Podcasts about doyne

Doyne Farmer is something of a rebel. Back in the seventies, when he was a student, he walked into a casino in Las Vegas, sat down at a roulette table and beat the house. To anyone watching the wheel spin and the ball clatter to its final resting place, his choice of number would've looked like a lucky guess. But knowing the physics of the game and armed with the world's first wearable computer, which he'd designed, a seemingly random win was actually somewhat predictable.Doyne is an American scientist and entrepreneur who pioneered many of the fields that define the scientific agenda of our time, from chaos theory and complex systems to wearable computing. He uses big data and evermore powerful computers to apply complex systems science to the economy, to better predict our future. Much like roulette, economics can appear random but, with the right tools and understanding, it is anything but.Now Director of the Complexity Economics Programme at the Institute for New Economic Thinking at Oxford, Doyne says there's a real need to act, to use these powers of prediction to help resolve one of the most pressing questions of our time - how best to prevent climate change.Presented by Jim Al-Khalili Produced by Beth Eastwood

In this episode of Greater Perimeter Business Radio, Lee Kantor and Rachel Simon talk with Scott Doyne, a certified career coach and author. Scott shares his journey from a 20-year career at Turner Broadcasting to becoming a career coach. He discusses the importance of coaching and mentorship, especially for mid-career professionals feeling stuck. Scott highlights […]

