Podcast appearances and mentions of thomas bayes

In this powerful episode of The Reformed Brotherhood, Tony and Jesse return to their parable series with an in-depth examination of the Laborers in the Vineyard from Matthew 20:1-16. This often-misunderstood parable confronts our natural inclination toward merit-based thinking and exposes the scandal of God's grace. The hosts unpack the covenantal language embedded in the text, particularly the workers' "grumbling"—a loaded term echoing Israel's wilderness rebellion. Through careful exegesis and theological reflection, they demonstrate how this parable dismantles religious entitlement while celebrating God's sovereign freedom to bestow mercy according to His purposes, not our calculations. The discussion offers fresh insights into grace, election, and the radical generosity that defines God's kingdom economy. Key Takeaways The parable operates on covenant logic, not economic fairness: The landowner's dealings with his workers reflect covenantal promise-keeping rather than marketplace transactions, establishing that God's relationship with His people is fundamentally gracious. "Grumbling" carries profound theological weight: The Greek word used for the workers' complaint is the same term in the Septuagint for Israel's wilderness rebellion—not mere dissatisfaction, but a covenantal accusation against God's faithfulness. Two types of workers represent two approaches to God: The first-hired workers who contracted for specific wages represent those relating to God through legal obligation and merit, while later workers who trusted the owner's promise represent faith-based relationship. The reversal of payment order is narratively essential: By paying the last workers first, the landowner deliberately exposes the merit-based assumptions of the first workers, forcing them to confront their entitlement. Grace doesn't negate justice—it transcends it: The landowner fulfills every contractual obligation while simultaneously exercising sovereign generosity beyond what is owed, demonstrating that mercy and justice coexist in God's character. The parable addresses the present kingdom, not just heaven: Because it includes grumbling and complaint, this parable describes life in God's kingdom now—the "already but not yet"—rather than the consummated state. Divine sovereignty in salvation is the theological climax: The landowner's declaration "Am I not allowed to do what I choose with what belongs to me?" directly addresses God's freedom in election and the scandal of unmerited grace. Key Ideas The Covenantal Nature of the Landowner's Dealings The parable's opening establishes a formal agreement between the landowner and the first workers: one denarius for a day's labor. This contractual arrangement is crucial for understanding what follows. Unlike marketplace haggling, this represents a covenant—the landowner binds himself to provide what he has promised. Tony emphasizes that even this initial contract is an act of condescension and grace, as the master had no obligation to employ anyone at all. As the day progresses, subsequent workers are hired with increasingly less formal agreements. By the third hour, the landowner promises only "whatever is right," and by the eleventh hour, no wage is even mentioned. These later workers enter the vineyard based entirely on the landowner's character and trustworthiness. This progression mirrors the movement from law to gospel—from contractual obligation to trusting promise. The theological implication is profound: those who relate to God based on His gracious word rather than calculated merit are actually in a more secure position than those who attempt to earn their standing through works. The Wilderness Echo: Grumbling as Covenant Violation The hosts make a critical exegetical observation about the Greek word for "grumbling" (γογγύζω) used in verse 11. This is not casual complaining but the identical term used throughout the Septuagint to describe Israel's covenant rebellion in the wilderness. When the workers grumble "upon receiving" their wages, they're not merely expressing disappointment about pay inequality—they're filing a covenant lawsuit against the master, accusing him of unfaithfulness. This connection to Numbers 16 and Exodus 16-17 is devastating. The Israelites' wilderness grumbling wasn't about logistics or comfort; it was fundamentally about doubting God's covenant fidelity. By employing this loaded terminology, Matthew signals that the first workers' complaint is nothing less than accusing God of covenant violation. The landowner's response ("Friend, I am doing you no wrong. Did you not agree with me for a denarius?") is a covenant defense—he has fulfilled his obligations precisely. The workers' real offense is not miscalculation but begrudging God's freedom to show mercy beyond what is contractually required. The "Evil Eye" and Begrudging God's Grace The final rhetorical question—"Or do you begrudge my generosity?"—contains another Jewish idiom often lost in translation. The Greek literally reads, "Is your eye evil because I am good?" This "evil eye" imagery appears throughout Scripture as a metaphor for envy, stinginess, and resentment toward another's blessing. The landowner's question cuts to the heart: are you cursing me for being generous? This directly parallels Jonah's response to Nineveh's salvation. Jonah had just experienced miraculous deliverance through the great fish, yet when God showed identical mercy to the Ninevites, Jonah's response was essentially, "I knew you were gracious—that's why I ran!" The parable exposes the same perverse logic: those who have received covenant mercy begrudging that same mercy extended to others. For the Pharisees listening to Jesus, this was an indictment of their resentment toward tax collectors and sinners receiving the kingdom. For Christians today, it challenges any sense of spiritual superiority based on how long we've been in the kingdom or how much we've sacrificed. Memorable Quotes Am I not allowed to do what I choose with what belongs to me? Or do you begrudge my generosity? That 'or' is a logical connector—either I'm not allowed to do what I want with my belongings, which is ridiculous, or if I am allowed, then you must be mad at me for being generous. Those are the only options. — Tony Arsenal The grumbling in the Old Testament in this context is a covenantal accusation. These workers aren't just complaining about not getting what they thought they would—they're questioning the veracity of the covenant that was made. — Tony Arsenal Most of us are this eleventh-hour call. It's much better to be in the place of that younger brother who comes in and repents than to be the older brother who is stubborn and finds some reason to come before God with self-righteous grievances. — Jesse Schwamb Full Episode Transcript [00:01:05] Jesse Schwamb: Welcome to episode 488 of the Reformer Brotherhood. I'm Jesse  [00:01:13] Tony Arsenal: and I am still Tony, and this is the podcast where Tony comes back. Hey brother.  [00:01:19] Jesse Schwamb: Hey brother. The band is back together again, man. It's reunited and boy, do you feel it? It feels good, doesn't  [00:01:26] Tony Arsenal: it? I do, I do. I'm excited to come back. It was nice to take a break. [00:01:29] Jesse Schwamb: Good.  [00:01:29] Tony Arsenal: I, uh, I've been, you know, texted with you a couple times. Just it was, I did my best to sort of not think about the podcast because that's sort of defeats the purpose of taking a break from something if you spend a lot of time thinking about it. Um, so I'm back. I'm refreshed. I'm ready to go.  [00:01:44] Break and Work Chaos [00:01:44] Tony Arsenal: I appreciate the listeners' patience. Uh, it's been sort of a weird, crazy busy time at work. Uh, there's a lot going on. I, I lost like. 60% of my staff in the course of like three weeks. And, um, I'm still kind of in the thick of it, but we're coming out of it. So took a little bit of time to just make sure that I was having a, an appropriate space to de-stress from that and take care of my family and attend to worship. And, um, it was really a, a blessing to have that. Uh, sort of sabbatical. Ironically, the sabbatical wars were going on at the same time on Twitter, and Jesse is blissfully unaware of that 'cause he's not involved in in the Twitter. That's true. Um, but yeah, just took a little break and it's kinda like overblown it, to call it a sabbatical. Like this is a podcast, it's a hobby, but, but it was nice to have, uh, a little bit of extra time, you know, couple hours extra week, uh, uh, each week of extra time to just decompress and, uh, play with the kids and spend time with my wife and clean the house a little bit, which was good.  [00:02:36] Jesse Schwamb: Yeah, it is always good to have a clean house. You look great. You seem refreshed. The voice sounds good, and I'm like, I don't know, in year seven or eight of my Twitter sabbatical, it's going great so far. I feel like I haven't missed a whole lot. The world still seems wild and I'm sure, or X, right? We gotta go X on this. It's  [00:02:53] Tony Arsenal: always Twitter. It's always gonna be Twitter. I don't care what Elon Musk  says.  [00:02:56] Jesse Schwamb: Yeah, I'm listen. I'm totally fine with that.  [00:02:58] Back to Parables [00:02:58] Jesse Schwamb: And I teased this in the last episode, but we can't be stopped. I mean, people should know this by now, we have an inexorable march through the parables of Jesus's true. That will not be stopped. We're always gonna come back until there are no more. And on this episode, we're gonna be hanging out in Matthew 20, talking about laborers in the Kingdom of Heaven.  [00:03:17] Tony Arsenal: Yeah. Yeah. I'm stoked. I'm, I'm, I'm excited to get back into it. I'm excited to get back into the word together with everybody. I'm excited to clear whatever that was on in my throat out  [00:03:27] Jesse Schwamb: emotion,  [00:03:27] Tony Arsenal: live on the air. Uh, but yeah, it'll be good. I'm, I'm stoked. I mean, I love this stuff and it's good to be back.  [00:03:32] Jesse Schwamb: Listen, you had the rest. Now let's talk about labor. So speaking of labor, it's, it's time for you to work up here, Tony. Are you affirming with or denying against on this episode?  [00:03:42] Tony Arsenal: Uh, I'm affirming something and I'm hopeful, uh, that just a little behind the scenes activity here. Jesse recorded episode 487, like an hour and a half ago. I have not yet listened to it, so I don't know if you did an affirmation and I I did. If you did. I hope it's not the same one.  [00:03:58] Jesse Schwamb: I did not. You're  [00:03:59] Tony Arsenal: safe. Uh, good. So I'm safe.  [00:04:01] Artemis II Hype [00:04:01] Tony Arsenal: So, um, I'm affirming the Artemis two mission. Um, oh, nice. Have you been, I mean, I know you're not on Twitter, but I'm sure there's news elsewhere. Uh, this amazing mission around the moon, um, for astronaut, for astronauts, I think, um, the furthest man space travel, um, since the Apollo program. Um. Pretty intense, pretty amazing pictures, right? The camera technologies amazing. Increased exponentially, uh, since we were there last. Um, this is ostensibly in preparation for an actual moon landing, which who knows when that will be? Um, but as far as I've seen, the mission was a resounding success. There was no right. I think they had, they ran into a few little hiccups early on with some technical things, but nothing crazy. I have not heard. Um, I know they did touch down and they did reentry. Um, I've not heard anything one way or another, but I'm assuming since I have not heard terrible, tragic news that they made it through, did they do the reentry? I'm really, apparently I'm not actually paying as much attention to this as I thought I was. I saw a lot of information about reentry, but I guess, I don't know for sure when that happened or is happening.  [00:05:05] Jesse Schwamb: I mean, by this point, when people listen to it, it'll be old news anyway, right? So  [00:05:09] Tony Arsenal: For sure. Yeah. And either, either it went terribly wrong and I'm gonna feel awful, or it went fine and I'm gonna feel a little silly for. Throwing a caveat that it went terribly wrong out there. But, um, it's cool. It's, it's amazing. I mean, I, I commented to my wife the other day and she's kinda like, yeah, maybe we should like, spend that money on people who are on the planet. I was like, okay, I can, I can buy that wisdom. But, um, there's something very cool and very Genesis, uh, one, ask Genesis one and two, ask about flying out into space and taking dominion over Yeah, for sure. Over a, a little ball of rock, uh, you know, uh, 25,000 miles away or whatever it is. Um. And, you know, I'm like an engineering nerd. I, I don't know anything about engineering, but I love watching YouTube videos that explain stuff like this. And  [00:05:52] Jesse Schwamb: me  [00:05:52] Tony Arsenal: too, all of the videos that have cropped up now about free return and how, like they're able to basically like do minimal burn on the thrusters to get into the right trajectory and then just like meet the moon in the place it's gonna be. And then the, you know, the moon's gravity captures it and whips it back around and then shoots it back towards Earth. And for the most part, they're able to do all of that with relatively minor, um, relatively minor energy output because they're just utilizing physics and gravity and math, um, to fly to the moon and come back. Yes. It's pretty crazy amazing. So, yeah. Amazing. And the photos of like the, the sort of like new versions of the Earthrise photos are really, really phenomenal. Um, they're crisp, they're clean, they're obviously like the best, the best actual pho photographic images we've had of the lunar surface. Um. And the, the far side of the lunar surface, which we get all sorts of like telescopic photos and things of this side of the lunar surface because it's tightly locked and is facing us at all times. We don't get a ton of really great photography of the far side of the moon, which is a big part of what this mission was, so,  [00:06:56] Jesse Schwamb: right.  [00:06:56] Tony Arsenal: Yeah. If you haven't seen the photos, I mean, they're out there, they're amazing. There will be even more available once we get back. You know, they, they're transmitting only the most stellar, amazing ones. Um, and, but they're taking, I'm sure thousands and thousands of photos and, um, so yeah, it's pretty cool. I'm affirming the Artemis two mission. Um. It's just amazing what, what people can do with common grace, you know? That's right. In insight into nature. Um, I don't know anything about the astronauts. I don't know anything about their religious faith or their spiritual life or anything like that. But, um, the people who design this, the people who fly it, they're just tapping into the truth that's present in God's creation. So good on them. Uh, either I'm glad they got home, wish they have a safe home coming, or something along those lines, I guess. I don't know.  [00:07:40] Jesse Schwamb: Yeah, you'll be happy to know that NASA is reporting that the four astronauts are an excellent condition after they landed in the Pacific Ocean. So  [00:07:47] Tony Arsenal: good.  [00:07:47] Jesse Schwamb: All, all appears to be well. And it says they have a giant SD card of pictures that's they've been taking. Yeah. And saving. I'm sure. They were just, they were just too big to send to over wifi.  [00:07:58] Tony Arsenal: Yeah. Like massive wideness. Yeah. I mean, I'm sure they have a ton that they didn't send because you know Right. Data rates to the moon are pretty high. Yeah.  [00:08:05] Jesse Schwamb: Ex. Yeah.  [00:08:05] Tony Arsenal: This economy is crazy. So  [00:08:07] Jesse Schwamb: Exactly. In this economy. Really In this economy. Yeah, exactly.  [00:08:11] Cosmic Worship Reflections [00:08:11] Jesse Schwamb: I think you're right. This is good. I haven't talked about this at all. It's hard not to get just stoked, even in the amateur way about the science, the technology, the physics of all this stuff, and then even the astronauts just being overwhelmed by what they're seeing. [00:08:24] Tony Arsenal: Mm-hmm.  [00:08:25] Jesse Schwamb: It's hard not to get pulled into that and think about the universe that God has created and find that there is something transcendent just, uh, by observing all of these things. Yeah. Like even casually, which I think shows, again, this is literally the, the heavens and the earth crying out for God, showing his immeasurable power and, you know, immortal nature. It's incredible that we can even see and be a part of some of these things. Just wild.  [00:08:49] Tony Arsenal: Yeah. Yeah, and I think it's crazy that they can get signals to the moon. I mean, I drive home from Dartmouth College and I go through half of the spot there, and I don't have a cell signal, but we can get images from the moon. Um, so yeah, it's great. It's great. Check it out if you haven't seen it. If you haven't heard about it, I don't know what you're doing. Uh, this is probably the largest major scientific advancement in our generation. Um, in terms of like big scale scientific enterprise projects. There's been a lot of really amazing technology that's been developed. But this is like the first big. Almost like risky kind of scientific,  [00:09:30] Jesse Schwamb: right?  [00:09:30] Tony Arsenal: I dunno. Gambit or I dunno, gamble that we've done in a long time. Big deal. I mean, big a lot. Deal of things. Deal. Nothing went wrong. Nothing ma major went wrong. Praise God that they all got back to the planet safely. Right. But, um, a lot of things could have gone wrong, uh, and they didn't. So check out the photos, check out the scientific data they're gonna get. I mean, I'm sure they've got all sorts of information about the way the, the, the space ship moved, all of that stuff. It's gonna be really interesting to see kind of how this all comes about.  [00:09:56] Jesse Schwamb: Get some worship on, right? Yeah. I mean this is what a one, a thing to be reminded about how big and how glorious God is. [00:10:01] Tony Arsenal: Yeah.  [00:10:01] Jesse Schwamb: And, and to realize, like you said, the risks of this exploration. And this is God again, creating all of this outta nothing. Why? Yeah. Just absolutely wild. Incredible.  [00:10:12] Tony Arsenal: Yeah. Yeah, for  [00:10:12] Jesse Schwamb: sure. Blown away.  [00:10:13] Tony Arsenal: Yeah. What about you, Jesse? What do you have for us?  [00:10:15] Bayes and Predictability [00:10:15] Jesse Schwamb: I got affirmation. It's equally nerdy, and actually this is as is always the case. This is why one of many reasons I miss you is it, it dovetails so nicely, so I'm affirming with a book. It's called Everything Is Predictable, how Esy and Statistics Explains the World. It's by a guy named Tom Chivers. I know this sounds super nerdy, but hear me out on this because Thomas Bayes, if you don't know this guy is first kind of like a wild and interesting guy, but this whole theory he put forward is super interesting. And this book is not like a mathematics book. It's like reads almost like a statistical thriller, which as it came outta my mouth, realized it was not maybe more ingratiating. I could have chosen better words than statistical thriller. But Thomas Bayes was alive in the 17 hundreds. And what's interesting to me at least about him, is he was an English statistician, who was a Presbyterian minister actually. He was a non-conformist and his, this whole theorem that he developed was actually published after his death. And the non-conformist part is super interesting. It's all in this book, even some of his different theological ideas. But because he was non-conformist, it basically meant like he couldn't learn. He was kicked out of all the English universities. He had to go to Scotland. Even all of that shaped how he came up with this particular theorem. But the gist of it is. Rather than treating like probabilities, as we think about it as this fixed frequency, you know, how many times does this thing occur? He argued and realized that it should represent a degree of belief and then you would update that belief rationally as new evidence comes in. And I know that sounds super quaint, but this is like what machine learning is based on medical diagnosis. A lot of like space travel is based on this in terms of understanding uncertainty and systems spam, all of that stuff. Here's an example, I think Tony, because we are, we have to carry forward with the top 50 medical podcast thing, right? We've got going on here. Lemme just give everybody an example of why you need this and why you automatically think this way. So. Statistics is really important, especially in medical testing. This was really prevalent in during COVID. So there's two ways that you can describe how a medical test performs you. You know this already, Tony, you're an expert. So one would be like sensitivity. So like how AIG  [00:12:19] Tony Arsenal: not an expert.  [00:12:20] Jesse Schwamb: Oh, you're definitely an expert in testing. Here we go. So one would be like sensitivity. How good is the test at catching people who are sick? So if you're sick, you, you want the test to identify that, that you're sick. That's sensitivity. So a test with a 99% sensitivity is gonna correctly identify 99 out of a hundred people who are truly sick. It always gonna miss one person. It's a false negative. The other half of that coin is something called specificity. So if sensitivity is all about catching the people who are sick, specificity is gonna say, how good is the test at clearing people who are not sick? And so a test with 99% specificity, you might have correctly guessed, is gonna identify or clear 99 out of a hundred healthy people. Now if you have a test. Both of those 99% sensitive and 99% specific, you might be thinking, that is the dream. That's exactly what I want. That that test is gonna be so precise and accurate. How could my intuition fail me? But this is the thing. It actually fails all the time, and here's why. Let's say that. You go out and you screen a group of people, a general population for a rare disease that affects one in a thousand people. One in a thousand people, rare disease. So if you screen 10,000 people from the general population, that means that truly only 10 of them are going to have the actual disease. I'm not gonna do all the math 'cause it'll, oh, this is already making for amazing podcasting. But here's the bottom line. That test, which sounds so good on the face, is going to identify 109 people as truly sick or truly having disease. But the problem is that only 10 of them actually have it. That means that only there's, it only has a success rate of 9%. There's only 9% chance you actually have the disease, but it's falsely identified. The short end of this is Bayes corrects that problem. He fixes it with his theorem so that we get to the right number of people. That's what's called like a base fallacy rate. It's not taking into account that really only 10 people should have this particular disease or this sickness. So I know that's sounds super nerdy, but so much of our lives are based on this. We have a prior belief or a prior set of things that we understand about the world. And then as evidence comes in, we refine that. That sounds so normal and normative, but it's revolutionary in this book actually. Bayes versus what's called like frequentist or frequent, um, probability is like hotly debated. People actually throw down over this theorem. So it's a really fun read. Go check out. Everything is predictable. Al Bayesian statistics explains our world. It really is for everybody. And then you can impress your friends with all the statistical pross you're gonna have when you're done reading it.  [00:14:56] Tony Arsenal: Like the medical administrator hat that I can't always take off is like, why would we screen 10,000 people? Are, are they all symptomatic? Are none of them symptomatic? But suppose it doesn't really  [00:15:08] Jesse Schwamb: matter for the example. That's a great, so generally what happens here is, let's say it's like some kind of rare form of cancer, unless you use Bayesian statistics, what you'll find is you'll get these false positive rates. So these tests do use Bayesian statistics. It corrects, in other words, for this problem. So there might be a lot of people that are gonna screen for this because if you, you wanna know if you have it, but you don't wanna get it wrong and say that you do. So this ensures his approach ensures that you get it. Right. It's wild. Fascinating stuff.  [00:15:34] Tony Arsenal: Yeah, and I would think actually, you know, there's probably, there's other mechanisms as well where they would, where they would sort of screen out. People that shouldn't be tested or help identify false negatives, false positives. Um, but yeah, that's, that's interesting. I probably won't read that book, but it sounds like an interesting read. I just don't have a lot of room on my A TBR shelf.  [00:15:55] Jesse Schwamb: Yeah, listen. That, that's fair.  [00:15:57] Goodreads DNF Update [00:15:57] Jesse Schwamb: By the way, here's like a, a side affirmation. I think you and I both share speaking like books and cataloging books. If you use Good Reads, good Reads. Right. Finally adding a list of the Do Not Did Not Finish book. That's fantastic. This, this might be an example for some people, so pick it up and even if you don't have a place for it, guess where you can put it on the did not finish list. Yeah. Good Reads.  [00:16:16] Tony Arsenal: That's finally, that's one of those like, like why didn't they add that 15 years ago? Kind of an updates and you get the email and they're like, we're so excited to introduce the did Not Finish thing. And we're like, yeah. Like of course. Like, duh. It's likes, like, we're proud to introduce that. Your keypad now has a zero on it.  [00:16:36] Jesse Schwamb: Right. So  [00:16:37] Tony Arsenal: yeah. I'm, I'm excited about the DNR, um, the DNF, um, I'm so excited. I can't even remember what it's called. Yeah. The shelf. But, uh, very, very useful. The DNR list  [00:16:47] Jesse Schwamb: is a diff it is a different list. Speaking of medical things, it's a different  [00:16:50] Tony Arsenal: list. Yeah. Yeah, that's definitely a different thing. Usually it's not a list. It's a list of one in most cases.  [00:16:56] Jesse Schwamb: Exactly,  [00:16:57] Tony Arsenal: yeah. You can't put other people on your  [00:17:00] Jesse Schwamb: DNR  [00:17:00] Tony Arsenal: This,  [00:17:00] Jesse Schwamb: I suppose. Yeah, I should clarify that. You can really, you can only really put yourself, or I suppose somebody for whom you have that kind of authority over on that list, but I was thinking that more from like a medical perspective, that somewhere there would be a database in which there might be a list of DNR. I don't know.  [00:17:15] Tony Arsenal: Yeah, maybe. I don't know. I'm not sure. Probably there was at some point, but I think with medical chart technology now, that's probably like a. A moot point. Yeah. They don't need to be able to like cross reference a master list anymore. They just look in the patient's electronic record. We're really like in the weeds here. You can tell it's been a while since I've, I've podcasted. I don't really remember how to do this.  [00:17:35] Jesse Schwamb: This is great.  [00:17:36] Segue to Matthew 20 [00:17:36] Jesse Schwamb: I think at this point we try to make some kind of awkward segue that is mildly successful. Again, probably has statistically like a 20 to 27% chance of being successful and really hitting the mark. Yeah. So do you have anything that's gonna move us into this?  [00:17:49] Tony Arsenal: Yeah, I mean, I feel like you've been podcasting for the last several weeks without me and I've been working hard and now I'm kind of coming in as Johnny come lately and we're gonna get paid the same amount so. Even though you've worked harder for longer and I'm coming in late to the game here. [00:18:03] Jesse Schwamb: Oh man. Ple loved ones. Please tell me you got that. Please tell me you got all of that. That's, that's what you show up for here. Yeah, that was  [00:18:10] Tony Arsenal: a deep cut.  [00:18:11] Jesse Schwamb: That, that was beautiful. And I think leads us right into Matthew 20. So I think we've got at least 16 verses to get through here. Maybe again, if we're gonna keep a statistical theme here, something about engineering and math, all that stuff, we'll let everybody else pick the over under and whether or not we're gonna get through this and how many verses that's going to be. But at this point, we might as well begin.  [00:18:32] Tony Arsenal: Yes. Yeah.  [00:18:33] Read the Parable [00:18:33] Tony Arsenal: I'll start by reading. Uh, we're here in Matthew chapter 20, the first 16 versus this is the parable of the laborers in the vineyard and it reads. For the Kingdom of Heaven is like a master of a house who went out early in the morning to hire laborer laborers for his vineyard. After agreeing with the laborers for a denarius a day, he sent them into the vineyard and going out about the third hour, he saw others standing idle in the marketplace. He said to them, you go into the vineyard too, and whatever is right, I will give you. So they went, going out again about the sixth hour and the ninth hour, he did the same. And about the 11th hour, he went out and found others standing. And he said to them, why do you stand here idle all day? They said to him, because no one has hired us. And he said to them, you go into the vineyard too. And when the evening came, the owner of the vineyard said to his foreman, call the laborers and pay them with their wages, beginning with the last up to the first. And when those hired about the 11th hour came, each of them received a denarius. Now, when those hired first came, they thought they would receive more, but each of them also received a denarius. And on receiving it, they grumbled at the master of the house saying, these last worked only one hour and you have made them equal to us who have borne the burden of the day and the scorching heat. And he replied to one of them, friend, I'm doing you no wrong. Did you not agree with me? For a denarius, take what belongs to you and go, I choose to give the last worker as I give to you. Am I not allowed to do what I choose with what belongs to me? Or do you beg, do you begrudge my generosity? So the last will be first and the first will be last. Now I just wanna head this off. I did bite my tongue earlier and I probably am lisping and this is like a running gag. We thought that we'd resolved it. Uh, so if you hear me stumble over my words a little bit, it's just, it's just the struggle bus today.  [00:20:24] Jesse Schwamb: Listen, this is the, these are like the real things we have to deal with when the podcasting, like the real threats, the real injuries. I appreciate you like working through it. Like you just get back up and you walk it off with your tongue.  [00:20:35] Tony Arsenal: Yeah, my, my, uh, my podcasting hiatus was actually just a recovery of the last time I bit my tongue. I just needed a couple weeks to, no, I'm just kidding.  [00:20:43] Jesse Schwamb: Yeah, we didn't wanna say.  [00:20:44] Tony Arsenal: Yeah.  [00:20:44] Kingdom Fairness and Grumbling [00:20:44] Tony Arsenal: So, Jesse, this is a, this is a parable that follows right on the heels, um, of kind of everything we've been talking about. And I think as we go through these parables and we look at them and we, we sort of pick them up and we look at the different facets of them, we sort of compare them to each other. We kind of, we kind of place them in their context really. They all have basically the same theme, right? Like they're all kind of circulating around these same topics. In this parable, it's circulating around this idea that, um, the, the owner of the vineyard, the master of the vineyard, is allowed to pay the people he employs whatever he wants. And as long as the payment that is due to an individual is received by that individual, then what other people receive and how they receive it and how hard they've worked and how hard they didn't work. That's really not germane to whether or not the, the laborer received a fair wage, uh, in the first place. Right. So we're, we're circling around themes of kind of fairness of, uh, of sort of resentment, I think for resentment at the master's generosity, which has been a big theme in previous ones. So this will be good for us to expand on. There's always little nuggets and kernels of things that are different from other parables, and then it's interesting to always see the ways that they kind of line up and, and tell us similar things.  [00:21:57] Jesse Schwamb: And this parable is unique to Matthew. Yeah. And it does function as this exposition or expansion of what Jesus says in chapter 19 where it says, but many who are first will be last. And the last first, which is repeated with this lovely like inverted emphasis in, at the end of this as you just read. So it belongs to this like interesting cluster of teacher teachings on discipleship and reward nature of the kingdom of God. And we've, we've spoken a lot about that. I think I was just reminded of this as you were, you were. Reading this, I feel like I remember this from some teaching, like this parable is kind of like a unique chiasm that's anchored on the landowner, sovereign generosity, which you brought up. And then there's the complaints of the first hired, which is mirrored by the late comers vulnerability. And then the landowners, two speeches which divide everything, kind of provide sandwich and the like, the theological climax. It does start in that really familiar way, which we've gotten accustomed to thinking about that introductory formula of the kingdom of heaven is like, and it signals of course that what follows is not gonna be a lesson in economics, but it's gonna use all this economic language as theological disclosure for how God's kingdom operates. And it starts again, like you said, with this master of the house, which to me seems. Pretty clearly like a, a God figure himself. Yeah. It's, that's kind of like a reoccurring mathian image. I think. So we've got this vineyard, which of course has all this symbolism, steeply rooted in Israel's covenant imagination and evokes God's people and his redemptive labor among them. So, man, now that I'm saying this all loud, is this thing like super pregnant with all kinds of like imagery and meaning?  [00:23:27] Tony Arsenal: Yeah. Yeah. And you know, it's, it's always good to remember, although parables have kind of some parables, most parables have sort of distinct discreet, symbolic elements where like, this represents that this represents that almost in an allegorical form. And, and in some cases, like purely in allegorical form, where it's like pilgrim's progress where each, each individual, each entity, each location each represents some sort of symbolic value. But we have to remember that when, when it says the parable of the kingdom of heaven is like the master of the house, it's not just like the master of the house. Yes. Right. It's like this whole scenario. Yes. It's, it's like. Blah, blah, blah, blah, blah. It's like everything that follows, it's like the entire, um, the entire paree here. That's what the Kingdom of Heaven is like. And one of the things that I think is striking about this is the kingdom of heaven is like some people complaining, like the people complaining about, some people are getting the same wage for less work. Um, that is part of what the Kingdom of Heaven is like. So I think we sometimes think of, of. The kingdom of heaven in, um, in the parables, we think of it as though God is just saying, this is what heaven is like. Right? Jesus Just saying like, this is what heaven is like, but the kingdom of heaven, that language is broader than what we normally would say, uh, is. We're thinking of heaven, like in the, the spiritual abode where God lives and the angels live. Um, where, where the departed saints are waiting for the resurrection, the kingdom of heaven is, is also inclusive of the, the sort of like. Time now between the victory of Christ on the cross and the consummation of the kingdom and the last day, the kingdom of heaven is inclusive of that time period too. And so this parable sort of situates us. I think it situates us in that pre consummated state where we're talking about what it's like to be a part of the kingdom of heaven here and now in our fallen state, but still solidly in the kingdom of heaven. 'cause there's not gonna be any complaining or grumbling about God's justice in God's fairness once we're in the final resurrected state. Right? Sure. Nobody's gonna be looking back and be like, yeah, you were way too gracious for that guy. Nobody's gonna be playing the Jonah part when we're all resurrected and we're worshiping for, for all time going forward. So this parable, because there are elements of. Dissatisfaction or elements of grumbling or complaining similar to like the, the parable of the prodigal son. There's this sun figure, the, the older sun figure who like is just a bonehead and doesn't get it. Well, that can't be talking about the people who are in the resurrection kingdom in the final kingdom. It's gotta be talking about people who are still awaiting the resurrection of the body and who are still not yet. Uh, and even in, in that parable, the, the older son doesn't even seem to be a figure who's, who's regener. Maybe he does become regener at some point in the future, but he doesn't seem to be. In, even in God's kingdom, he doesn't seem to be, even among God's people, he's consistently placed outside of the field. You don't even know he exists until Nick halfway through the parable. This is similar in that there are these workers, they're receiving their wages and some of them are, are outwardly dissatisfied and grumbling against the master of the house. Um, so I think if we think about parables as describing heaven rather than the kingdom of heaven, we can lose sight of, of what's actually being said in a lot of them. [00:26:50] Contracts Versus Grace [00:26:50] Jesse Schwamb: Yeah, that's really good stuff because it strikes me that there are like, strangely, two groups here mentioned, I, I find this really kind of fascinating. We, I think we should talk about this, like the first group has like the most formal agreement, it's almost a legal contract, right? Various was like a standard day laborers wage sufficient mostly for subsistence. And so that detail seems theologically loaded to me. These workers relate to the landowner on the basis of a contract and what is owed. And so their claim at the end of the day will be exactly that. They're owed something and they know it, and that sets up Then this contrast with a second group, which is mostly all about grace because by the time we get to that third hour, like. Approximately like 9:00 AM then we're beginning this pattern repeated at the sixth and the ninth hours. And crucially, for those workers who go out, go out and get recruited, there's no wage that's specified for them. Only the promise of like whatever is right. And so they enter the vineyard, not on the basis of a contract, but on the basis of like the owner's word and character. And that seems to be like more of a picture of trust and not, not calculation. Yeah. Separate than like the first group. And that marketplace, idleness, as I read this, doesn't imply like laziness because verse seven clarifies like they just had not been hired. Right? They were overworked, they were unemployed. They were marginalized. So it does set up, like you said, everything you just talked about, about the kind of this, I like that. Like the Jonah, the Jonah whiners or whatever, like yeah, they want to complain about this, right? There are, and there are two, two separate groups that have kind of been brought into the fold, not under different terms or pretenses, but differently. [00:28:17] Tony Arsenal: Yeah. And I think too, bear's saying, um. Although there are elements of parables that are very, very directly applicable. Mm. We shouldn't read this as though every, every specific thing in the parable is not a parable. Right. Right. I think we can look at this and we can go, you know, you can read this in a way where, oh yeah, there's some people actually earn their, earn their wage, they earn ary. Right. It's a fair contract. And they work all day and he says, well, I'm gonna give you what's right, what you, what I owe you.  [00:28:45] God Owes Nothing [00:28:45] Tony Arsenal: The reality is God doesn't owe any of us anything. Right? Right. He owes us wrath and judgment and destruction. And so even, even the people who are the hard workers in the kingdom of God don't merit and never could merit, um, to, in a certain sense, in a strict sense and stick with me before you send your, your angry emails in a real strict sense. Even Adam couldn't merit. What was, well, it was guaranteed to him, according to the Covenant of Works, God had to condescend to make the covenant of works in order for Adam to have any sort of fruition of his blessedness. So there there's no natural obligation, strict obligation that God has to reward the work of his creatures because nothing they could do could ever be sufficient enough to obligate him. So the, the obligation of himself, and that's, this is where I do think this is strong, the fact that he obligates himself to these workers to give them their denarius after a hard day's work  [00:29:37] Jesse Schwamb: exactly  [00:29:37] Tony Arsenal: is itself. A covenantal, um, contractual, yes. But I actually read this as sort of a covenantal thing and the, the strange part is that the people don't recognize the sort of semi gracious covenantal nature of this. Yes.  [00:29:50] Grace In The Hiring [00:29:50] Tony Arsenal: I think, um, you know, there have been times when I, where I've been unemployed, um, not for very long. Now, I know some people face unemployment for a lot longer than I ever have, but I know there was times where I was, I was looking for work and someone would say to me like, Hey, you know, my, my, my lawn needs to be mowed. Could you come over and I'll, I'll give you 25 bucks to mow my lawn. It's a small lawn. Um. That's a gracious act in most cases. Right, right. Um, yes, I'm performing a task. Yes, they're paying me, but they didn't have to offer me that work. They didn't have to offer me that job, especially when it's something that like they could have accomplished themselves. They could have just done it themselves. Um, so I think there's an element of that here, that there's, there's a condescension of the master to these workers, to these laborers who are not part of his household. These are not, they're not slaves. These are not people who are part of his household, who are regular employees. These are people that he goes out into the market to, to find and to hire. And as we see some of, some of these mark, like the difference between the ones that are hired and the ones that are not hired until later in the day, the parable's not super clear about what it is. Just that they're not hired, it doesn't say the lazy ones were left there. The ones were exactly, that were ugly or had like limp legs or like just couldn't cut it. It just says like there was some that didn't get hired. Um, so there's a gracious element of this, and that makes the recognition at the end or the lack of recognition at the end by these full day laborers, the, the sort of like recognition, this, this entitled ness, um, that actually makes it all the worst. It's like the people who are outwardly attached to the covenant of grace. Um, I know all the Baptists in our, our group, their heads just exploded, but like are outwardly attached to the covenant of grace, um, who wanna somehow complain about like the graciousness of the covenant of grace that they're outwardly attached to it. It's just sort of like a form of, of theological and temporary insanity, I think. And that's what we see on full display here.  [00:31:40] Jesse Schwamb: It's definitely all grace. You're right that nobody's gonna get injustice right in this parable. And I think that's definitely exemplified the further out you go in this hiring order. [00:31:49] Eleventh Hour Mercy [00:31:49] Jesse Schwamb: So by the time you get to 5:00 PM which is pretty extraordinary, right? Only really like one hour remains before sense, right? It's the end of the working day.  [00:31:56] Tony Arsenal: Yeah.  [00:31:56] Jesse Schwamb: You can imagine like these guys who are being hired at the hour probably can contribute very little in the last hour of the day, right? But this owner goes out and hires them and no agreement is stated whatsoever. It's just pure grace. The landowner's question, why do you stand here idle all day? I think to your point, underlies their vulnerability. They were not idle by choice, presumably. And so I think we rightly here in this, like a foreshadowing of those who are called the late in redemptive history, Gentile sinners, the seemingly least qualified for kingdom membership. All of that I think is at play and it's all, it's getting this lovely setup of all these groups to help us understand what that kingdom is actually like.  [00:32:33] Tony Arsenal: Yeah. Yeah.  [00:32:35] Reverse Payroll Setup [00:32:35] Tony Arsenal: And then we have this, um, this is where the sort of dramatic tension turns, right? The end of the day comes and, uh, the master calls the, the people that he brought last, right? He calls the people who'd only been there for an hour and he starts to go down the list of the people who, the people who were last, and the people who came in next. And the people who came in next, right? And the workers who had contracted at the beginning of the day. Um, they're watching this happen and they're kind of going, oh, this is gonna be good. Like, that guy's only been here for an hour and he got a denarius. You know, the logic is probably like, I'm gonna get 12 denarius, like I'm gonna go 12 days worth of work. Um, because I think there's an assumption on their part, um, that the master's fair that he is, he's providing an equitable wage. Um, of course the master is fair, but he's providing an equitable wage that's commensurate with the work delivered. A delivered, delivered, right? And that, that's the key to this parable.  [00:33:26] Merit Mindset Exposed [00:33:26] Tony Arsenal: I think the expectation that God. Helps those who help themselves. Right? God rewards those who put in the hard work. God. God provides blessing or salvation according to the merit provided by the one who's being saved. That perspective is what's on full display here. Yes. By the people who are, uh, the ones who contracted for the full day. They're not thinking about the covenant that they have with this person or the contract they have with this person. They're not thinking about the fact that they agreed to work for the day in order to earn a day's wage. They're thinking about how this actually is gonna work out great in their favor. They're looking at this as a strictly merit-based kind of a, a thing. And you would think that like when the, the one hour people come in, they get a denarius, and then the three hour people come in and they get a denarius. You'd think they would pick up on it at some point, but then in the course of the payroll, it doesn't seem that they do. They still get to the bottom of the list and think they're gonna get more compared to the other people who all got the same.  [00:34:22] Jesse Schwamb: Yeah, that display piece is critical to this. It is like complete setup. Like you can imagine he, the landowner calling everybody together at the end of the day and they're all standing around. Some of them are exhausted because they've again born all their work in the heat of the day on their backs. They're tired, they're dirty, maybe they're exhausted. And he starts in this reverse order. And by the way, we should note that there is something here that's beautiful in that the law, the landowner is law abiding because right evening payment is mandated in the Torah. So we see all this taking place as to fulfill the law in some ways. But the reversal of the order that last of first is like such deliberative and good narrative storytelling and staging, isn't it? 'cause it ensures that the first hired workers are going to witness the payment of those who work the least. And if without that order, if you just did it the other way around, the more a crisis of the parable disc like completely goes away.  [00:35:10] Tony Arsenal: Yeah.  [00:35:10] Jesse Schwamb: So this execution of the payment at the owner's will, it just shows that he has. He's completely independent. His sovereignty belong. The sovereignty belongs to the master alone. And so this 11th hour workers receiving a full day's wage for one hour of work, that's like an act of sheer generosity. It's not proportional justice. And I think as reform, people, maybe all of us at some point have had this conversation about predestination and justice and mercy. And again, really I think putting a crowbar between this idea that nobody is receiving injustice, but some are receiving mercy and grace. And here these first hired workers seeing this form, like you said, this expectation that they're gonna receive more, like you said, where that came from. Yeah, it's just them, right? It's purely manufactured in their own reasoning. It's not anchored in the covenantal promise and certainly not witnessed in the grace that they should be receive, like perceiving as the payments get doled out, like sequentially moving in their reverse order toward those who have worked the longest. But their expectation reveals that they have fundamentally misread like the landowner's character. They're still operating in the register of a contract and not grace.  [00:36:16] Tony Arsenal: Yeah. And you know, I think to sort of lock this covenant covenantal frame and sort of like lack of recognition of the covenant into place too, when you look at the language of this parable, um, and especially kind of what it's following up on, it's coming on the heels of this interaction with this rich, rich young ruler who comes in and he thinks that he's gonna earn eternal life by keeping the commandments. Um, and, and he, he has this outward sense or this outward display of pty. He's calling Jesus good. He's saying he, you know, he keeps the commandments, Jesus doesn't even disagree with him actually, that he has connect. Yes. You know, I think it's implied that, well, of course you haven't, but he, he still is graciously trying to like, convince this guy, no, you actually need to abandon your self righteousness and, and pursue and follow me. Um. But this is a parable where like other people are listening, right? There's other witnesses. This isn't like the rich young ruler came to him in the middle of the night, like Nicodemus. This is something that's happened on PO on in the public. So we can anticipate that the Pharisees and the Sadducees and the scribes and the lawyers were all aware of this. They may have been there, but they were at least aware of this happening. And I think there's some language in here that is actually directed at those people.  [00:37:30] Grumbling As Accusation [00:37:30] Tony Arsenal: And, and here's where it comes in, is you get to verse, um, we'll start reading again at verse nine. It says, when those hired about the 11th hour came, each of them received a denarius. Now, when those hired first came, so we're referring to the people who are hired at the beginning of the day. Now, when those who were hired first came, they thought they would receive more, but each of them also received a denarius and on receiving it, right? So this is as, this is, um, uh, just unbelievable as they're receiving the denarius on receiving it, they grumbled at the master of the house. Now, just the way that I read that and said the word grumbled tells you that that word is really important here. Yes. If you look at this Greek word. And you compare it to the, the word, the usage of this word in the, the, um, Sept. Yes. Which of course is the Greek translation of the Old Testament. This word most commonly appears in the wilderness wandering accounts. [00:38:22] Jesse Schwamb: Yes.  [00:38:23] Tony Arsenal: Right. And the, the primary sin of the Israelites during the wilderness wandering was grumbling against the Lord. And this grumbling against the Lord in that context is not just a general complaining, right. It's not just like a, a sort of like a, a general dissatisfaction or like murmuring. This isn't like water cooler frustration about your boss. The grumbling in the Old Testament in this context is a covenantal accusation, right. So this is tied to the, the accounts where Moses first is told to strike the rock, and he does so when the water comes out, and then second is told to speak to the rock, but he strikes it. I won't go into all the details, but the scene that's being, being displayed there is the people come, they accuse the Lord of abandoning them into the wilderness. And this scene where Moses is set up on the rock and he strikes the rock, that scene is a judicial scene. The people have filed a covenant accusation against the Lord, and in reality, it's the people who have been unfaithful. But the Lord standing in the place of the rock is the one who is struck, right? Jesus was the rock in the wilderness from which the water came. Paul says that in First Corinthians, right? So this language of grumbling in this is not just, they're not just complaining about the fact that they didn't get what they thought they were going to, they're questioning the veracity of the covenant that was made. So they're, they're still locked into this merit-based. This merit-based idea even more than it seemed at first, right? There's a logic to the idea that like, oh, if the, the master is actually paying a wage of one denarius for per hour, like there's a logic to that. But it's not just that they're saying, and this is, this explains the response of the master. It's not just that they're saying like, Hey, wait a second, like the wage rate that you're paying is not right. They're saying you have violated the terms of our covenant in the way that you have paid us. 'cause it's upon receiving it that they complain or they grumble and the master says more or less like, Hey. You agreed with me for one Denarius, I'm giving you what you've earned. I'm giving you what you agreed on. Why don't you take it and go. So the answer is not to try to justify why he is free to pay these other people more, or why he's free to pay these people a perceived less. The answer is, again, they're complaining against the covenant. He is bringing it back to the covenant saying, well, here's what the covenant relationship was. You work for the day. I give you Denarius. We're square here, we're on the same page. We've fulfilled our covenant obligations, and you've received your reward for that. So I, I think that's another thing we have to lock in here is this is not just a general idea of like unfairness that's being presented. This is not just a general idea that people are saying the master of the house is unfair. They're saying he's covenantal. Unfaithful. Right? That's a pretty big accusation.  [00:41:09] Jesse Schwamb: Yeah, that is, thank you by the way, for completely stealing the whole tugen thing from me. Like I was just going hot to Tugen to find that reference. And now all I can do is add to it. So that is from at least one of those occasions, a number 16, and I just wanna read the verse. This is 16 six. So Moses and Aaron said to all the sons of Israel at evening, you will know that Yahweh has brought you outta the land of Egypt. And in the morning you will see the glory of Yahweh for he hears your grumblings against Yahweh. And what we are that you grumble against us. So I'm totally with you. This is not subtle. The workers first complaint here, the first workers' complaint is like theologically serious. Uh, I think that's what you're hitting us on. Like it charges the owner with injustice. Right. And as I read it, the grievance has like two layers or two parts, I would say. One is this comparative part, which is basically saying, you made us equal to them. Right? And the second be like a meritorious part, they have worked harder and in worse conditions. And that's why they say things like, it's, it's all inflammatory language, isn't it? Like the scorching heat emphasizes like the real bodily cost and their complaint. I think if we're honest, it's not irrational, but it's spiritually revealing at least because Right, they believe their greater effort, mayors greater reward and they resent that grace shown to others. So like you said, they're bringing forward a very serious grievance and it's, it's not just like, Hey, we think maybe could you give us a bonus? Right. But that is a matter of faithfulness. And in fact, like as I'm looking at this tugen here, shout out to logos Bible software. And I'm saying that that verb that we're talking about in Exodus 16 is in the imperfect tense. So this is, they kept on grumbling and it is like an an echo of Israel's murmuring in the wilderness, which I presume like Matthew certainly had intentionally used there or had that view in part casting these workers as the same types of those who relate to God through entitlement rather than gratitude. So it's like insults upon insult here, but it is to emphasize this fact that it's no small accusation, it's not subtle, it's meant to be in your face. They're coming in hot with this and they're making a big deal about it.  [00:43:16] Tony Arsenal: Yeah, and again, I think like underscoring the covenantal nature of this is so key. And I think, you know, when we look at this, we really have to land that this is not just saying. Your wage structure is not right. 'cause and, and we gotta remember, they weren't there when the master went and made this bargain, or, you know, brought these other workers into the vineyard. They weren't there to hear what covenant or contract he did or didn't make. And as we've commented, they didn't, he didn't even make a covenant with them. He basically just said, I'm gonna put you to work and I'll pay you what's fair. I'll pay you what's right. Um, and they went, okay, you need the work and thank you. Like, I think, I think that's kind of like the, the scene here is they're standing there. They recognize they're not gonna get a wage for the day, especially these ones that he's coming in at the 11th hour, they're not gonna get a wage for the day. And as you said, these are subsistence workers. Right. These are people that if you don't get a wage, and this is the, the grounding of the Old Testament, um, the Old Testament command of, of paying at the end of the day is that if they don't get their wage, they're not gonna eat. They're not gonna have food, they're not gonna have the money they need to survive. Um, so he comes in and he basically says like. You don't have a job that's not gonna be good for you. I'll take care of you. I'll, I'll give you a job and I'll take care of you. And the ones who are complaining and grumbling, they have no line of sight to that process. That, that's right. They make a lot of assumptions about the, and this is, goes back to, um. The parable of the talents, which we haven't really talked about yet. The, the, there's a lot of assumptions about the nature of this master that the, the contracted or covenanted day laborers are making that don't turn out to be accurate. Right. They, they assume that he's working, as you've said, that he's working on this one-to-one, you know, quid pro quo. You do this, I do that kind of a, a methodology and he's actually operating on a basis of a much more. Basic, uh, grace principle. Uh, and again, even, even the principle of hiring these original workers and covenanting with them is gracious in the sense that he didn't have to hire them. Right. So, so all along the way they're, they're, it's like the epitome of looking a gift horse in the mouth.  [00:45:24] Jesse Schwamb: Yes.  [00:45:24] Tony Arsenal: They've been hired, and so yes, it is right for them to expect their, um, to expect their wage, whatever that wage might be. But they, they are misinterpreting the idea of what the wages are and how the wages are to be delivered. They're, they're applying, this is actually a lot like job's, friends, right? Their, their logic is not actually all that bad, but they have, they have missing parts of the picture that makes the logic. Apply differently in this particular situation. They think that this, this master works on a strict merit-based. You do X amount of work, you receive X amount of money. And this master is actually more functioning on this covenantal principle of, I'm gonna pay you what's right, regardless of what, what work you've done, which, what work is actually owed to you. And the master makes these, this agreement with these other workers to just say, go into the vineyard and then when the evening comes, I'll pay you. Right. Well, he intended to pay them what they needed to survive, regardless of how much work they provided. Right? So they're all, even though there's a formal contract to say these, this group works for the whole day and this group, you know, and, and they receive one day's labor, at the end of the day, he's graciously providing another day of survival for all of these people, for the work that they're, they're putting forward regardless of how much they actually contribute to his bottom line. [00:46:41] Owner Defends The Covenant [00:46:41] Jesse Schwamb: And we see that in verse 13, where the landowner gives his defense, you know, it says. He and he replied, friends, I'm doing you no wrong. Did you not agree with me for Denarius? Now the address, because now I'm deep in the Greek Tony. Here we go. So the address I'm seeing in, uh, again, shout out to Locus Bible software, it, this use of friend is not like the warm fellows, but like a more formal or distance term of address. It's used elsewhere in Matthew. But I think the point here is that the owner's first line of defense is this contractual point, which you're saying. I have not wronged you. He's kept his agreement precisely. No injustice has been done. And that's crucial. The owner doesn't re appreciate justice. He actually fulfills it. He obligates himself and he fulfills that obligation. And what the worker receives is exactly what was promised and exactly what is due. And so by the time he gets to verse 14 where he says, take what belongs to you, and go, I choose to give to this last worker as I give to you here. I think this is like the theological beating hide of this whole bad boy. Yeah.  [00:47:37] Jesse Schwamb: The landowner explicitly invokes his will, his sovereign freedom to do and to give as he pleases, which is exactly how God behaves. It's not a negation of justice, but this declaration of something beyond justice, it is grace. He exercises his freedom and generosity to those who had no claim, and the command, take what belongs to you and go is, is kind of like a world dismissal, like, like you were saying. Yeah. We're in the courtroom. He's like, I, I've ruled on this already. Like, bring Brian, bring your grievance. Here's my ruling. Take what you have and go. Their grumbling has revealed that they're not celebrating the kingdom. They're actually grieving it. So yeah, you know, I think original invocation of like Jonah is right on the money. It's basically like, are are you mad enough? Yeah, I'm mad enough to die. Like, how dare you give me, give me this great shade and then take it away from me. Yeah. And in some ways this is even worse because what they have been given has been that were promised to them, was given to them, and they get to retain and God says, go, or the landowner as God says, go now and take what is yours. Take what I've given to you graciously. But your point that like what supersedes that, the antecedent to all of that is still God's covenant keeping, covenant making promise, making, right? That sets the whole thing up. But I love this idea that, you know, I will choose, it's my desire, it's language of divine volition. And of course the reform theology, this single verb resonates with the entire doctrine of election. It's God's free, sovereign, and gracious will to bestow blessing without reference to merit, like praise his name.  [00:49:00] Tony Arsenal: Yeah. Yeah. And then we come to kind of the close of this parable, right? And this is, this reall

