Conflict, 1960, Strategy and Arms Control (with Morton H, Halperin) 1961, Arms and Influence 1966, Micromotives and Macrobehaviour 1978, Choice and Consequence 1984, and Strategies of Commitment and Other Essays 2006. He has been elected to the National Academy of Sciences, the National Academy of Medicine, and the American Academy of Arts and Sciences, and is the recipient of the Frank E. Seidman Distinguished Award in Political Economy and the National Academy of Sciences Award for Behavioural Research Relevant to the Prevention of Nuclear War. In 2005 he received, jointly with Robert Aumann, the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel. Thomas Schelling lives with his wife, Alice Coleman Schelling, in Bethesda Maryland.

本期节目由 思客教学 赞助，思客教学正在进行 React 全端训练营招生, 如果报名时说明你是 Teahour 听众, 你将获得 100 元优惠, 我们也将获得 100元的赞助, 感谢你的支持. 本期为 Teahour.FM 三周年特别节目, 由 Terry 主持, 继续请到了他的最好基友 Jan, 聊聊基于 Ethereum 平台的开发. 我们的反馈邮箱是: hi@teahour.fm 欢迎你的来信, 我们将抽取有价值的来信和反馈在节目中播出. 主持人 Terry 的 blog 我如何把薪水从 50人民币/天 提升到 100美元/小时的 (1) 核心开发者宣布比特币实验失败 Sam Stephenson Sam tweet 1 Sam tweet 2 Asset Pipeline Prototype JS Backbone JS rbenv rvm Adrian Holovaty Adrian tweet Adrian’s presentation at 37Singal Django: wanxiang blockchain labs 德勤(Deloitte) Ethereum Vitalik Vitalik github account Vitalik Blog Bitcoin Magazine The Zen of Python RubyMotion Halting Problem Toward a 12-second Block Time Casper Riak Formalize Casper Binary Sharding Public vs Private Blockchain Raft Consensus Algorithm 同态加密 Ethereum: the World Computer Thomas Schelling SchellingCoin lightning network State Channels IPFS Swarm Storj Slock 百万格子 庞氏合约 RANDAO Alarm Clock ethfans CloudMagic Special Guest: Jan.

Today on the show, Michael Covel speaks with Tyler Cowen. Cowen is an American economist, academic, and writer. He’s the co-author of the popular economics blog Marginal Revolution. Covel and Cowen discuss Sabermetrics and the new idea that we will be judged by numbers; Cowen’s early experience studying under Nobel Prize winner Thomas Schelling; the notion of thinking in terms of paradoxes; why we’re moving from the 1% to the 15%; “average is over” and why Singapore and Israel are the two places in the world where this has come true first; problems because of “average is over” in Singapore; pure free markets; thinking about why past regulations have failed; how computers change us; globalization, where things are headed, and what we should be prepared for; zero marginal product workers. For more information on Tyler Cowen, go to marginalrevolution.com. Want a free trend following DVD? Go to trendfollowing.com/win.

Last night, I sat down to talk nuclear strategy with William Rosenau of the Center for Naval Analysis, defense analyst Elbridge Colby, Robert Zarate of the Foreign Policy Initiative, and Stanley Orman – a former nuclear arms wizard who saved the U.S. and U.K. nuclear arsenals from corrosion in the 1960s.  It was a fascinating discussion during which I learned a great deal about nuclear arms, the Cold War, and giants of strategy like Thomas Schelling, Herman Kahn, and Albert Wohlstetter. Have a listen and read Stanley’s new book, An Uncivil Civil Servant.   Ryan Evans is the assistant director of the Center for the National Interest and the editor-in-chief of War on the Rocks.    Image:  Free Grunge Textures, Flickr

Tim Harford tells the story of Thomas Schelling, an economist who helped America and the Soviet Union to avoid nuclear war.

A panel of distinguished nuclear experts honor Morton Halperin and Thomas Schelling on the 50th anniversary of their landmark book Strategy and Arms Control.