Farmer is the Baillie Gifford Professor of Complex Systems at Oxford's Institute for New Economic Thinking. Before joining Oxford in 2012, he worked at Los Alamos National Laboratory and the Santa Fe Institute, where he studied complex systems and economic dynamics. During the 1990s, he took a break from academia to run a successful quantitative trading firm using statistical arbitrage strategies.Farmer has been a pioneer in chaos theory and complexity economics, including the development of agent-based models to understand economic phenomena. His work spans from housing markets to climate change, and he recently authored Making Sense of Chaos exploring complexity science and economic modeling.In This Episode* What is complexity economics? (1:23)* Compliment or replacement for traditional economics (6:55)* Modeling Covid-19 (11:12)* The state of the science (15:06)* How to approach economic growth (20:44)Below is a lightly edited transcript of our conversation. What is complexity economics? (1:23)We really can model the economy as something dynamic that can have its own business cycles that come from within the economy, rather than having the economy just settle down to doing something static unless it's hit by shocks all the time, as is the case in mainstream models.Pethokoukis: What does the sort of economics that people would learn, let's say, in the first year of college, they might learn about labor and capital, supply-demand equilibrium, rational expectations, maybe the importance of ideas. How does that differ from the kind of economics you are talking about? Are you looking at different factors?Farmer: We're really looking at a completely different way of doing economics. Rather than maximizing utility, which is really the central conceptual piece of any standard economic model, and writing down equations, and deducing the decision that does that, we simulate the economy.We assume that we identify who the agents in the and economy are, who's making the decisions, what information do they have available, we give them methods of making the decisions — decision-making rules or learning algorithms — and then they make decisions, those decisions have economic impact, that generates new information, other information may enter from the outside, they make decisions, and we just go around and around that loop in a computer simulation that tries to simulate what the economy does and how it works.You've been writing about this for some time. I would guess — perhaps I'm wrong — that just having more data and more computer power has been super helpful over the past 10 years, 20 years.It's been super helpful for us. We take much more advantage of that than the mainstream does. But yes, computers are a billion times more powerful now than they were when Herb Simon first suggested this way of doing things, and that means the time is ripe now because that's not a limiting factor anymore, as it was in the past.So if you're not looking at capital and labor per se, then what are the factors you're looking at?Well, we do look at capital and labor, we just look at them in a different way. Our models are concerned about how much capital is there to invest, what labor is available. We do have to assign firms production functions that tells, given an amount of capital and labor and all their other inputs, how much can the firms produce? That part of the idea is similar. It's a question of the way the decision about how much to produce is made, or the way consumers decide how much to consume, or laborers decide at what price to provide their labor. All those parts are different.Another difference — if I'm understanding it correctly — is, rather than thinking about economies that tend toward equilibrium and focusing how outside shocks may put an economy in disequilibrium, you're looking a lot more at what happens internally. Am I correct?We don't assume equilibrium. Equilibrium, it has two senses in economics: One is supply equals demand. We might or might not run a model where we assume that. In many models we don't, and if that happens, that's great, but it's an outcome of the model rather than an assumption we put in at the beginning.There's another sense of equilibrium, which is that everybody's strategy is lined up. You've had time to think about what you're doing, I've had time to think about what I'm doing, we've both come to the optimal decision for each of us to make, taking the other one into account. We don't assume that, as standard models typically do. We really can model the economy as something dynamic that can have its own business cycles that come from within the economy, rather than having the economy just settle down to doing something static unless it's hit by shocks all the time, as is the case in mainstream models. We still allow shocks to hit our models, but the economy can generate dynamics even without those shocks.This just popped in my head: To whom would this model make more intuitive sense, Karl Marx or Adam Smith?Adam Smith would like these models because they really allow for emergent behavior. That is, Smith's whole point was that the economy is more than the sum of its parts, that we get far more out of specializing than we do out of each acting like Robinson Crusoes. Our way of thinking about this gets at that very directly.Marx might actually like it too, perhaps for a different reason. Marx was insightful in understanding the economy as being like, what I call in the book, the “metabolism of civilization.” That is, he really did recognize the analogy between the economy and the metabolism, and viewed labor as what we put together with natural resources to make goods and services. So those aspects of the economy are also embodied in the kind of models we're making.I think they both like it, but for different reasons.Compliment or replacement for traditional economics (6:55)There are many problems where we can answer questions traditional methods can't even really ask.The way I may have framed my questions so far is that you are suggesting a replacement or alternative. Is what you're suggesting, is it one of those things, or is it a compliment, or is it just a way of looking at the world that's better at answering certain kinds of questions?I think the jury is out to find the answer to that. I think it is certainly a compliment, and that we're doing things very differently, and there are some problems where this method is particularly well-suited. There are many problems where we can answer questions traditional methods can't even really ask.That said, I think time will tell to what extent this replaces the traditional way of doing economics. I don't think it's going to replace everything that's done in traditional economics. I think it could replace 75 percent of it — but let me put an asterisk by that and say 75 percent of theory. Economists do many different things. One thing economists do is called econometrics, where they take data and they build models just based on the data to infer things that the data is telling them. We're not talking about that here. We're talking about theories where economists attempt to derive the decisions and economic outcomes from first principles based on utility maximization. That's what we're talking about providing an alternative to. The extent to which it replaces that will be seen as time will tell.When a big Wall Street bank wants to make a forecast, they're constantly incorporating the latest jobless claims numbers, industrial production numbers, and as those numbers get updated, they change their forecasts. You're not using any of that stuff?Well, no. We can potentially could ingest any kind of data about what's going on.But they're looking at big, top-down data while you're bottom-up, you're sort of trying to duplicate the actual actors in the economy.That is true, but we can adjust what's at the bottom to make sure we're matching initial conditions. So if somebody tells us, “This is the current value of unemployment,” we want to make sure that we're starting our model out, as we go forward, with the right level of unemployment. So we will unemploy some of the households in our model in order to make sure we're matching the state of unemployment right now and then we start our simulation running forward to see where the economy goes from here.I would think that the advent of these large language models would really take this kind of modeling to another level, because already I'm seeing lots of papers on their ability to . . . where people are trying to run experiments and, rather than using real people, they're just trying to use AI people, and the ability to create AI consumers, and AI in businesses — it would have to be a huge advance.Yes. This is starting to be experimented with for what we do. People are trying to use large language models to model how people actually make decisions, or let's say, to simulate the way people make decisions, as opposed to an idealized person that makes perfect decisions. That's a very promising line of attack to doing this kind of modeling.Large language models also can tell us about other things that allow us to match data. For example, if we want to use patents as an input in our modeling — not something we're doing yet, but we've done a lot of studies with patents — one can use large language models to match patents to firms to understand which firms will benefit from the patents and which firms won't. So there are many different ways that large language models are likely to enter going forward, and we're quite keen to take advantage of those.Modeling Covid-19 (11:12)We predicted a 21.5 percent hit to UK GDP in the second quarter of 2020. When the dust settled a year later, the right answer was 22.1. So we got very close.Tell me, briefly, about your work with the Covid outbreak back in 2020 and what your modeling said back then and how well it worked.When the pandemic broke out, we realized right away that this was a great opportunity to show the power of the kind of economic modeling that we do, because Covid was a very strong and very sudden shock. So it drove the economy far out of equilibrium. We were able to predict what Covid would do to the UK economy using two basic ideas: One is, we predicted the shock. We did that based on things like understanding a lot about occupational labor. The Bureau of Labor Statistics compiles tables about things like, in a given occupation, how close together do people typically work? And so we assumed if they worked closer together than two meters, they weren't going to be able to go to their job. That combined with several other things allowed us to predict how big the shock would be.Our model predicted how that shock would be amplified through time by the action of the economy. So in the model we built, we put a representative firm in every sector of the economy and we assumed that if that firm didn't have the labor it needed, or if it didn't have the demand for its product, or if it didn't have the inputs it needed, it wouldn't be able to produce its product and the output would be reduced proportional to any of those three limiting factors.And so we started the model off on Day One with an inventory of inputs that we read out of a table that government statistical agencies had prepared for each sector of the economy. And we then just looked, “Well, does it have the labor? Does it have demand? Does it have the goods?” If yes, it can produce at its normal level. If it's lacking any of those, it's going to produce at a lower level. And our model knew the map of the economy, so it knew which industries are inputs to which other industries. So as the pandemic evolved day by day, we saw that some industries started to run out of inputs and that would reduce their output, which, in turn, could cause other industries to run out of their inputs, and so on.That produced quite a good prediction. We predicted a 21.5 percent hit to UK GDP in the second quarter of 2020. When the dust settled a year later, the right answer was 22.1. So we got very close. We predicted things pretty well, industry by industry. We didn't get them all exactly right, but the mistakes we made averaged out so that we got the overall output right, and we got it right through time.We ran the model on several different scenarios. At the time, this was in April of 2020, the United Kingdom was in a lockdown and they were trying to decide what to do next, and we tested several different scenarios for what they might do when they emerged from the full lockdown. The one that we thought was the least bad was keeping all the upstream industries like mining, and forestry, and so on open, but closing the downstream, customer-facing industries like retail businesses that have customers coming into their shop, or making them operate remotely. That was the one they picked. Already when they picked it, we predicted what would happen, and things unfolded roughly as we suggested they would.The state of the science (15:06)Mainstream models can only model shocks that come from outside the economy and how the economy responds to those shocks. But if you just let the model sit there and nothing changes, it will just settle down and the economy will never change.I'm old enough to remember the 1990s and remember a lot of talk about chaos and complexity, some of which even made it into the mainstream, and Jurassic Park, which may be the way most people heard a little bit about it. It's been 30 years. To what extent has it made inroads into economic modeling at central banks or Wall Street banks? Where's the state of the science? Though it sounds like you're really taking another step forward here with the book and some of your latest research.Maybe I could first begin just by saying that before Jurassic Park was made, I got a phone call and picked up the phone, and the other end of the line said, “Hi, this is Jeff Goldblum, have you ever heard of me?” I said, “Yeah.” And he said, “Well, we're making this movie about dinosaurs and stuff, and I'm going to play a chaos scientist, and I'm calling up some chaos scientists to see how they talk.” And so I talked to Jeff Goldblum for about a half an hour. A few of my other friends did too. So anyway, I like to think I had a tiny little bit of impact on the way he behaved in the movie. There were some parallels that it seemed like he had lifted.Chaos, it's an important underlying concept in explaining why the weather is hard to predict, it can explain some forms of heart arrhythmias, we use it to explain some of the irregular behavior of ice ages. In economics, it was tossed around in the '90s as something that might be important and rejected. As I described in the book, I think it was rejected for the wrong reasons.I'm proposing chaos, the role it plays in here is that, there's a debate about business cycles. Do they come from outside? The Covid pandemic was clearly a business cycle that came from outside. Or do they come from inside the economy? The 2008 financial crisis, I would say, is clearly one that came from inside the economy. Mainstream models can only model shocks that come from outside the economy and how the economy responds to those shocks. But if you just let the model sit there and nothing changes, it will just settle down and the economy will never change.In contrast, the kinds of models we build often show what we call endogenous business cycles, meaning business cycles that the model generates all on its own. Now then, you can ask, “Well, how could it do that?” Well, basically the only plausible way it can do that is through chaos. Because chaos has two properties: One is called sensitive dependence on initial conditions, meaning tiny changes in the present can cause large changes in the future; but the other is endogenous motion, meaning motion that comes from within the system itself, that happens spontaneously, even in very simple systems of equations.Would something like consumer pessimism, would that be an external shock or would something more internal where everybody, they're worried about the futures, then they stop spending as much money? How would that fit in?If the consumer pessimism is due to the fear of a nuclear war, I would say it's outside the economy, and so that's an external shock. But if it's caused by the fact that the economy just took a big nose dive for an internal reason, then it's part of the endogenous dynamicsI spent many years as a journalist writing about why the market's going up, the market's going down, and by the end of the day, I had to come up with a reason why the market moved, and I could — I wasn't always quite confident, because sometimes it wasn't because of a new piece of data, or an earnings report, they just kind of moved, and I had no real reason why, even though I had to come up . . . and of course it was when I was doing that was when people started talking about chaos, and it made a lot of intuitive sense to me that things seem to happen internally in ways that, at least at the time, were utterly unpredictable.Yeah, and in fact, one of the studies I discuss in the book is by Cutler, Poterba, and Summers — the Summers would be Larry Summers — where they did something very simple, they just got the 100 largest moves of the S&P index, they looked up what the news was the next day about why they occurred in the New York Times, and they subjectively marked the ones that they thought were internally driven, versus the ones that were real news, and they concluded they could only find news causes for about a third of them.There is always an explanation in the paper; actually, there is one day on the top 12 list where the New York Times simply said, “There appears to be no cause.” That was back in the '40s, I don't think journalists ever say that anymore. I don't think their paper allows them to do it, but that's probably the right answer about two-thirds of the time, unless you count things like “investors are worried,” and, as I point out in the book, if the person who invests your money isn't worried all the time, then you should fire them because investors should worry.There are internal dynamics to markets, I actually show some examples in the book of simple models that generate that kind of internal dynamics so that things change spontaneously.How to approach economic growth (20:44)I'm not saying something controversial when I say that technological change is the dominant driver of economic growth, at least for the economy as a whole. You recently founded a company, Macrocosm, trying to put some of these ideas to work to address climate change, which would seem to be a very natural use for this kind of thinking. What do you hope to achieve there?We hope to provide better guidance through the transition. We're trying to take the kind of things we've been doing as academics, but scale them up and reduce them to practice so they can be used day-in and day-out to make the decisions that policymakers and businesspeople need to make as the transition is unfolding. We hope to be able to guide policymakers about how effective their policies will be in reducing emissions, but also in keeping the economy going and in good shape. We hope to be able to advise businesses and investors about what investments to make to make a profit while we reduce emissions. And we think that things have changed so that climate change has really become an opportunity rather than a liability.I write a lot about economic growth and try to figure out how it works, what are the key factors. . . What insights can you give me, either on how you think about growth and, since I work at a think tank, the kind of policies you think policy makers should be thinking about, or how should they think about economic growth, since that seems to be on top-of-mind in every rich country in the world right now?I'm not saying something controversial when I say that technological change is the dominant driver of economic growth, at least for the economy as a whole. And we've spent a lot of time studying technological change by just collecting data and looking for the patterns in that data: What does the technology cost through time and how rapidly is it deployed? We've done this for 50 or 60 technologies where we look at past technological transitions, because typically, as a technology is coming in, it's replacing something else that's going out, and what we've seen are a couple of striking things:One is, many technologies don't really improve very much over time, at least in terms of cost. Fossil fuels cost about the same as they did 140 years ago once you adjust for inflation. In fact, anything we mine out of the ground costs about the same as it did a hundred years ago.In contrast, solar energy from solar photovoltaic panels costs 1/10,000th what it did when it was introduced in the Vanguard satellite in 1958. Transistors have been going down at 40 percent per year, so they cost about a billionth of what they did back in 1960. So some technologies really make rapid progress, and the economy evolves by reorganizing itself around the technologies that are making progress. So for example, photography used to be about chemistry and film. Photography now is about solid-state physics because it just unhitched from one wagon and hitched itself to another wagon, and that's what's happening through the energy transition. We're in the process of hitching our wagon to the technologies that have been making rapid progress, like solar energy, and wind energy, and lithium ion batteries, and hydrogen catalyzers based on green energy.I think we can learn a lot about the past, and I think that when we look at what the ride should be like, based on what we understand, we think the transition is going to happen faster than most people think, and we think it will be a net saving of moneySo then how do you deal with a wild card, which I think if you look at the past, nuclear power seems like it's super expensive, no progress being made, but, theoretically, there could be — at least in the United States — there could be lots of regulatory changes that make it easier to build. You have all these venture capital firms pouring money into these nuclear startups with small reactors, or even nuclear fusion. So a technology that seems like it's a mature technology, it might be easy to chart its future, all of a sudden maybe it's very different.I'm not arguing we should get rid of nuclear reactors until they run their normal lifetime and need to be gotten rid of, but I think we will see that that is not going to be the winning technology in the long run, just because it's going to remain expensive while solar energy is going to become dirt cheap.In the early days, nuclear power had faced a very favorable regulatory environment. The first nuclear reactors were built in the '50s. Until Three Mile Island and Chernobyl happened, it was a very regulatorily friendly environment and they didn't come down in cost. Other countries like France have been very pro-nuclear. They have very expensive electricity and will continue to do so.I think the key thing we need to do is focus on storage technologies like green hydrogen. Long-term storage batteries have already come down to a point where they're beginning to be competitive; they will continue to do so. And in the future, I think we'll get solid-state storage that will make things quite cheap and efficient, but I don't think small modular reactors are going to ever be able to catch up with solar and wind at this point.On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were PromisedMicro Reads▶ Economics* United States Economic Forecast - Deloitte* The Hidden Threat to National Security Is Not Enough Workers - WSJ▶ Business* DOGE Can't Do It All. 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We're excited to welcome Doyne Farmer to the podcast. Doyne is a pioneering complexity scientist and a leading thinker on economic systems, technological change, and the future of society. Doyne is a Professor of Complex Systems at the University of Oxford, an external professor at the Santa Fe Institute, and Chief Scientist at Macrocosm. Doyne's work spans an extraordinary range of topics, from agent-based modeling of financial markets to exploring how innovation shapes the long-term trajectory of human progress. At the heart of Doyne's thinking is a focus on prediction—not in the narrow sense of forecasting next week's market trends, but in understanding the deep, generative forces that shape the evolution of technology and society. His new book, Making Sense of Chaos: A Better Economics for a Better World, is a reflection on the limitations of traditional economics and a call to embrace the tools of complexity science. In it, Doyne argues that today's economic models often fall short because they assume simplicity where there is none. What's especially compelling about Doyne's perspective is how he uses complexity science to challenge conventional economic assumptions. While traditional economics often treats markets as rational and efficient, Doyne reveals the messy, adaptive, and unpredictable nature of real-world economies. His ideas offer a powerful framework for rethinking how we approach systemic risk, innovation policy, and the role of AI-driven technologies in shaping our future. We believe Doyne's ideas are essential for anyone trying to understand the uncertainties we face today. He doesn't just highlight the complexity—he shows how to navigate it. By tracking the hidden currents that drive change, he helps us see the bigger picture of where we might be headed. We hope you enjoy our conversation with Doyne Farmer. ------------------------------ If you enjoy our podcasts, please subscribe and leave a positive rating or comment. Sharing your positive feedback helps us reach more people and connect them with the world's great minds. Subscribe to get Artificiality delivered to your email Learn about our book Make Better Decisions and buy it on Amazon Thanks to Jonathan Coulton for our music