An award-winning astrophysicist looks at how the understanding of uncertainty and randomness has led to breakthroughs in our knowledge of the cosmos All of us understand the world around us by constructing models, comparing them to observations, and drawing conclusions. Scientists create, test, and replace these models by applying the twinned concepts of probability and randomness. Exploring how this process has refined our knowledge of quantum mechanics and the birth of the universe, In The Random Universe: How Models and Probability Help Us Make Sense of the Cosmos (Yale UP, 2025) Andrew H. Jaffe offers a unique synthesis of the philosophy of epistemology, the mathematics of probability, and the science of cosmology. As Jaffe puts Enlightenment thinkers like David Hume in conversation with contemporary philosophers such as Karl Popper and Imre Lakatos and engages with scientists ranging from Isaac Newton and Galileo to Albert Einstein and Arthur Eddington, he uses Thomas Bayes's seminal studies of statistics and probability to make sense of conflicting currents of thought. This is a deep look into how we have learned to account for uncertainty in our search for knowledge--and a reminder that science is not about facts and data as such but about creating models that correctly account for those facts and data. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

An award-winning astrophysicist looks at how the understanding of uncertainty and randomness has led to breakthroughs in our knowledge of the cosmos All of us understand the world around us by constructing models, comparing them to observations, and drawing conclusions. Scientists create, test, and replace these models by applying the twinned concepts of probability and randomness. Exploring how this process has refined our knowledge of quantum mechanics and the birth of the universe, In The Random Universe: How Models and Probability Help Us Make Sense of the Cosmos (Yale UP, 2025) Andrew H. Jaffe offers a unique synthesis of the philosophy of epistemology, the mathematics of probability, and the science of cosmology. As Jaffe puts Enlightenment thinkers like David Hume in conversation with contemporary philosophers such as Karl Popper and Imre Lakatos and engages with scientists ranging from Isaac Newton and Galileo to Albert Einstein and Arthur Eddington, he uses Thomas Bayes's seminal studies of statistics and probability to make sense of conflicting currents of thought. This is a deep look into how we have learned to account for uncertainty in our search for knowledge--and a reminder that science is not about facts and data as such but about creating models that correctly account for those facts and data. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science

An award-winning astrophysicist looks at how the understanding of uncertainty and randomness has led to breakthroughs in our knowledge of the cosmos All of us understand the world around us by constructing models, comparing them to observations, and drawing conclusions. Scientists create, test, and replace these models by applying the twinned concepts of probability and randomness. Exploring how this process has refined our knowledge of quantum mechanics and the birth of the universe, In The Random Universe: How Models and Probability Help Us Make Sense of the Cosmos (Yale UP, 2025) Andrew H. Jaffe offers a unique synthesis of the philosophy of epistemology, the mathematics of probability, and the science of cosmology. As Jaffe puts Enlightenment thinkers like David Hume in conversation with contemporary philosophers such as Karl Popper and Imre Lakatos and engages with scientists ranging from Isaac Newton and Galileo to Albert Einstein and Arthur Eddington, he uses Thomas Bayes's seminal studies of statistics and probability to make sense of conflicting currents of thought. This is a deep look into how we have learned to account for uncertainty in our search for knowledge--and a reminder that science is not about facts and data as such but about creating models that correctly account for those facts and data. Learn more about your ad choices. Visit megaphone.fm/adchoices

You're frozen. The deadline's approaching. You don't have all the data. Everyone wants certainty. You can't give it. Sound familiar? Maybe it's a hiring decision with three qualified candidates and red flags on each one. Or a product launch where the market research is mixed. Or a career pivot where you can't predict which path leads where. You want more information. More time. More certainty. But you're not going to get it. Meanwhile, a small group of professionals—poker players, venture capitalists, military strategists—consistently make better decisions than the rest of us in exactly these situations. Not because they have more information, but because they've mastered something fundamentally different: they think in probabilities, not certainties. I learned this the hard way—I once created a biometric security algorithm that the NSA reverse-engineered, where I mastered probabilistic thinking perfectly in the technology, then made every wrong bet with the business around it. By the end of this episode, you'll possess a powerful mental toolkit that transforms how you approach uncertainty. You'll learn to estimate likelihoods without perfect data, update your beliefs as new information emerges, make confident decisions when multiple uncertain factors collide, and act decisively even when you can't guarantee the outcome. This is the difference between paralysis and power, between gambling recklessly and betting wisely. What Is Probabilistic Thinking? But what does probabilistic thinking actually entail? At its core, it's the practice of reasoning in terms of likelihoods rather than absolutes—thinking in percentages instead of yes-or-no answers. Instead of asking "Will this work?" you ask "What are the odds this will work, and what are the consequences if it doesn't?" This approach acknowledges that the future is uncertain and that every decision carries risk. By quantifying that uncertainty and weighing it against potential outcomes, you make smarter choices even when you can't eliminate the unknown. The Cost of Demanding Certainty Today's world punishes those who demand certainty before acting. Research from Oracle's 2023 Decision Dilemma study—which surveyed over 14,000 employees and business leaders across 17 countries—found that 86% feel overwhelmed by the amount of data available to them. Rather than clarity, all that information creates decision paralysis. And the paralysis has real consequences. When we can't be certain, we freeze. We endlessly research options, seeking that final piece of data that will guarantee success. We postpone critical decisions, waiting for perfect information that never arrives. Meanwhile, opportunities pass us by, problems grow worse, and competitors who are comfortable with uncertainty move forward. This demand for certainty doesn't just slow us down—it exhausts us. Decision fatigue sets in as we agonize over choices, draining our mental resources until we either make impulsive decisions or avoid deciding altogether. Neither outcome serves us well. What Certainty-Seeking Actually Costs You Here's what it looks like in real life: You're the VP of Marketing. Your CMO wants a decision on next quarter's campaign budget by Friday. You have three agencies to choose from, each with strengths and weaknesses. So you ask for more data. Customer focus groups. Competitive analysis. Agency references. By Wednesday you're drowning in spreadsheets and conflicting opinions. Friday arrives. You still can't be certain which choice is right, so you ask for an extension. Two weeks later, you finally pick one—not because you're confident, but because you're exhausted and the CMO is furious about the delay. The campaign launches late. You've burned political capital. And you still have no idea if you made the right choice. Meanwhile, your competitor's marketing VP looked at the same decision, spent two hours assessing the probabilities, and launched on time. If it works, great. If it doesn't, they'll pivot. They didn't need certainty. They needed enough information to make a good bet. That's the tax you pay for demanding certainty: missed timing, exhausted teams, and decisions made from fatigue rather than judgment. Meanwhile, a small group of professionals thrives in these exact conditions. Professional poker players like Annie Duke understand that good decisions sometimes lead to bad outcomes and bad decisions sometimes get lucky—so they judge their choices by process, not results. Venture capitalists often see that most of their investments will fail, but they bet anyway because one success out of twenty can return the entire fund. Military strategists make life-and-death decisions with incomplete intelligence, not because they're reckless, but because waiting for perfect information means defeat. The difference isn't access to better information. It's the willingness to act on probabilities rather than certainties. How To Make Better Decisions When Nothing Is Certain So how do you actually develop this skill? It's more accessible than you might think. Here are clear strategies to transform how you process uncertainty and make decisions. Think in Ranges, Not Points The first shift in probabilistic thinking is abandoning single-number estimates for ranges of possibility. When most people predict an outcome, they pick one number: "Sales will be $500,000 next quarter" or "This project will take three months." But the world doesn't work that way. Every estimate carries uncertainty, and pretending otherwise sets you up for failure. Professional forecasters think differently. They don't ask "What will happen?" They ask "What's the range of plausible outcomes, and how likely is each?" This approach forces you to acknowledge what you don't know while still making useful predictions. Watch a professional poker player deciding whether to call a bet. They're not thinking "Do I have the best hand?" They're thinking "Given what I've seen, maybe 35% chance I have the best hand, 20% chance my opponent is bluffing, 45% chance they've got me beat." They act on probabilities, not certainties. Steps to implement range thinking: Replace point estimates with probability ranges. When making any prediction, state a range instead of a single number. Instead of "We'll close 50 deals," say "We'll likely close 40-60 deals, with a small chance of 30-70."   Assign rough percentages to your ranges. You don't need mathematical precision—just honest self-assessment. Estimate: "60% chance of 40-50 deals, 30% chance of 50-60, 10% chance outside that range." This forces you to think about likelihood, not just possibility.   Track your estimates against actual outcomes. Keep a simple log of your predictions and what actually happened. Over time, you'll discover if you're consistently over-optimistic, over-cautious, or actually well-calibrated. This feedback loop is how you improve.   Update Your Beliefs with New Evidence One of the most powerful aspects of probabilistic thinking is treating your beliefs as hypotheses, not conclusions. When new information emerges, skilled thinkers update their probability estimates rather than clinging to their original position. This practice—called Bayesian updating after the mathematician Thomas Bayes—is how professionals stay accurate in changing environments. Consider a doctor diagnosing a patient with intermittent chest pain. Initially, based on the patient's age and health history, she estimates a 15% probability of heart disease. Then the EKG comes back with minor abnormalities—not definitive, but concerning. She updates her estimate to 35%. Blood work shows elevated cardiac markers. Now she's at 65%. Each piece of evidence shifts the probability, but none gives absolute certainty. She doesn't wait for 100% certainty to act—she orders more tests and starts precautionary treatment based on her updated 65% estimate. That's Bayesian thinking in action. Financial firms continuously adjust their models as new data arrives. Weather forecasters update storm predictions hourly. In my own work building biometric security systems, we updated our false acceptance and rejection rates constantly—but I failed to apply that same updating framework to the business model itself. The enemy of updating is confirmation bias—our tendency to accept information that supports our existing beliefs and dismiss information that contradicts them. When you're emotionally invested in being right, you'll unconsciously filter evidence to protect your original view. Steps to update your thinking: Start with a baseline probability before you have strong evidence. If you're launching a new product, estimate: "Based on what I know about similar products, there's maybe a 40% chance this succeeds." That's your prior—your starting point before specific evidence comes in.   When new information arrives, ask: "How much should this change my estimate?" Not all evidence is equal. Strong evidence—like actual customer purchases—should move your probability significantly. Weak evidence—like one person's opinion—should barely budge it.   Separate the quality of a decision from the quality of the outcome. This is crucial. A good decision based on sound probabilities can still result in a bad outcome due to chance. Conversely, a terrible decision can get lucky. Judge yourself on whether you correctly assessed the probabilities and acted accordingly, not on whether you "got it right" this time.   Actively seek disconfirming evidence. Force yourself to look for information that contradicts your current view. If you think your strategy will work, deliberately search for reasons it might fail. This counteracts confirmation bias and gives you a more accurate probability estimate.   Make Decisions by Expected Value Probabilistic thinking isn't just about estimating odds—it's about acting on them. The concept of expected value gives you a framework for making decisions when outcomes are uncertain. Expected value multiplies each possible outcome by its probability, then adds them together. It's how professionals decide whether a bet is worth taking. Here's why it matters: sometimes a decision with a low probability of success is still the right choice if the potential payoff is enormous. Venture capitalists know that perhaps 18 out of 20 startups in their portfolio will fail or return little money. But that one company that becomes the next Airbnb or Uber can return 100x their investment—more than covering all the losses. That's positive expected value thinking. Conversely, decisions that seem "safe" can be terrible bets. Playing it safe might give you a 90% chance of mediocre success, but if that 10% downside risk includes catastrophic consequences, the expected value might be negative. This is why you buy insurance: the probability of your house burning down is low, but the cost if it happens is devastating. Think about a parent choosing between schools for their child. Public school is free but overcrowded. Private school costs $20K/year with smaller classes but adds an hour of family stress daily. Charter school is free with innovative curriculum but it's a first-year program with unknowns. There's no guarantee. The better question is expected value: "Given the probabilities and what matters most to us—academic success, family time, financial stability—which bet has the best expected outcome?" Steps for expected value decision-making: List all plausible outcomes for your decision, not just the best and worst. For a job offer, don't just think "great career move" versus "terrible mistake." Consider: "Modest improvement (40%), breakthrough opportunity (20%), lateral move (25%), step backward (10%), complete disaster (5%)."   Assign a rough value to each outcome. This doesn't have to be money—it can be career satisfaction, life quality, time saved, or any currency that matters to you. The key is making the values comparable across outcomes.   Multiply each outcome's value by its probability, then add them up. This gives you the expected value. If the positive expected value option has meaningful downside risk, ask: "Can I survive the worst case?" If yes, it's usually the right bet.   Remember: expected value is about long-term results, not single instances. If you make a high expected value bet and it fails, that doesn't mean you were wrong. Over many decisions, following expected value will outperform any other approach. Trust the math, not the emotional reaction to one outcome.   Practice: The Probability Forecast Journal A practical way to develop your probabilistic thinking is to keep a Probability Forecast Journal. This exercise builds calibration—your ability to accurately assess how confident you should be in your predictions. Here's how to implement it: Choose three areas where you regularly make predictions. These could be work-related (project timelines, sales numbers), personal (will your flight be delayed), or current events (election outcomes).   Each week, make five specific, testable predictions. Write down the prediction and assign a probability. For example: "70% chance the client approves our proposal by Friday" or "85% chance our website traffic increases this month."   After each prediction resolves, record the actual outcome. Did the thing you said had a 70% chance of happening actually happen? Don't judge yourself harshly on any single prediction—remember that a 70% prediction should fail about 30% of the time.   Monthly, analyze your calibration. Look at all predictions where you said "70% confident"—did roughly 70% of them come true? If you're consistently overconfident, you need to adjust. If you're underconfident, you're being too cautious.   The goal isn't perfection—it's calibration. After several months of this practice, you'll notice your ability to assess probabilities improves dramatically. You'll know when you're 60% sure versus 90% sure, and you'll make better decisions as a result. The Rewards Mastering probabilistic thinking is a journey, not a destination. It requires practice, humility about what you don't know, and the courage to act despite uncertainty. But the rewards are substantial. When you think probabilistically, you make faster decisions because you're not paralyzed waiting for certainty that will never come. You become more resilient to failure because you understand that good decisions sometimes have bad outcomes—and that's not a reason to change your approach. You'll find yourself taking calculated risks that others avoid, capturing opportunities that demand action before perfect information arrives. You'll waste less time second-guessing yourself because you've already thought through the probabilities and made your peace with uncertainty. You'll explain your decisions more clearly to others because you can articulate not just what you think will happen, but how confident you are and why. Most importantly, you'll stop confusing confidence with correctness. In a world obsessed with appearing certain, probabilistic thinkers have the courage to say "I'm 65% sure, and that's enough to act." That honesty—with yourself and others—is the foundation of better judgment. Want to see what happens when you master probabilistic thinking in one domain but fail to apply it in another? I wrote about my experience creating a fingerprint recognition algorithm that the NSA reverse-engineered—where I got the technical probabilities right and the business bets completely wrong. [Read the full story here](link to substack). The future will always be uncertain. The question is whether you'll be paralyzed by that uncertainty or empowered by it.   If this helped you think differently about decision-making, I'd really appreciate it if you'd hit the like button and subscribe—it genuinely helps others find this content through the algorithm. And click that notification bell so you don't miss the next episode in this series. If you want to go deeper, I share the behind-the-scenes thinking, mistakes, and extended stories over on Studio Notes on Substack. Paid subscriptions help cover the costs of the team who makes all of this possible—the editing, research, and production work that gets these episodes to you each week. None of it comes to me; it all goes to supporting them. Without this team, there'd be no podcast, no YouTube channel, no articles. So if you find value in this work, that's a meaningful way to keep it going. The future will always be uncertain. The question is whether you'll be paralyzed by it or empowered by it.   Sources Cited In This Episode Oracle Decision Dilemma Study (2023) - Survey of 14,000+ employees and business leaders across 17 countries on data overwhelm and decision paralysis. https://www.oracle.com/uk/cloud/decision-dilemma/ Thinking in Bets - Duke, A. (2018). Portfolio. On judging decisions by process, not outcomes. https://www.penguinrandomhouse.com/books/552885/thinking-in-bets-by-annie-duke/ How to Improve Bayesian Reasoning Without Instruction: Frequency Formats - Gigerenzer, G. & Hoffrage, U. (1995). Psychological Review, 102(4), 684-704. On updating beliefs with evidence. Prospect Theory: An Analysis of Decision under Risk - Kahneman, D. & Tversky, A. (1979). Econometrica, 47(2), 263-291. Prospect Theory foundations.

Paul Duan est devenu en une décennie l'un des visages les plus emblématiques du mouvement Tech for Good. Franco-chinois, formé à la fois aux mathématiques et à Sciences Po Paris, il part très jeune à l'université de Berkeley, où il découvre l'ébullition de la data science naissante. À 19 ans, il devient le premier data scientist de la plateforme Eventbrite et vit l'hypercroissance de la Silicon Valley. Mais il ressent vite un manque de sens : pourquoi consacrer son énergie à optimiser des algorithmes publicitaires alors que, dans les rues de San Francisco, la misère côtoie les fortunes de la tech ?En 2014, il fonde Bayes Impact, une ONG inspirée par le théorème de probabilité de Thomas Bayes et portée par une conviction : les technologies les plus avancées doivent servir le bien commun. Lauréat de Y Combinator, soutenu par Sam Altman, Duan fait de Bayes Impact une vitrine mondiale du numérique au service de l'action sociale. L'organisation développe notamment des outils pour Pôle emploi, pour la gestion des cas contacts pendant le Covid-19 ou encore pour la lutte contre la violence policière en Californie.Aujourd'hui, Bayes Impact concentre ses forces sur Case AI, un copilote open source d'intelligence artificielle destiné aux travailleurs sociaux. Son objectif : alléger la charge mentale et administrative des professionnels de terrain afin qu'ils consacrent davantage de temps à l'accompagnement humain. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

This week we learn how to make more money gambling from the lessons of "The Reverend" Thomas Bayes.His theorem is the backbone of every successful sports bettor (even if they don't know it).This week we walk through examples of priors, posteriors and general Bayesian betting hygiene. It's more electric than it sounds! Andrew Mack's Book: Amazon0:00 Bayesian Thinking Intro10:05 Bayes in Sports Betting51:23 News1:07:30 SP v. DK Pick61:18:45 Q&AWelcome to The Risk Takers Podcast, hosted by professional sports bettor John Shilling (GoldenPants13) and SportsProjections. This podcast is the best betting education available - PERIOD. And it's free - please share and subscribe if you like it.My website: https://www.goldenpants.com/ Follow SportsProjections on Twitter: https://x.com/Sports__ProjWant to work with my betting group?: john@goldenpants.comWant 100s of +EV picks a day?: https://www.goldenpants.com/gp-picks

Today's revolutionary idea is something a bit different: David talks to statistician David Spiegelhalter about how an eighteenth-century theory of probability emerged from relative obscurity in the twentieth century to reconfigure our understanding of the relationship between past, present and future. What was Thomas Bayes's original idea about doing probability in reverse: from effect to cause? What happened when this way of thinking passed through the vortex of the French Revolution? How has it come to lie behind recent innovations in political polling, AI, self-driving cars, medical research and so much more? Why does it remain controversial to this day? The latest edition of our free fortnightly newsletter is available: to get it in your inbox sign up now https://www.ppfideas.com/newsletter  Next time: 1848: The Liberal Revolution w/Chris Clark Past Present Future is part of the Airwave Podcast Network Learn more about your ad choices. Visit megaphone.fm/adchoices

Everything Is Predictable: How Bayes' Remarkable Theorem Explains the World is a book about an 18th century mathematical rule for working out probability, which shapes many aspects of our modern world. Written by science journalist Tom Chivers, the book has made it onto the shortlist for the Royal Society Trivedi Science Book Prize. In the lead up to the winner's announcement, New Scientist books editor Alison Flood meets all six of the shortlisted authors.In this conversation, Tom explores the life of Thomas Bayes, the man behind the theorem, and how he had no clue his discovery would have such sweeping implications for humanity. He explains the theorem's many uses, both in practical settings like disease diagnosis, as well as its ability to explain rational thought and the human brain. And he digs into some of the controversy and surprising conflict that has surrounded Bayes' theorem over the years.The winner of the Royal Society Trivedi Science Book Prize will be announced on the 24th October. You can view all of the shortlisted entries here:https://royalsociety.org/medals-and-prizes/science-book-prize/ To read about subjects like this and much more, visit https://www.newscientist.com/ Hosted on Acast. See acast.com/privacy for more information.