******Support the channel****** Patreon: https://www.patreon.com/thedissenter PayPal: paypal.me/thedissenter PayPal Subscription 3 Dollars: https://tinyurl.com/ybn6bg9l PayPal Subscription 5 Dollars: https://tinyurl.com/ycmr9gpz PayPal Subscription 10 Dollars: https://tinyurl.com/y9r3fc9m PayPal Subscription 20 Dollars: https://tinyurl.com/y95uvkao   ******Follow me on****** Website: https://www.thedissenter.net/ The Dissenter Goodreads list: https://shorturl.at/7BMoB Facebook: https://www.facebook.com/thedissenteryt/ Twitter: https://x.com/TheDissenterYT   This show is sponsored by Enlites, Learning & Development done differently. Check the website here: http://enlites.com/   Dr. J. Doyne Farmer is the Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, Baillie Gifford Professor in the Mathematical Institute at the University of Oxford and an External Professor at the Santa Fe Institute. His current research is in economics, including agent-based modeling, financial instability and technological progress. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology. He was an Oppenheimer Fellow and the founder of the Complex Systems Group at Los Alamos National Laboratory. He is the author of Making Sense of Chaos: A Better Economics for a Better World.   In this episode, we focus on Making Sense of Chaos. We talk about the economy as a complex system, business cycles, simulating the economy, and the housing bubble crises of the 2000s. We discuss the differences between standard economics and complexity economics. We talk about how we can understand inequality, market inefficiencies and crashes, and whether we can prevent financial crises. Finally, we discuss climate economics, how we can solve climate change, and whether we can tackle inequality. -- A HUGE THANK YOU TO MY PATRONS/SUPPORTERS: PER HELGE LARSEN, JERRY MULLER, BERNARDO SEIXAS, ADAM KESSEL, MATTHEW WHITINGBIRD, ARNAUD WOLFF, TIM HOLLOSY, HENRIK AHLENIUS, FILIP FORS CONNOLLY, DAN DEMETRIOU, ROBERT WINDHAGER, RUI INACIO, ZOOP, MARCO NEVES, COLIN HOLBROOK, PHIL KAVANAGH, SAMUEL ANDREEFF, FRANCIS FORDE, TIAGO NUNES, FERGAL CUSSEN, HAL HERZOG, NUNO MACHADO, JONATHAN LEIBRANT, JOÃO LINHARES, STANTON T, SAMUEL CORREA, ERIK HAINES, MARK SMITH, JOÃO EIRA, TOM HUMMEL, SARDUS FRANCE, DAVID SLOAN WILSON, YACILA DEZA-ARAUJO, ROMAIN ROCH, DIEGO LONDOÑO CORREA, YANICK PUNTER, CHARLOTTE BLEASE, NICOLE BARBARO, ADAM HUNT, PAWEL OSTASZEWSKI, NELLEKE BAK, GUY MADISON, GARY G HELLMANN, SAIMA AFZAL, ADRIAN JAEGGI, PAULO TOLENTINO, JOÃO BARBOSA, JULIAN PRICE, EDWARD HALL, HEDIN BRØNNER, DOUGLAS FRY, FRANCA BORTOLOTTI, GABRIEL PONS CORTÈS, URSULA LITZCKE, SCOTT, ZACHARY FISH, TIM DUFFY, SUNNY SMITH, JON WISMAN, WILLIAM BUCKNER, PAUL-GEORGE ARNAUD, LUKE GLOWACKI, GEORGIOS THEOPHANOUS, CHRIS WILLIAMSON, PETER WOLOSZYN, DAVID WILLIAMS, DIOGO COSTA, ALEX CHAU, AMAURI MARTÍNEZ, CORALIE CHEVALLIER, BANGALORE ATHEISTS, LARRY D. LEE JR., OLD HERRINGBONE, MICHAEL BAILEY, DAN SPERBER, ROBERT GRESSIS, IGOR N, JEFF MCMAHAN, JAKE ZUEHL, BARNABAS RADICS, MARK CAMPBELL, TOMAS DAUBNER, LUKE NISSEN, KIMBERLY JOHNSON, JESSICA NOWICKI, LINDA BRANDIN, NIKLAS CARLSSON, GEORGE CHORIATIS, VALENTIN STEINMANN, PER KRAULIS, ALEXANDER HUBBARD, BR, MASOUD ALIMOHAMMADI, JONAS HERTNER, URSULA GOODENOUGH, DAVID PINSOF, SEAN NELSON, MIKE LAVIGNE, JOS KNECHT, ERIK ENGMAN, LUCY, MANVIR SINGH, PETRA WEIMANN, CAROLA FEEST, STARRY, MAURO JÚNIOR, 航 豊川, TONY BARRETT, BENJAMIN GELBART, NIKOLAI VISHNEVSKY, AND STEVEN GANGESTAD! A SPECIAL THANKS TO MY PRODUCERS, YZAR WEHBE, JIM FRANK, ŁUKASZ STAFINIAK, TOM VANEGDOM, BERNARD HUGUENEY, CURTIS DIXON, BENEDIKT MUELLER, THOMAS TRUMBLE, KATHRINE AND PATRICK TOBIN, JONCARLO MONTENEGRO, AL NICK ORTIZ, NICK GOLDEN, AND CHRISTINE GLASS! AND TO MY EXECUTIVE PRODUCERS, MATTHEW LAVENDER, SERGIU CODREANU, BOGDAN KANIVETS, ROSEY, AND GREGORY HASTINGS!