Tom Chivers is a journalist who writes a lot about science and applied statistics. We talk about his new book on Bayesian statistics, the biography of Thomas Bayes, the history of probability theory, how Bayes can help with the replication crisis, how Tom became a journalist, and much more.BJKS Podcast is a podcast about neuroscience, psychology, and anything vaguely related, hosted by Benjamin James Kuper-Smith.Support the show: https://geni.us/bjks-patreonTimestamps0:00:00: Tom's book about Bayes & Bayesian statistics relates to many of my previous episodes and much of my own research0:03:12: A brief biography of Thomas Bayes (about whom very little is known)0:11:00: The history of probability theory 0:36:23: Bayesian songs0:43:17: Bayes & the replication crisis0:57:27: How Tom got into science journalism1:08:32: A book or paper more people should read1:10:05: Something Tom wishes he'd learnt sooner1:14:36: Advice for PhD students/postdocs/people in a transition periodPodcast linksWebsite: https://geni.us/bjks-podTwitter: https://geni.us/bjks-pod-twtTom's linksWebsite: https://geni.us/chivers-webTwitter: https://geni.us/chivers-twtPodcast: https://geni.us/chivers-podBen's linksWebsite: https://geni.us/bjks-webGoogle Scholar: https://geni.us/bjks-scholarTwitter: https://geni.us/bjks-twtReferences and linksEpisode with Stuart Ritchie: https://geni.us/bjks-ritchieScott Alexander: https://www.astralcodexten.com/Bayes (1731). Divine benevolence, or an attempt to prove that the principal end of the divine providence and government is the happiness of his creatures. Being an answer to a pamphlet entitled Divine Rectitude or an inquiry concerning the moral perfections of the deity with a refutation of the notions therein advanced concerning beauty and order, the reason of punishment and the necessity of a state of trial antecedent to perfect happiness.Bayes (1763). An essay towards solving a problem in the doctrine of chances. Philosophical transactions of the Royal Society of London.Bellhouse (2004). The Reverend Thomas Bayes, FRS: a biography to celebrate the tercentenary of his birth. Project Euclid.Bem (2011). Feeling the future: experimental evidence for anomalous retroactive influences on cognition and affect. Journal of personality and social psychology.Chivers (2024). Everything is Predictable: How Bayesian Statistics Explain Our World.Chivers & Chivers (2021). How to read numbers: A guide to statistics in the news (and knowing when to trust them).Chivers (2019). The Rationalist's Guide to the Galaxy: Superintelligent AI and the Geeks Who Are Trying to Save Humanity's Future.Clarke [not Black, as Tom said] (2020). Piranesi.Goldacre (2009). Bad science.Goldacre (2014). Bad pharma: how drug companies mislead doctors and harm patients.Simmons, Nelson & Simonsohn (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science.

Today our guest Ivan Phillips methodically explains what Bayesianism is and is not. Along the way we discuss the validity of critiques made by critical rationalists of the worldview that is derived from Thomas Bayes's 1763 theorem. Ivan is a Bayesian that is very familiar with Karl Popper's writings and even admires Popper's epistemology. Ivan makes his case that Bayesian epistemology is the correct way to reason and that Karl Popper misunderstood some aspects of how to properly apply probability theory to reasoning and inference. (Due in part to those theories being less well developed back in Popper's time.) This is a video podcast if you watch it on Spotify. But it should be consumable as just audio. But I found Ivan's slides quite useful. This is by far the best explanations for Bayesianism that I've ever seen and it does a great job of situating it in a way that makes sense to a critical rationalist like myself. But it still didn't convince me to be a Bayesian. ;) --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support

Back in the 1700s, in a spa town outside of London, Thomas Bayes, a Presbyterian minister and amateur mathematician, invented a formula that lets you figure out how likely something is to happen based on what you already know. It changed the world. Today, pollsters use it to forecast election results and bookies to predict Super Bowl scores. For neuroscientists, it explains how our brains work; for computer scientists, it's the principle behind artificial intelligence. In this episode, we explore the modern-day applications of this game-changing theorem with the help of Tom Chivers, author of the new book "Everything Is Predictable: How Bayesian Statistics Explain Our World."

In this episode, Xavier Bonilla has a dialogue with Tom Chivers about Bayesian probability and the impact Bayesian priors have on ourselves. They define Bayesian priors, Thomas Bayes, subjective aspects of Bayes theorem, and the problematic elements of statistical figures such as Galton, Pearson, and Fisher. They talk about the replication crisis, p-hacking, where priors come from, AI, Friston's free energy principle, and Bayesian priors in our world today. Tom Chivers is a science writer. He does freelance science writing and also writes for Semafor.com's daily Flagship email. Before joining Semafor, he was a science editor at UnHerd, science writer for BuzzFeed UK, and features writer for the Telegraph. He is the author of several books including the most recent, Everything Is Predictable: How Bayesian Statistics Explain Our World. Website: https://tomchivers.com/ Get full access to Converging Dialogues at convergingdialogues.substack.com/subscribe

Dr. Arturo Casadevall from Johns Hopkins School of Public Health talks about a potential fungal epidemic in his new book, "What if Fungi Win?"Then, what if there was one overarching theory that could help explain much of our modern-day daily lives? Science journalist Tom Chivers explores the concept of the predictability of everything, based on a theorem developed by Thomas Bayes, an 18th-century Presbyterian minister and statistician.

At its simplest, Bayes's theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. But in Everything Is Predictable, Tom Chivers lays out how it affects every aspect of our lives. He explains why highly accurate screening tests can lead to false positives and how a failure to account for it in court has put innocent people in jail. A cornerstone of rational thought, many argue that Bayes's theorem is a description of almost everything. But who was the man who lent his name to this theorem? How did an 18th-century Presbyterian minister and amateur mathematician uncover a theorem that would affect fields as diverse as medicine, law, and artificial intelligence? Fusing biography and intellectual history, Everything Is Predictable is an entertaining tour of Bayes's theorem and its impact on modern life, showing how a single compelling idea can have far reaching consequences. Tom Chivers is an author and the award-winning science writer for Semafor. Previously he was the science editor at UnHerd.com and BuzzFeed UK. His writing has appeared in The Times (London), The Guardian, New Scientist, Wired, CNN, and more. He was awarded the Royal Statistical Society's “Statistical Excellence in Journalism” awards in 2018 and 2020, and was declared the science writer of the year by the Association of British Science Writers in 2021. His books include The Rationalist's Guide to the Galaxy: Superintelligent AI and the Geeks Who Are Trying to Save Humanity's Future, and How to Read Numbers: A Guide to Stats in the News (and Knowing When to Trust Them). His new book is Everything Is Predictable: How Bayesian Statistics Explain Our World. Shermer and Chivers discuss: Thomas Bayes, his equation, and the problem it solves • Bayesian decision theory vs. statistical decision theory • Popperian falsification vs. Bayesian estimation • Sagan's ECREE principle • Bayesian epistemology and family resemblance • paradox of the heap • Reality as controlled hallucination • human irrationality • superforecasting • mystical experiences and religious truths • Replication Crisis in science • Statistical Detection Theory and Signal Detection Theory • Medical diagnosis problem and why most people get it wrong.

Neste episódio semanal, falamos um pouco sobre a vida de Thomas Bayes e suas contribuições para a estatística

The HBS hosts chat with Justin Joque about how we might get Thomas Bayes' robot boot off our necks. Why does Netflix ask you to pick what movies you like when you first sign on in order to recommend other movies and shows to you? How does Google know what search results are most relevant? Why does it seem as if every tech company wants to collect as much data as they can get from you? It turns out that all of this is because of a shift in the theoretical and mathematical approach to probability. Bayesian statistics, the primary model used by machine learning systems, currently dominates almost everything about our lives: investing, sales at stores, political predictions, and, increasingly, what we think we know about the world. How did the "Bayesian revolution" come about? And how did come to dominate? And, perhaps more importantly, is this the best mathematical/statistical model available to us? Or is there another, more "revolutionary," mathematics out there?This week we are joined by Justin Joque, visualization librarian at University of Michigan who writes at the intersection of philosophy and technology. He is the author Deconstruction Machines: Writing in the Age of Cyberwar and, most recently, Revolutionary Mathematics: Artificial Intelligence, Statistics and the Logic of Capitalism.Full episode notes available at this link:http://hotelbarpodcast.com/podcast/episode-78-revolutionary-mathematics-with-justin-joque-------------------If you enjoy Hotel Bar Sessions podcast, please be sure to subscribe and submit a rating/review! Follow us on Twitter @hotelbarpodcast, on Facebook, and subscribe to our YouTube channel!You can also help keep this podcast going by supporting us financially at patreon.com/hotelbarsessions. 

Las matemáticas siempre han sido consideradas como una materia ardua. Muchos incluso la detestan y se preguntan para qué estudiar temas como la aritmética, las integrales y derivadas. ¡Qué osada es la ignorancia! Probablemente conocer el origen de muchos principios matemáticos te haga admirar esta ciencia, como por ejemplo el origen del cálculo de probabilidades, inspirado en matemáticos a quienes el juego y las apuestas les apasionaba. Los orígenes del cálculo de la probabilidad se remontan a principios del siglo XVII con los trabajos de matemáticos y filósofos como Blaise Pascal y Pierre de Fermat. Estos dos pensadores franceses realizaron importantes contribuciones al estudio de los juegos de azar y sentaron las bases para el desarrollo del campo de la teoría de la probabilidad. En 1654, Pascal y Fermat intercambiaron una serie de cartas conocidas como la "correspondencia sobre los juegos de azar". En estas cartas, discutieron problemas relacionados con apuestas y juegos de azar, y plantearon preguntas sobre cómo dividir los premios cuando un juego se interrumpe antes de que se obtenga una victoria clara. A través de su correspondencia, Pascal y Fermat desarrollaron métodos para calcular las probabilidades y determinar las expectativas matemáticas en juegos de azar. En particular, Fermat desarrolló el concepto de expectativa matemática, que es la cantidad esperada de ganancia o pérdida en un juego de azar. También formuló el principio fundamental de la teoría de la probabilidad, conocido como el principio de la equiprobabilidad, que establece que si todos los resultados posibles de un experimento son igualmente probables, entonces la probabilidad de un evento es la razón entre el número de resultados favorables y el número total de resultados posibles. Estas ideas pioneras sentaron las bases para el desarrollo posterior de la teoría de la probabilidad. A lo largo de los siglos siguientes, matemáticos como Jacob Bernoulli, Thomas Bayes y Pierre-Simon Laplace, entre otros, realizaron importantes avances en el campo, desarrollando métodos y teoremas más sofisticados para calcular y entender las probabilidades. En el siglo XX, el campo de la teoría de la probabilidad se consolidó como una rama matemática formal con el trabajo de matemáticos como Andréi Kolmogórov, quien estableció los fundamentos axiomáticos de la teoría de la probabilidad moderna. Hoy en día, la teoría de la probabilidad se aplica en una amplia gama de disciplinas, incluyendo estadísticas, economía, física, ciencias sociales, ingeniería y muchas otras áreas en las que se deben tomar decisiones basadas en la incertidumbre y la aleatoriedad. Publicado en luisbermejo.com en el enlace directo: https://luisbermejo.com/anticitera-con-nombre-de-podcast-04x48/ Puedes encontrarme y comentar o enviar tu mensaje o preguntar en: WhatsApp: +34 613031122 Paypal: https://paypal.me/Bermejo Bizum: +34613031122 Web: https://luisbermejo.com Facebook: https://www.facebook.com/ConNombredePodcast/ Twitter: https://twitter.com/LuisBermejo Instagram: https://www.instagram.com/luisbermejo/ Canal Telegram: https://t.me/ConNombredePodcast Grupo Signal: https://signal.group/#CjQKIA_PNdKc3-SAGWKoJZjqR3RwMQ7uzo0bW2eBB4QDtJVZEhBc504fpeK4tyETyuwFVAUI Grupo Whatsapp: https://chat.whatsapp.com/FQadHkgRn00BzSbZzhNviT

Wat?! Is er meer dan een soort statistiek? Inderdaad, de statistiek kent verschillende scholen. Een van die scholen is Bayesiaanse statistiek, vernoemd naar Thomas Bayes, de bedenker van de regel van Bayes. In deze aflevering leg ik uit wat Bayesiaanse statistiek is, in de volgende aflevering gaan we zien hoe deze verschilt van de oude vertrouwde ‘frequentistische' statistiek. Thomas Bayes had een goede vriend, dat leren we ook.