Navigating Midlife Career Transitions, with Scott Doyne, Author of Exploring the Midlife Career Crisis (North Fulton Business Radio, Episode 826) In this episode of North Fulton Business Radio, host John Ray discusses career transitions with Scott Doyne, a certified career coach and author of Exploring the Midlife Career Crisis: A Guide to Navigating the Four Stages […] The post Navigating Midlife Career Transitions, with Scott Doyne, Author of

Navigating Midlife Career Transitions, with Scott Doyne, Author of Exploring the Midlife Career Crisis (North Fulton Business Radio, Episode 826) In this episode of North Fulton Business Radio, host John Ray discusses career transitions with Scott Doyne, a certified career coach and author of Exploring the Midlife Career Crisis: A Guide to Navigating the Four Stages […]

What if we could predict the economy the way we predict the weather? What if governments could run simulations to forecast the effects of new policies—before they happen? And what if the key to all of this lies in the same chaotic systems that explain spinning roulette wheels and rolling dice?J. Doyne Farmer is a University of Oxford professor, complexity scientist, and former physicist who once beat Las Vegas casinos using his scientific-based methods. In his recent book “Making Sense of Chaos: A Better Economics for a Better World” Farmer is using those same principles to build a new branch of economics called complexity economics—one that uses big data to help forecast market crashes, design better policies and find ways to confront climate change.But can we really predict the unpredictable? And how will using chaos theory shake up well-established economic approaches?

A large economy is one of the best examples we have of complex dynamics. There are multiple components arranged in complicated overlapping hierarchies, out-of-equilibrium dynamics, nonlinear coupling and feedback between different levels, and ubiquitous unpredictable and chaotic behavior. Nevertheless, many economic models are based on relatively simple equilibrium principles. Doyne Farmer is among a group who think that economists need to start taking the tools of complexity theory seriously, as he argues in his recent book Making Sense of Chaos: A Better Economics for a Better World.Support Mindscape on Patreon.Blog post with transcript: https://www.preposterousuniverse.com/podcast/2024/10/21/293-doyne-farmer-on-chaos-crashes-and-economic-complexity/J. Doyne Farmer received his Ph.D. in physics from the University of California, Santa Cruz. He is currently Director of the Complexity Economics program and Baillie Gifford Professor of Complex Systems Science at the University of Oxford, External Professor at the Santa Fe Institute, and Chief Scientist at Macrocosm. He was the founder of the Complex Systems Group in the Theoretical Division at Los Alamos National Laboratory, and co-founder of The Prediction Company.Web siteOxford web pageGoogle Scholar publicationsAmazon author pageWikipediaSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Welcome to The Orthogonal Bet, an ongoing mini-series that explores the unconventional ideas and delightful patterns that shape our world. Hosted by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Samuel Arbesman⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. In this episode, Sam speaks with J. Doyne Farmer, a physicist, complexity scientist, and economist. Doyne is currently the Director of the Complexity Economics program at the Institute for New Economic Thinking at the Oxford Martin School and the Baillie Gifford Professor of Complex Systems Science at the Smith School of Enterprise and the Environment at the University of Oxford. Doyne is also the author of the fascinating new book “Making Sense of Chaos: A Better Economics for a Better World.” Sam wanted to explore Doyne's intriguing history in complexity science, his new book, and the broader field of complexity economics. Together, they discuss the nature of simulation, complex systems, the world of finance and prediction, and even the differences between biological complexity and economic complexity. They also touch on Doyne's experience building a small wearable computer in the 1970s that fit inside a shoe and was designed to beat the game of roulette. Produced by ⁠⁠CRG Consulting⁠⁠ Music by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠George Ko⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ & Suno

Are you feeling the career itch? Whether you're ready to make a change, up for a new challenge within your current organization or if you're simply curious as to the available options, today's guest is here to help us navigate the four stages of a career transition. Author and certified career coach, Scott Doyne, joins host Brandon Smith to help us answer: "What do you want to try next?" 

J. Doyne Farmer is director of the Complexity Economics program at the Institute for New Economic Thinking at the Oxford Martin School, Baillie Gifford Professor in the Mathematical Institute at the University of Oxford, and an external professor at the Santa Fe Institute. He is a renowned physicist and complex systems scientist with a career spanning more than four decades. As a pioneer in chaos theory and complexity science, he has made significant contributions to understanding dynamic systems and their applications in various fields, including economics and financial markets.In this conversation with Dave Young, the Global Leader of the BCG Henderson Institute's Center for Climate & Sustainability, Doyne discusses his journey from astrophysics to pioneering work in chaos theory and complex systems. He explains how modern computational power and big data are revolutionizing economic modeling, sharing insights from his team's accurate prediction of COVID-19's economic impact in the U.K. Farmer argues for a shift from traditional macroeconomic models to more dynamic, data-driven approaches that can capture the intricacies of our complex economic systems.This podcast uses the following third-party services for analysis: Chartable - https://chartable.com/privacy

J. Doyne Farmer is Director of the Complexity Economics Programme and Professor of Complex Systems Science at the University of Oxford. He is also External Professor at the Santa Fe Institute and Chief Scientist at Macrocosm. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to UBS in 2006. His book, Making Sense of Chaos: A Better Economics for a Better World, was published in 2024. During the 1980s he was an Oppenheimer Fellow and the founder of the Complex Systems Group at Los Alamos National Laboratory. While a graduate student in the 1970s, he built the first wearable digital computer, which was successfully used to predict the game of roulette. This podcast covers what chaos theory is, what complexity science is, how economists model the economy, and much more.    Follow us here for more amazing insights: https://macrohive.com/home-prime/ https://twitter.com/Macro_Hive https://www.linkedin.com/company/macro-hive

Physicist J. Doyne Farmer wants a new kind of economics that takes account of what we've learned from chaos theory and that builds more accurate models of how humans actually behave. Listen as he makes the case for complexity economics with EconTalk's Russ Roberts. Farmer argues that complexity economics makes better predictions than standard economic theory and does a better job dealing with the biggest problems in today's society.

In Episode 374 of Hidden Forces, Demetri Kofinas speaks with J. Doyne Farmer. Dr. Farmer is a complex systems scientist and entrepreneur who pioneered many of the theories and applications that we discuss today, including chaos theory, complexity science, artificial life, and wearable computing. We live in an age of increasing complexity, accelerating technological change, and global connectivity that holds more promise and peril than arguably any time in human history. Successfully navigating these changes will depend immeasurably on the quality of our economic models because, at their heart, all these changes—the changes associated with trends in automation, digitization, demographics, and financial markets—are rooted in the economy and the network of systems that keep us alive. For the first time, using big data and ever-more-powerful computers, we are now able to apply complexity science to economic activity, building realistic models of the global economy and financial markets that promise to vastly outperform in terms of verisimilitude and predictive power anything that we have seen in human history. This episode is divided into two parts. The first hour is meant to provide you with a foundational understanding of complexity science and its application to economics. We discuss chaos and volatility and compare the explanatory and predictive power of agent-based simulations to the standard economic model. The second hour is an exploration of economic frameworks that treat the economy as an ecological network and series of metabolic processes. We also apply the lessons of the first hour to specific economic and financial questions related to investment styles, risk management, technological disruption, and policymaking. You can subscribe to our premium content and access our premium feed, episode transcripts, and Intelligence Reports at HiddenForces.io/subscribe. If you want to join in on the conversation and become a member of the Hidden Forces Genius community, which includes Q&A calls with guests, access to special research and analysis, in-person events, and dinners, you can also do that on our subscriber page at HiddenForces.io/subscribe. If you enjoyed listening to today's episode of Hidden Forces, you can help support the show by doing the following: Subscribe on Apple Podcasts | YouTube | Spotify | Stitcher | SoundCloud | CastBox | RSS Feed Write us a review on Apple Podcasts & Spotify Subscribe to our mailing list at https://hiddenforces.io/newsletter/ Producer & Host: Demetri Kofinas Editor & Engineer: Stylianos Nicolaou Subscribe and Support the Podcast at https://hiddenforces.io Join the conversation on Facebook, Instagram, and Twitter at @hiddenforcespod Follow Demetri on Twitter at @Kofinas Episode Recorded on 08/02/2024

In the 1970's, J. Doyne Farmer built the first wearable computer which he used to predict the game of roulette. While this didn't make him particularly popular in casinos, it did mark the beginning of a glittering scientific career in complexity and systems theory, as well as in theoretical physics and biology. And, along the way, Farmer founded a quantitative automated trading firm that was sold to UBS in 2006 as well as working for a while as an Oppenheimer Fellow at Los Alamos Labs. So when a guy as smart as Farmer - who now teaches both at Oxford and at the Santa Fe Institute — turns his big brain to economics, we should take note. In his new book, Making Sense of Chaos, Farmer explains how we can get to a “better economics for a better world” through what he calls complex economics. As a fusion of big data analysis and behavioral economics, Farmer is navigating a third economic way between the scylla of traditional free market economics and the charybdis of de-growth economics. Seriously smart stuff from one the world's brainiest men. J. Doyne Farmer is Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, Baillie Gifford Professor of Complex Systems Science at the Smith School for Enterprise and the Environment, University of Oxford, External Professor at the Santa Fe Institute, and Chief Scientist at Macrocosm. His current research is in economics, including agent-based modeling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to UBS in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology. During the 1980s he was an Oppenheimer Fellow and the founder of the Complex Systems Group at Los Alamos National Laboratory. While a graduate student in the 1970s he built the first wearable digital computer, which was successfully used to predict the game of roulette.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.Keen On is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

Send us your feedbackIn this episode of the My Macular and Me podcast we are joined by Professor Jacqueline van der Spuy from the UCL Institute of Ophthalmology. She discusses the development of treatments for Doyne honeycomb retinal dystrophy, an inherited juvenile macular dystrophy affecting sight from early adulthood.The Macular Society has been supporting people with macular conditions for over 30 years. The right information and support can help people overcome their worries and retain their independence. We provide free information and support to those with macular disease, along with their family and friends. If you or a family member need advice or support, please make sure to reach out. No one has to face macular disease alone. Please call us on 0300 3030 111.

This week, Nick and Goldy talk to Doyne Farmer, a renowned physicist and mathematician, to discuss his new book, "Making Sense of Chaos: A Better Economics for a Better World." Farmer, who is a professor at the Institute for New Economic Thinking, challenges traditional orthodox economic frameworks by applying complex systems theory. Their conversation explores the limitations of mainstream economic models, the importance of incorporating uncertainty into economic thinking, and the potential for complexity economics to provide better guidance for policymakers in addressing pressing issues like climate change and inequality. It's a thoughtful discussion that explores more effective approaches to understanding and managing complex economic systems. Doyne Farmer is a renowned physicist and mathematician who is currently a Professor at the Institute for New Economic Thinking at the University of Oxford and the Director of the Complexity Economics program. He is also an author known for his groundbreaking work in the field of complex systems and chaos theory. His recent book, "Making Sense of Chaos: A Better Economics for a Better World," delves into how chaos theory can be applied to understand and address the complexities of modern economic systems. Twitter: @doyne_farmer Further reading:  Making Sense of Chaos: A Better Economics for a Better World Website: http://pitchforkeconomics.com Twitter: @PitchforkEcon Instagram: @pitchforkeconomics Nick's twitter: @NickHanauer