The HBS hosts chat with Justin Joque about how we might get Thomas Bayes' robot boot off our necks. Why does Netflix ask you to pick what movies you like when you first sign on in order to recommend other movies and shows to you? How does Google know what search results are most relevant? Why does it seem as if every tech company wants to collect as much data as they can get from you? It turns out that all of this is because of a shift in the theoretical and mathematical approach to probability. Bayesian statistics, the primary model used by machine learning systems, currently dominates almost everything about our lives: investing, sales at stores, political predictions, and, increasingly, what we think we know about the world. How did the "Bayesian revolution" come about? And how did come to dominate? And, perhaps more importantly, is this the best mathematical/statistical model available to us? Or is there another, more "revolutionary," mathematics out there?This week we are joined by Justin Joque, visualization librarian at University of Michigan who writes at the intersection of philosophy and technology. He is the author Deconstruction Machines: Writing in the Age of Cyberwar and, most recently, Revolutionary Mathematics: Artificial Intelligence, Statistics and the Logic of Capitalism.Full episode notes available at this link:http://hotelbarpodcast.com/podcast/episode-78-revolutionary-mathematics-with-justin-joque -------------------If you enjoy Hotel Bar Sessions podcast, please be sure to subscribe and submit a rating/review! Follow us on Twitter @hotelbarpodcast, on Facebook, and subscribe to our YouTube channel!You can also help keep this podcast going by supporting us financially at patreon.com/hotelbarsessions. 

Veteran Security Practitioner Rick Howard shares how Alan Turing's ideas and Thomas Bayes' Theorem hold the key to how organizations should forecast risk. Most organizations default to heat maps relying on a low, medium, and high model. But they aren't reliable. What if we said you're better off providing risk metrics, that offer ballpark answers and not so much precision? Is it possible to forecast complex things without a lot of data? 

In probability theory and statistics, Bayes' theorem, named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

A lo largo del programa, y en clave de tertulia, hablamos sobre Thomas Bayes, una de las figuras más enigmáticas de la Historia de la Ciencia. Comenzamos hablando sobre lo poco que se conoce de su vida, de los diferentes enigmas que rodean su existencia, para centrarnos posteriormente en el teorema que lleva su nombre. Un teorema que estuvo olvidado durante mucho tiempo, pero que en la actualidad es una de las herramientas más potentes para el estudio de las diferentes disciplinas científicas. Un teorema además que va mucho más allá de calcular una probabilidad, ya que tras él se esconde la verdadera esencia del aprendizaje humano. Analizamos también cómo se estudia el teorema en la actualidad y cómo podría mejorarse su didáctica. Todo ello de la manos de Anabel Forte, Pablo Beltrán y Víctor Marco.

A lo largo del programa, y en clave de tertulia, hablamos sobre Thomas Bayes, una de las figuras más enigmáticas de la Historia de la Ciencia. Comenzamos hablando sobre lo poco que se conoce de su vida, de los diferentes enigmas que rodean su existencia, para centrarnos posteriormente en el teorema que lleva su nombre. Un teorema que estuvo olvidado durante mucho tiempo, pero que en la actualidad es una de las herramientas más potentes para el estudio de las diferentes disciplinas científicas. Un teorema además que va mucho más allá de calcular una probabilidad, ya que tras él se esconde la verdadera esencia del aprendizaje humano. Analizamos también cómo se estudia el teorema en la actualidad y cómo podría mejorarse su didáctica. Todo ello de la manos de Anabel Forte, Pablo Beltrán y Víctor Marco. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Bayes’ Theorem Applying It To Emergency Management Mental models help us with making decisions under stress. They give us a starting point, think of how we teach triage, “start where you stand”. This applies to decision-making as well during a disaster or crisis, start with information that you have. We can make the adjustments as more or better information is obtained.  This brings me to the concepts of Bayes’ Theorem.  Thomas Bayes was an English minister in the 18th century, whose most famous work, “An Essay toward Solving a Problem in the Doctrine of Chances,” The essay did not contain the theorem as we now know it but had the seeds of the idea. It looked at how to adjust our estimates of probabilities when encountering new data that influence a situation. Later development by French scholar Pierre-Simon Laplace and others helped codify the theorem and develop it into a useful tool for thinking.Now you do not need to be great at math to use this concept. I still need to take off my shoes to count to 19. . More critical is your ability and desire to assign probabilities of truth and accuracy to anything you think you know and then be willing to update those probabilities when new information comes in.We talk about making decisions based on the new information that has come in, however, we often ignore prior information, simply called “priors” in Bayesian-speak. We can blame this habit in part on the availability heuristic—we focus on what’s readily available. In this case, we focus on the newest information, and the bigger picture gets lost. We fail to adjust the probability of old information to reflect what we have learned.The big idea behind Bayes’ theorem is that we must continuously update our probability estimates on an as-needed basis. Let’s take a look at a hurricane as our crisis. We have all seen the way it tracks and can predict that it may make landfall at a certain time and location. We can use past storms as predictors of how this hurricane may act and the damage it could cause. However, new information may come to light on the behavior of the storm. This however should not necessarily negate the previous experience and information you have on hand. In their book The Signal and the Noise, Nate Silver and Allen Lane give a contemporary example, reminding us that new information is often most useful when we put it in the larger context of what we already know:Bayes’ theorem is an important reality check on our efforts to forecast the future. How, for instance, should we reconcile a large body of theory and evidence predicting global warming with the fact that there has been no warming trend over the last decade or so? Skeptics react with glee, while true believers dismiss the new information.A better response is to use Bayes’ theorem: the lack of recent warming is evidence against recent global warming predictions, but it is weak evidence. This is because there is enough variability in global temperatures to make such an outcome unsurprising. The new information should reduce our confidence in our models of global warming—but only a little.The same approach can be used in anything from an economic forecast to a hand of poker, and while Bayes’ theorem can be a formal affair, Bayesian reasoning also works as a rule of thumb. We tend to either dismiss new evidence or embrace it as though nothing else matters. Bayesians try to weigh both the old hypothesis and the new evidence in a sensible way.So much of making better decisions hinges on dealing with uncertainty. The most common thing holding people back from the right answer is instinctively rejecting new information, or not integrating the old.  To better serve our communities, have a mental model, work with it and use it to make better decisions. PodcastsThe Todd De Voe Show School Shootings and Emergency Management  The K-12 School Shooting Database research project is a widely inclusive database that documents each and every instance a gun is brandished is fired, or a bullet hits school property for any reason, regardless of the number of victims, time, or day of the week.The School Shooting Database Project is conducted as part of the Advanced Thinking in Homeland Security (HSx) program at the Naval Postgraduate School’s Center for Homeland Defense and Security (CHDS).Prepare Respond Recover Saving Lives Through Training Due to the uptick of mass shootings over the years, many professions outside of law enforcement are now being trained in active shooter response programs. But have you ever thought about who teaches the law enforcement officers themselves? Join prepare.respond.recover. host Todd De Voe as he talks with Erik Franco, the CEO of "High Speed Tac Med", one of the nation’s most sought-after active shooter training programs for law enforcement and firefighting. Learn about “Run, Hide, Fight” and how this training is preparing law enforcement officers to tackle an active shooter situation as quickly and efficiently as possible.HSTM - https://highspeedtacmed.com/If you would like to learn more about the Natural Disaster & Emergency Management (NDEM) Expo please visit us on the web - https://www.ndemevent.comBusiness Continuity Today Training for Active Shooters Beyond The Response Active shooting scenarios focus on the police response, and the larger emergency management role during these complex incidents is often overlooked. However, they are multi-week, multi-jurisdictional incidents requiring command & control, interoperable communications, and a host of other services. Supportershttps://www.disastertech.com/https://titanhst.com/https://www.ndemevent.com/en-us/show-info.html Get full access to The Emergency Management Network at emnetwork.substack.com/subscribe

Este é o Variância, um Spin-off do podcast Intervalo de Confiança, com periodicidade mensal. Este programa é mais curto e tem por objetivo trazer notícias ou curiosidades sobre algum assunto relacionado à ciência e jornalismo de dados ou sobre algum dado específico. Por ser mais curto, tanto a edição e conteúdo são mais simples e mais diretos.No episódio de hoje, Igor Alcantara fala de uma estatística diferente da "tradicional", onde a cada novo conhecimento adicionado, a probabilidade de um evento acontecer é atualizada.  Você se lembra do jogo da "Porta dos Desesperados" sucesso dos anos 1990 apresentado pela eterna criança Sérgio Malandro? Se fosse você naquele jogo, qual eria sua decisão, mudaria de porta ou continuaria na mesma? Será que isso faz alguma diferença?E que tal um problema de Fórmula 1? Em uma corrida hipotética, quais as chances de Lewis Hamilton vencer Max Verstappen? E se a corrida for na chuva? E se for na chuva em um circuito de rua? E se entrar um Safety Car na penúltima volta?Esses tipos de problema não são facilmente resolvidos com a estatística clássica, mas o legado de Thomas Bayes nos deixou uma ferramenta incrível para uma série de problemas do dia-a-dia. Escute esse episódio e tenha mais dados para decidir o mais importante. Você é #TeamFrequentista ou #Team Bayesiano?A Pauta foi escrita por  Igor Alcantara. A edição foi feita por Leo Oliveira e a vitrine do episódio por Júlia Frois. A coordenação de redação é de Tatiane do Vale e a gerência de projetos de Kézia Nogueira. de As vinhetas de todos os episódios foram compostas por Rafael Chino e Leo Oliveira.  Visite nosso site em http://intervalodeconfianca.com.br/

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: 12 interesting things I learned studying the discovery of nature's laws, published by Ben Pace on February 19, 2022 on LessWrong. I've been thinking about out whether I can discover laws of agency and wield them to prevent AI ruin (perhaps by building an AGI myself in a different paradigm than machine learning). So far I've looked into the history of the discovery of physical laws (gravity in particular) and mathematical laws (probability theory in particular). Here are 12 things I've learned or been surprised by. 1. Data-gathering was a crucial step in discovering both gravity and probability theory. One rich dude had a whole island and set it up to have lenses on lots of parts of it, and for like a year he'd go around each day and note down the positions of the stars. Then this data was worked on by others who turned it into equations of motion. 2. Relatedly, looking at the celestial bodies was a big deal. It was almost the whole game in gravity, but also a little helpful for probability theory (specifically the normal distribution was developed in part by noting that systematic errors in celestial measuring equipment followed a simple distribution). It hadn't struck me before, but putting a ton of geometry problems on the ceiling for the entire civilization led a lot of people to try to answer questions about it. (It makes Eliezer's choice in That Alien Message apt.) I'm tempted in a munchkin way to find other ways to do this, like to write a math problem on the surface of the moon, or petition Google to put a prediction market on its home page, or something more elegant than those two. 3. Probability theory was substantially developed around real-world problems! I thought math was all magical and ivory tower, but it was much more grounded than I expected. After a few small things like accounting and insurance and doing permutations of the alphabet, games of chance (gambling) was what really kicked it off, with Fermat and Pascal trying to figure out the expected value of games (they didn't phrase it like that, they put it more like “if the game has to stop before it's concluded, how should the winnings be split between the players?“). Other people who consulted with gamblers also would write down data about things like how often different winning hands would come up in different games, and discovered simple distributions, then tried to put equations to them. Later it was developed further by people trying to reason about gases and temperatures, and then again in understanding clinical trials or large repeated biological experiments. Often people discovered more in this combination of “looking directly at nature” and “being the sort of person who was interested in developing a formal calculus to model what was going on”. 4. Thought experiments about the world were a big deal too! Thomas Bayes did most of his math this way. He had a thought experiment that went something like this: his assistant would throw a ball on a table that Thomas wasn't looking at. Then his assistant would throw more balls on the table, each time saying whether it ended up to the right or the left of the original ball. He had this sense that each time he was told the next left-or-right, he should be able to give a new probability that the ball was in any particular given region. He used this thought experiment a lot when coming up with Bayes' theorem. 5. Lots of people involved were full-time inventors, rich people who did serious study into a lot of different areas, including mathematics. This is a weird class to me. (I don't know people like this today. And most scientific things are very institutionalized, or failing that, embedded within business.) Here's a quote I enjoyed from one of Pascal's letters to Fermat when they founded the theory of probability. (For context: de Mere was the gam...

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: 12 interesting things I learned studying the discovery of nature's laws, published by Ben Pace on February 19, 2022 on LessWrong. I've been thinking about out whether I can discover laws of agency and wield them to prevent AI ruin (perhaps by building an AGI myself in a different paradigm than machine learning). So far I've looked into the history of the discovery of physical laws (gravity in particular) and mathematical laws (probability theory in particular). Here are 12 things I've learned or been surprised by. 1. Data-gathering was a crucial step in discovering both gravity and probability theory. One rich dude had a whole island and set it up to have lenses on lots of parts of it, and for like a year he'd go around each day and note down the positions of the stars. Then this data was worked on by others who turned it into equations of motion. 2. Relatedly, looking at the celestial bodies was a big deal. It was almost the whole game in gravity, but also a little helpful for probability theory (specifically the normal distribution was developed in part by noting that systematic errors in celestial measuring equipment followed a simple distribution). It hadn't struck me before, but putting a ton of geometry problems on the ceiling for the entire civilization led a lot of people to try to answer questions about it. (It makes Eliezer's choice in That Alien Message apt.) I'm tempted in a munchkin way to find other ways to do this, like to write a math problem on the surface of the moon, or petition Google to put a prediction market on its home page, or something more elegant than those two. 3. Probability theory was substantially developed around real-world problems! I thought math was all magical and ivory tower, but it was much more grounded than I expected. After a few small things like accounting and insurance and doing permutations of the alphabet, games of chance (gambling) was what really kicked it off, with Fermat and Pascal trying to figure out the expected value of games (they didn't phrase it like that, they put it more like “if the game has to stop before it's concluded, how should the winnings be split between the players?“). Other people who consulted with gamblers also would write down data about things like how often different winning hands would come up in different games, and discovered simple distributions, then tried to put equations to them. Later it was developed further by people trying to reason about gases and temperatures, and then again in understanding clinical trials or large repeated biological experiments. Often people discovered more in this combination of “looking directly at nature” and “being the sort of person who was interested in developing a formal calculus to model what was going on”. 4. Thought experiments about the world were a big deal too! Thomas Bayes did most of his math this way. He had a thought experiment that went something like this: his assistant would throw a ball on a table that Thomas wasn't looking at. Then his assistant would throw more balls on the table, each time saying whether it ended up to the right or the left of the original ball. He had this sense that each time he was told the next left-or-right, he should be able to give a new probability that the ball was in any particular given region. He used this thought experiment a lot when coming up with Bayes' theorem. 5. Lots of people involved were full-time inventors, rich people who did serious study into a lot of different areas, including mathematics. This is a weird class to me. (I don't know people like this today. And most scientific things are very institutionalized, or failing that, embedded within business.) Here's a quote I enjoyed from one of Pascal's letters to Fermat when they founded the theory of probability. (For context: de Mere was the gam...