In today's episode, Nathan is joined by J. Doyne Farmer, the director of the Complexity Economics Program at the Institute for New Economic Thinking at the Oxford Martin School, and author of the new book "Making sense of Chaos: A Better Economics for Better World" to discuss the importance of understanding and explaining economic models, particularly agent-based models. J. Doyne Farmer: Book: "Making Sense of Chaos: A Better Economics for a Better World" More S&P Global Content: The Daily Update Look Forward Credits: Host/Author: Nathan Hunt Producer/Editor: Patrick Moroney Published With Assistance From: Kyle May, Kurt Burger, Camille McManus www.spglobal.com

Accelerating technology and global interconnections hold more promise – and more peril – than any other time in human history. How can we shape an economy to better address the complex problems facing the world?Many books have been written about Doyne Farmer and his work. Making Sense of Chaos is his personal manifesto for doing economics better.As a complex systems scientist and entrepreneur, Doyne has pioneered many of the fields that define the scientific agenda of our times: chaos, complexity, artificial life, wearable computing, and more.A former Oppenheimer Fellow and founder of the Complex Systems Group at Los Alamos National Laboratory, Doyne built the first wearable digital computer while still a graduate student, and successfully used it to predict the game of roulette.#RSAeconomyBecome an RSA Events sponsor: https://utm.guru/udI9xDonate to The RSA: https://utm.guru/udNNBFollow RSA Events on Instagram: https://instagram.com/rsa_events/Follow the RSA on Twitter: https://twitter.com/RSAEventsLike RSA Events on Facebook: https://www.facebook.com/rsaeventsofficialListen to RSA Events podcasts: https://bit.ly/35EyQYUJoin our Fellowship: https://www.thersa.org/fellowship/join

J. Doyne Farmer is the Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, a Professor at the Mathematical Institute of Oxford University, and an External Professor at the Santa Fe Institute. In this episode, we explore Doyne's latest book, “Making Sense of Chaos.” We focus on the relationship between chaos and scaling theory, and more specifically, how chaos can be factored into scaling theory. By the end of this conversation, you will learn why it might be easier to predict the long distant future than predicting tomorrow, how Moore's Law conflicts with other scaling laws that underpin technological progress, how agent-based modeling can help all scientists and policymakers, how to dominate the world with your theories (...), and even how to trick casinos. I hope you enjoy the conversation. Find me on X at @⁠⁠ProfSchrepel⁠⁠. Also, be sure to subscribe to the Scaling Theory podcast; it helps its growth. ***

J. Doyne Farmer is Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, Baillie Gifford Professor in the Mathematical Institute at the University of Oxford and an External Professor at the Santa Fe Institute.In this episode, Doyne discusses his journey from chaos theory to complexity economics. He shares his experience developing agent-based models for the economy and talks about the importance of multidisciplinary work and applying complexity science principles to economics and climate change.   Resources: Purchase ‘Making Sense of Chaos: A Better Economics for a Better World' here   Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.

In Making Sense of Chaos: A better economics for a better world, J. Doyne Farmer challenges traditional economic models, which rely on simplistic assumptions and fail to provide accurate predictions.Farmer, a complex systems scientist at the University of Oxford and the Santa Fe Institute, argues that with technological advances in data science and computing, we are now able to apply complex systems thinking to build models that more accurately capture reality and enable us to make better predictions about the economy.Together with Martin Reeves, Chairman of the BCG Henderson Institute, Farmer discusses the limitations of standard models of economics as well as the consequences of such limitations. He proposes an alternative based on complex systems thinking and agent-based modeling—and describes how it can be applied in various fields, including business.Key topics discussed: 01:42 | Limitations of the standard model of economics04:44 | How complex systems thinking works09:01 | Consequences of using inadequate economic models12:44 | Agent-based modeling as a powerful alternative19:02 | Leveraging alternative modeling techniques in business24:59 | How CEOs can start embracing complexity thinkingThis podcast uses the following third-party services for analysis: Chartable - https://chartable.com/privacy

Markham interviews Dr. Doyne Farmer is the Baillie Gifford Professor of Complex Systems Science at the Smith School of Enterprise and the Environment, at Oxford University. He is the author of “Making Sense of chaos: A Better Economics for a Better World” that will be published by Allen Lane on April 25. 

Doyne Farmer is Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, Baillie Gifford Professor in the Mathematical Institute at the University of Oxford, and an External Professor at the Santa Fe Institute. His current research is in economics, including agent-based modeling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to UBS in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology.

Christina Gambini, Hyatt's regional vice president for Latin America and the Caribbean, and Erica Doyne, senior vice president of marketing and communications for Hyatt's Inclusive Collection, talk with Alan Fine of Insider Travel Report about the acquisition of Apple Leisure Group and how it is helping Hyatt support the luxury leisure all-inclusive customer and reinvent itself in the business and group travel space. Doyne explains how the all-inclusive resort is evolving to include unlimited luxury with endless privileges and what that actually means in terms of amenities, perks and a robust loyalty program. Gambini and Doyne also introduce their newest resorts in Mexico. For more information, visit www.HyattPrive.com and www.hyatt.com. If interested, the original video of this podcast can be found on the Insider Travel Report Youtube channel or by searching for the podcast's title on Youtube.

Professor Doyne Farmer is the Baillie Gifford Professor in Mathematics at Oxford, the Director of the Complexity Economics programme at INET, and an External Professor at the Santa Fe Institute. In our conversation we discuss: How Doyne and his friends used physics and hidden computers to beat the roulette wheel in Las Vegas casinos Advancing economic models to better predict business cycles and knock-on effects from extreme events like Covid-19 Techniques for predicting technological progress and long-run growth, with specific applications to energy technologies and climate change You can read more about the topics we cover in this episode's write-up: hearthisidea.com/episodes/farmer If you have any feedback or suggestions for future guests, feel free to get in touch through our website. Consider leaving us a review wherever you're listening to this — it's the best free way to support the show. If you want to support the show more directly, consider leaving a tip. Thanks for listening!

The world today is as complex as ever. With some systems crumbling and others doubling down, navigating meaningful work can be exhausting and can easily lead to burnout. This is just one aspect of my conversation with Maggie Doyne that I loved diving into. I have long admired people (especially women) who do things differently than the status quo. Who have a desire, a passion and a fuel to do something big- and actually follow that calling. It's SO hard friends. SO freaking hard. This is why I was so fueled and inspired by my conversation with my friend Maggie Doyne, Co-Founder and CEO of the BlinkNow Foundation. In this episode we talked about: What it looks like to navigate privilege, radically listen and work through the "white savior compex" How to have scrappy, messy hope in the midst of todays cultural climate Ins and outs of motherhood in all of its shapes and sizes The power of simplicity and getting our basic needs met I loved having Maggie Doyne on the podcast to discuss her highly anticipated debut memoir Between the Mountain and the Sky: A Mother's Story of Hope and Love. As the CEO and co-founder of BlinkNow, a nonprofit dedicated to serving the children of Nepal, Doyne has been globally recognized for her community-based and sustainable initiatives abroad. The book highlights Doyne's inspirational coming of age story spanning over a decade since she journeyed from her comfortable middle-class New Jersey home for the college gap year that forever changed the trajectory of her life. When Doyne first arrived in Surkhet, Nepal the area was just coming out of a civil war that left families in poverty and many children orphaned on the streets. After an unexpected encounter with a Nepali girl breaking rocks in a quarry, Doyne decided to invest her life savings of five thousand dollars to buy a piece of land and open the Kopika Valley Children's Home with a vision to care for, comfort, educate, and empower the children of the community. Alongside BlinkNow co-founder Tope Malla, Doyne has gone on to open a school, health clinic, women's center, and more, and was recognized for her remarkable initiatives as a 2015 CNN Hero of the Year. Find out more about the BlinkNow Foundation here.Follow, subscribe and leave us a review! Find out more about Yellow Co.'s community of women creaeting meaningful work: yellowco.co | @yellowco.co • Connect with Joanna at joannawaterfall.com and on IG @joannawaterfall :) Music Written by Jonny Pickett (check out his music on spotify) Thanks for listening!

As our world knits together, economic interdependencies change in both shape and nature. Supply chains, finance, labor, technological innovation, and geography interact in puzzling nonlinear ways. Can we step back far enough and see clearly enough to make sense of these interactions? Can we map the landscape of capability across scales? And what insights emerge by layering networks of people, firms, states, markets, regions? We're all riding a bucking horse; what questions can we ask to make sure that we can stay in the saddle?Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I'm your host, Michael Garfield, and every other week we'll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.This week on Complexity, we speak with two SFI External Professors helping to rethink political economy: newly-appointed Science Board Co-Chair Ricardo Hausmann (Website, Wikipedia, Twitter) is the Director of the Harvard Growth Lab and J. Doyne Farmer (Website, Wikipedia) is Director of the Complexity Economics program at the Institute for New Economic Thinking at the Oxford Martin School. In this episode we zoom wide to try and find a way to garden all together, learning limits that can help inform discussion and decisions on the shape of things to come…If you value our research and communication efforts, please subscribe, rate and review us at Apple Podcasts, and consider making a donation — or finding other ways to engage with us — at santafe.edu/engage. You can find the complete show notes for every episode, with transcripts and links to cited works, at complexity.simplecast.com. Heads up that our online education platform Complexity Explorer's Origins of Life Course is still open for enrollment until June 1st! We hope to see you in there…Thank you for listening!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 • LinkedInMentions and additional resources:The new paradigm of economic complexityPierre-Alexandre Balland, Tom Broekel, Dario Diodato, Elisa Giuliani, Ricardo Hausmann, Neave O'Clery, and David Rigbyin Research PolicyHow production networks amplify economic growthJames McNerney, Charles Savoie, Francesco Caravelli, Vasco M. Carvalho, and J. Doyne Farmer in PNASProductive Ecosystems and the arrow of developmentby Neave O'Clery, Muhammed Ali Yıldırım, and Ricardo Hausmann Horrible trade-offs in a pandemic: Poverty, fiscal space, policy, and welfareRicardo Hausmann and Ulrich Schetterin ScienceDirectHistorical effects of shocks on inequality: the great leveler revisitedBas van Bavel and Marten Schefferin Nature Humanities & Social Sciences Communications(Twitter thread)Complexity 56 - J. Doyne Farmer on The Complexity Economics RevolutionThe Multiple Paths to Multiple LifeChristopher P. Kempes and David C. Krakauer in Journal of Molecular EvolutionScaling of urban income inequality in the USAElisa Heinrich Mora, Cate Heine, Jacob J. Jackson, Geoffrey B. West, Vicky Chuqiao Yang and Christopher P. Kempesin Journal of The Royal Society InterfaceComplexity 12 - Matthew Jackson on Social & Economic NetworksComplexity 81 - C. Brandon Ogbunu on Epistasis & The Primacy of Context in Complex SystemsPitchfork Economicsby Nick Hanauer (podcast)Complexity 15 - R. Maria del-Rio Chanona on Modeling Labor Markets & Tech UnemploymentWill a Large Complex System be Stable?by Robert Mayin NatureInvestigationsby Stuart KauffmanThe Collapse of Networksby Raissa D'Souza (SFI Symposium Talk)

Until very recently, the prevailing wisdom cautioned that transitioning to a clean energy economy would be extremely expensive, and therefore only possible if undertaken slowly. New research upends that thinking—when it comes to going green, the faster we go, the cheaper it will be. University of Oxford professors Eric Beinhocker and Doyne Farmer talk with Nick about a new strategy for clean technology that could transform the climate fight.  Eric Beinhocker is a Professor of Public Policy Practice at the Blavatnik School of Government and the Executive Director of the Institute for New Economic Thinking at the University of Oxford's Martin School. He is also a Supernumerary Fellow in Economics at Oriel College, and External Professor at the Santa Fe Institute.  Twitter: @EricBeinhocker Doyne Farmer is Director of the Complexity Economics program at the Institute for New Economic Thinking. He is Baillie Gifford Professor in the Mathematical Institute at the University of Oxford and an External Professor at the Santa Fe Institute.  Website: http://www.doynefarmer.com/ Going big and fast on renewables could save trillions in energy costs: https://www.washingtonpost.com/business/energy/going-big-and-fast-on-renewables-would-save-trillions-in-energy-costs/ A new strategy for climate: make the clean stuff cheap - https://democracyjournal.org/arguments/a-new-strategy-for-climate-make-the-clean-stuff-cheap/  Website: http://pitchforkeconomics.com/ Twitter: @PitchforkEcon Instagram: @pitchforkeconomics Nick's twitter: @NickHanauer

Once upon a time at UC Santa Cruz, a group of renegade grad students started mixing physics with math and computers, determined to discover underlying patterns in the seeming-randomness of systems like the weather and roulette. Their research led to major insights in the emerging field of chaos theory, and eventually to the new discipline of complexity economics — which brings models from ecology and physics, cognitive science and biology together to improve our understanding of how value flows through networks, how people make decisions, and how new technologies evolve. As the human world weaves new global economic systems and sustainability looms ever-larger in importance, it is finally time to heed the warnings — and the promises — of this new paradigm of economics.Welcome to Complexity, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.This week on Complexity, we speak with SFI External Professor J. Doyne Farmer at INET Oxford, to tour his fifty years of pioneering work and current book-in-progress, The Complexity Economics Revolution. Topics include how ecology inspires novel forms of macroeconomics; how “bounded rationality” changes the narrative about rational self-interested economic actors; how leverage leads to greater instability; how new tools can help us predict emerging innovations and engineer a better banking system; the skewed incentives of science funding and venture capital; his take on cryptocurrencies; and more…If you value our research and communication efforts, please rate and review us at Apple Podcasts, and/or consider making a donation at santafe.edu/podcastgive. You can find numerous other ways to engage with us at santafe.edu/engage. Thank you for listening!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 • LinkedInKey Links:Doyne Farmer’s Personal Website | SFI Page | INET Oxford Page | Google Scholar PageDoyne Farmer and related talks on our YouTube channelComplexity Economics from SFI PressRelated Complexity Podcast Episodes:W. Brian Arthur on The History & Future of Complexity Economics[Farmer’s PhD student] R. Maria del-Rio Chanona on Modeling Labor MarketsMatthew Jackson on The Science of Social NetworksGeoffrey West on Scaling and Superlinear InnovationDavid Krakauer on Collapse & High-Beta Investment Strategies

New Jersey native Maggie Doyne was age 18 when she arrived in Nepal, 19 when she had co-founded the BlinkNow Foundation nonprofit to support children in the district of Surkhet, and by 25, she had become a mother to 40 children. Doyne’s unlikely story began in 2005, with the decision to take a gap year after high school and travel; she felt it was necessary to press pause on a more expected path and learn about herself and her purpose in the world. Upon her visit to Nepal, Doyne fell in love with the country and the people. But she also found it in the aftermath of a nearly 11-year civil war, with displaced families, schools shut down, and children breaking rocks to sell for money. Doyne gathered her babysitting savings—just five thousand dollars—to buy a piece of land in Surkhet, and started a children’s home there. She still lives in that home now as the mother to 54 children. Today, BlinkNow, which she co-founded with her Nepali friend Top Malla, supports the Kopila Valley School, as well as a children’s home, health clinic, “Big Sister’s” home, and women’s center. The Kopila Valley School’s new campus opened this past February. Not only does the pre-primary through 12th grade program have 20 classrooms to educate more than 400 students, it is one of the greenest schools in the world. For her work, Doyne has received the Unsung Hero of Compassion Award, presented to her by the Dalai Lama in 2014, and was recognized as CNN’s 2015 Hero of the Year. On this episode of Time Sensitive, the 32-year-old Doyne discusses her path from presumably college-bound student to full-time mother of nearly five dozen Nepali children; experiencing heartbreaking loss and meeting the love of her life; and the importance of taking action.

The Connect talk from the morning of 7 March

P.S. This discussion happened on December 3, 2018 before Billboard corrected the sales! Timestamps are down below Timestamps: 0:00 - Intros 5:36 - Lil Pump Shenanigans 8:10 - Chief Keef’s Personal Backwood Roller 9:13 - Is Quality Control Music flooding the streets too much? 19:17 - Tory Lane vs Joyner Lucas 35:18 - 6ix9ine vs Travis Scott for #1 billboard spot 38:24 - Rappers Re-writing verses 51:21 - How far do you take ghostwriting 59:39 - world star bad for hip hop? 1:06:25 - Championships discussion 1:19:30 - Is it okay for a sample owners to bully their way into a big percentage? 1:35:10 - What is your definition of FOR THE CULTURE 1:45:25 - Having an ear for production & how important is a good live performance 1:52:00 - brief discussion of the future of hip hop 1:53:30 - how many bad albums before I give up on an artist? 1:59:50 - outros Keep Up with Andrew ● twitter.com/therealandrew98  Keep Up With Adam ● twitter.com/siskertini  ● www.instagram.com/_up.and.adam_  Dylan Dilworth ● Twitter - twitter.com/Dylan_CBE  ● Instagram: instagram.com/Dylan_CBE  If you're a listener of the show & you know me personally text or call me. If you don't know me personally tag or DM on twitter or instagram @Dylan_CBE . I want to shout y'all out Keep up with Andrew Welcome to Dylan's Dilworth Podcast. Here you'll get the latest news in the industry with my opinion on it aka my two cents. The main focus being gaming and film / TV. There will be occasions where I may touch on other news in other industries like music, sports, etc. If there's any feedback, questions, or topics you want to hear discussed, best way to reach me is via Twitter: @Dylan_CBE

Michelle and Nima talk about Thanksgiving heartbreak.  Stories from: Rebecca Hapin (www.rebeccahalpin.com),   Mel Green (www.melgreenonline.wordpress.com) Libby Doyne (www.libbydoyne.com),  Rebecca Halpin is an actor/director according to Facebook,, which won't let her choose just one. You can catch her as the bitchy best friend in the short film The Big O.   Mel Green has survived disco, military school and a childhood in Odessa Texas. Discovered as a stand-up comedian and improvisational performer at The Comedy Store, The Improv, and The Holy City Zoo in San Francisco, he wrote on such seminal television series as SATURDAY NIGHT LIVE and SCTV. His novel Marker: A true story of misery and misinformation with an Appendix of lies is available on Amazon.  Libby Doyne is a writer and comedian living in Los Angeles. She is the proud recipient of the Thomas Angel Foundation Scholarship to Upright Citizens Brigade and the UCLA Writers’ Extension Program Scholarship for TV Writing. Her work has been published in The Invisible Tattoo, a collection of essays about grief, and LGBTQ Comedic Monologues That Are Actually Funny. She has a pet rabbit named Juniper.

J. DOYNE FARMER is director of the Complexity Economics Programme at the Institute for New Economic Thinking at the Oxford Martin School, professor in the Mathematical Institute at the University of Oxford, and an external professor at the Santa Fe Institute. He was a co-founder of Prediction Company, a quantitative automated trading firm that was sold to the United Bank of Switzerland in 2006. The Conversation: https://www.edge.org/conversation/jdoynefarmer-don_ross-collective-awareness

Eva and Olga welcome Maggie Doyne, mom to over 50 children and now a founder of a top-rated school in Nepal that serves over 350 students from impoverished backgrounds. For our show notes, visit DreamBigPodcast.com/63

We made an audiobook! Steve narrated the audiobook version of Thomas A. Bass's The Eudaemonic Pie. The book tells the story of a band of physicists and computer wizards in the late 1970s and early 1980s who set out to beat roulette in Las Vegas with computers in their shoes. A story of science, art, and the birth of the hacker culture. The episode contains a brief account of the making of the audiobook and the entire prologue of the audiobook itself.

Maggie Doyne, founder of BlinkNow in Nepal and a 2015 CNN Hero, was 19 years old when she witnessed the huge numbers of orphaned children in Nepal and was determined to do something about it. With her babysitting money, she built a children's home and school in the Kopila Valley, and now has 53 children, some of whom have just gone off to college. Maggie is one of the women featured in our  anthology.  with host Stacey Gualandi, is a show from , an Online Magazine which features news and interviews with women who want to make the world a better place. From newsmakers, changemakers, entrepreneurs, best-selling authors, cancer survivors, adventurers, and experts on leadership, stress and health, to kids helping kids, global grandmothers improving children's lives, and women who fight for equal rights,"It's the world as we see it." The Women's Eye Radio Show is available on iTunes and at . Learn more about The Women's Eye at 

Doyne Farmer, Professor of Mathematics at Oxford University, moderated a session on computing technology and macroeconomics at NIESR's recent "Rethinking Macroeconomics Conference". Here is a taster of what emerged in the session.

We are getting better at predicting things about our environment - the impact of climate change for example. But what about predicting our collective effect on ourselves? We can predict the small things, but we fail miserably when it comes to many of the big things. The financial crisis cost the world trillions, yet our ability to forecast and mitigate the next economic crisis is very low. Is this inherently impossible? Or perhaps we are just not going about it the right way? The complex systems approach to economics, which brings in insights from the physical and natural sciences, presents an alternative to standard methods. Doyne will explain this new approach and give examples of its successes. He will present a vision of the economics of the future as it confronts the serious problems that our world will face. J. Doyne Farmer is Director of the Complexity Economics program at the Institute for New Economic Thinking at the Oxford Martin School and Professor in the Mathematical Institute at the University of Oxford

This weeks guest is Professor Doyne Farmer. Doyne is a physicist, econo-physicist, and founder of the Prediction Company, which brought insights from physics to the world of finance and stock markets. He is a Professor of Mathematics at Oxford University, where he co-directs the Oxford Martin Programme on Complexity, and is External Professor at the Santa Fe Institute. We ask him about econo-physics, how it ties in with complexity theory, and what this all means for the current economic orthodoxy. We also talk about some models he has built that try and replicate the housing crash experienced in the Washington DC area, and how leverage and market impact works to destabilise our economies. He also has recommendations for what we should use instead of mark-to-market accounting that might help quell some of the market instability and high leverage we see about us today. And on top of all that, we talk of his days as a professional roulette player....