Thomas Bayes formuló la teoría de la probabilidad condicionada, la probabilidad que ocurra A sabiendo que estamos en B. ¿Cuál es la probabilidad de morir por Covid de un anciano con ligero sobrepeso? Alarmantemente alta. ¿Cuál es la probabilidad de morir por Covid de un joven sin patologías? Prácticamente nula. Te vacunas entonces para proteger a un tercero, familiar o desconocido. La vacunación es un debate emocional y, a pesar del riesgo de cancelación, con Nacho racionalizamos la decisión.Escucha el podcast en tu plataforma habitual:Spotify — Apple — iVoox — YouTubeArtículos sobre finanzas en formato blog:Substack Kapital — Substack CardinalApuntes:Filosofía, corporalidad y percepción. Maurice Merleau-Ponty.Piel negra, máscaras blancas. Frantz Fanon.Despertares. Oliver Sacks.Pandemia. Slavoj Žižek.Jugarse la piel. Nassim Nicholas Taleb.Índice:0.28. Conflicto de intereses en el debate público del Covid.12.49. Merleau-Ponty: La consciencia se genera en el cuerpo.27.05. Mínimo común múltiple o máximo común divisor.37.57. Nassim Nicholas Taleb y los riesgos de cola.1.10.22. La vacuna podría estar retrasando la inmunidad de rebaño.1.18.08. Externalidades, los efectos no incorporados en el precio.1.25.05. ¿Debo vacunar a mi hijo de 5 años?1.36.10. Fiscalizar a las grandes multinacionales.2.19.09. La lógica de Djokovic: my body, my choice.

What makes a satisfying explanation? Understanding and prediction are two different goals at odds with one another — think fundamental physics versus artificial neural networks — and even what defines a “simple” explanation varies from one person to another. Held in a kind of ecosystemic balance, these diverse approaches to seeking knowledge keep each other honest…but the use of one kind of knowledge to the exclusion of all others leads to disastrous results. And in the 21st Century, the difference between good and bad explanations determines how society adapts as rapid change transforms the world most people took for granted — and sends humankind into the epistemic wilds  to find new stories that will help us navigate this brave new world.This week we dive deep with SFI External Professor Simon DeDeo at Carnegie Mellon University to explore his research into intelligence and the search for understanding, bringing computational techniques to bear on the history of science, information processing at the scale of society, and how digital technologies and the coronavirus pandemic challenge humankind to think more carefully about the meaning that we seek, here on the edge of chaos…If you value our research and communication efforts, please subscribe to Complexity Podcast wherever you  listen, rate and review us at Apple Podcasts, and/or consider making a donation 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 • LinkedInWorks Discussed:“From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning”Zachary Wojtowicz & Simon DeDeo (+ SFI press release on this paper)“Supertheories and Consilience from Alchemy to Electromagnetism”Simon DeDeo (SFI lecture video)“From equality to hierarchy”Simon DeDeo & Elizabeth HobsonThe Complex Alternative: Complexity Scientists on the COVID-19 PandemicSFI Press (with “From Virus to Symptom” by Simon DeDeo)“Boredom and Flow: An Opportunity Cost Theory of Attention-Directing Motivational States”Zachary Wojtowicz, Nick Chater, & George Loewenstein“Scale and information-processing thresholds in Holocene social evolution”Jaeweon Shin, Michael Holton Price, David H. Wolpert, Hajime Shimao, Brendan Tracey, & Timothy A. Kohler “Slowed canonical progress in large fields of science”Johan Chu and James Evans“Will A Large Complex System Be Stable?”Robert MayRelated Podcast Episodes:• Andy Dobson on Disease Ecology & Conservation Strategy• Nicole Creanza on Cultural Evolution in Humans & Songbirds• On Coronavirus, Crisis, and Creative Opportunity with David Krakauer• Carl Bergstrom & Jevin West on Calling Bullshit: The Art of Skepticism in a Data-Driven World• Vicky Yang & Henrik Olsson on Political Polling & Polarization: How We Make Decisions & Identities• David Wolpert on The No Free Lunch Theorems and Why They Undermine The Scientific Method• Science in The Time of COVID: Michael Lachmann & Sam Scarpino on Lessons from The Pandemic• Jonas Dalege on The Physics of Attitudes & Beliefs• Tyler Marghetis on Breakdowns & Breakthroughs: Critical Transitions in Jazz & MathematicsMentioned:David Spergel, Zachary Wojtowicz, Stuart Kauffman, Jessica Flack, Thomas Bayes, Claude Shannon, Sean M. Carroll, Dan Sperber, David Krakauer, Marten Scheffer, David Deutsch, Jaewon Shin, Stuart Firestein, Bob May, Peter Turchin, David Hume, Jimmy Wales, Tyler Marghetis

There is growing appreciation of the impact of the non conformist minister. Thomas Byre lifes work.  His theorem which was posthumously published has now created a whole school in the field of Statistics

Bayes’ Rule has been used in AI, genetic studies, translating foreign languages and even cracking the Enigma Code in the Second World War. We find out about Thomas Bayes - the 18th century English statistician and clergyman whose work was largely forgotten until the 20th century.

Tripp in an AI researcher at Amazon and a friend I met through the Paul Vanderklay online community. He is an expert on Aristotelianism, Thomistic theology, and the philosophy of mind with a masters in philosophy from Duke. We talk about the doctrine of divine simplicity, how that relates to the doctrine of the trinity, Thomas Aquinas, Aristotle, Ray Kurzweil, Alvin Plantinga, the evolutionary argument against naturalism, David Hume, Thomas Bayes, the argument against miracles, Adam Friended, Michael Shermer, Alexander the Great, Tim McGrew, Douglas Murray, Christian Atheists, William Lane Craig, the cosmological argument, the Gospel of John, John the Baptist, and just why was Jesus crucified? Here is our first conversation: https://www.youtube.com/watch?v=hvXOZj4kQGU&t=2s Here is a link to Tripp, Esther and Adam Friended debating the resurrection: https://www.youtube.com/watch?v=V7g9ic521c8 Tim McGrew and the argument from miracles: https://www.youtube.com/watch?v=GSEobV4cHnc

Connor Tan is a physicist and senior data scientist working for a multinational energy company where he co-founded and leads a data science team. He holds a first-class degree in experimental and theoretical physics from Cambridge university. With a master's in particle astrophysics. He specializes in the application of machine learning models and Bayesian methods. Today we explore the history, pratical utility, and unique capabilities of Bayesian methods. We also discuss the computational difficulties inherent in Bayesian methods along with modern methods for approximate solutions such as Markov Chain Monte Carlo. Finally, we discuss how Bayesian optimization in the context of automl may one day put Data Scientists like Connor out of work. Panel: Dr. Keith Duggar, Alex Stenlake, Dr. Tim Scarfe 00:00:00 Duggars philisophical ramblings on Bayesianism 00:05:10 Introduction 00:07:30 small datasets and prior scientific knowledge 00:10:37 Bayesian methods are probability theory 00:14:00 Bayesian methods demand hard computations 00:15:46 uncertainty can matter more than estimators 00:19:29 updating or combining knowledge is a key feature 00:25:39 Frequency or Reasonable Expectation as the Primary Concept 00:30:02 Gambling and coin flips 00:37:32 Rev. Thomas Bayes's pool table 00:40:37 ignorance priors are beautiful yet hard 00:43:49 connections between common distributions 00:49:13 A curious Universe, Benford's Law 00:55:17 choosing priors, a tale of two factories 01:02:19 integration, the computational Achilles heel 01:35:25 Bayesian social context in the ML community 01:10:24 frequentist methods as a first approximation 01:13:13 driven to Bayesian methods by small sample size 01:18:46 Bayesian optimization with automl, a job killer? 01:25:28 different approaches to hyper-parameter optimization 01:30:18 advice for aspiring Bayesians 01:33:59 who would connor interview next? Connor Tann: https://www.linkedin.com/in/connor-tann-a92906a1/ https://twitter.com/connossor

Thomas Chittenden, chief data science officer at Genuity Science, says what's keeping the genomics revolution from turning into an equivalent revolution in drug discovery is that most of our domain knowledge about the molecular biology of disease has come from a hunt-and-peck approach, focused on one gene at a time. Find some gene relevant to a disease, knock it out, and you see what happens. Such experiments are always revealing, but the reality is that human biology is the product of the interactions of huge networks of thousands of genes—which means most diseases are the product of dysregulation across these networks. Which means, in turn, that to figure out where to intervene with a drug, you really need to identify the patterns that cascade through the whole network. That’s where AI and machine learning come in, and that’s why Genuity has tasked Chittenden to lead R&D at its Advanced Artificial Intelligence Research Laboratory. Chittenden's team is pioneering new applications of old ideas from the world of probability and statistics, including some that go all the way back to the work of the English statistician Thomas Bayes in the eighteenth century, to look at gene expression data from individual cells and predict which genes are at the beginning of the cascade and are the causal drivers of diseases like atherosclerosis or high blood pressure. The hope is that Genuity can help its clients in the drug discovery business make smarter bets about which drug candidates will be most effective. And that could help shave years of development and billions of dollars in costs off the drug development process.Chittenden is one of those rare professionals who has more degrees than you can shake a stick at—he has a PhD in Molecular Cell Biology and Biotechnology from Virginia Tech and a DPhil in Computational Statistics from the University of Oxford, and completed postdoctoral training at Dartmouth Medical School, the Dana-Farber Cancer Institute, and the Harvard School of Public Health—but can also explain the actual science in a way that makes sense for a non-expert. On top of that he’s been thinking hard about how to rein in some of the hype around the power of AI and machine learning in drug development and how to set expectations about what computing can and can’t do for the industry.Please rate and review MoneyBall Medicine on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:• Launch the “Podcasts” app on your device. If you can’t find this app, swipe all the way to the left on your home screen until you’re on the Search page. Tap the search field at the top and type in “Podcasts.” Apple’s Podcasts app should show up in the search results.• Tap the Podcasts app icon, and after it opens, tap the Search field at the top, or the little magnifying glass icon in the lower right corner.• Type MoneyBall Medicine into the search field and press the Search button.• In the search results, click on the MoneyBall Medicine logo.• On the next page, scroll down until you see the Ratings & Reviews section. Below that, you’ll see five purple stars.• Tap the stars to rate the show.• Scroll down a little farther. You’ll see a purple link saying “Write a Review.”• On the next screen, you’ll see the stars again. You can tap them to leave a rating if you haven’t already.• In the Title field, type a summary for your review.• In the Review field, type your review.• When you’re finished, click Send.• That’s it, you’re done. Thanks!

Hoje é dia do "Influencers da Ciência", um Spin-Off do podcast "Intervalo de Confiança". Neste programa trazemos o nome de Influencers que de fato trouxeram algo de positivo para a sociedade, aqueles que expandiram as fronteiras do conhecimento científico e hoje permitiram o desenvolvimento de diversas áreas. Neste episódio, Igor Alcantara fala sobre a vida e obra daquele que podemos considerar o Van Gogh da estatística. Assim como o famoso pintor francês, Thomas Bayes teve seu principal trabalho reconhecido (e publicado) apenas após a sua morte. Neste episódio também é explicado um pouco sobre o que é o famoso Teorema de Bayes, tão importante e fundamental para a ciência até nos dias de hoje em questões que vão desde a calcular a chance de um time vencer uma partida até a fotografar um buraco negro. Escute este episódio e conheça mais sobre esta importante personalidade. Apresentou este episódio Igor Alcantara. A edição foi feita por Leo Oliveira. A vitrine do episódio foi criada por Diego Madeira. Não esqueça também de visitar nosso site em http://intervalodeconfianca.com.br

Episode: 1876 In which Thomas Bayes mixes prior knowledge with a priori deduction.  Today, we learn how to hedge bets.

(NOTAS DEL CAPÍTULO AQUÍ: https://www.jaimerodriguezdesantiago.com/kaizen/37-toma-de-decisiones-iv-pensamiento-probabilistico-zipi-y-zape-bebes-aleatorios-y-cosas-asimetricas/)A lo tonto habré dedicado ya tres o cuatro capítulos a la toma de decisiones en lo que llevamos de podcast y lo cierto es que a medida que lo hago tengo un poco más claro mi objetivo con ello. Sé que así dicho suena raro, pero es lo que tiene que yo también vaya aprendiendo en el proceso y entendiendo mejor las implicaciones de lo que te cuento :)A mí me obsesionan las decisiones porque creo que nuestras vidas son esencialmente una sucesión continua de decisiones, desde que nos levantamos hasta que nos acostamos. Creo también que hay pocas cosas más poderosas para sentirnos bien con nosotros mismos que asumir la responsabilidad sobre nuestras vidas y sobre lo que hacemos. Es decir, sobre nuestras decisiones. De ahí mi obsesión.Y en el fondo no es que haya dedicado 3 o 4 capítulos al tema, sino que seguramente casi todo kaizen está directa o indirectamente relacionado con ello. Decidir mejor es la consecuencia de 4 cosas:1) Tener los suficientes conocimientos y modelos mentales para entender la realidad, siendo conscientes de nuestras propias limitaciones y sesgos.2) Saber cómo priorizar y elegir dónde enfocar nuestros esfuerzos3) Tener las herramientas adecuadas para anticipar el impacto de las distintas alternativas4) Ser capaces de enfrentarnos a los resultados de las mismas.Vamos, lo que viene siendo, vivir.Bueno, pues hoy nos vamos a enfocar en una forma de entender el mundo y de tomar decisiones que a mí me parece especialmente interesante; porque creo que refleja muy bien la complejidad en la que vivimos. Es el pensamiento probabilístico.En realidad, ya te he hablado antes del pensamiento probabilísitico. En la temporada pasada de kaizen, le dediqué un primer capítulo a la toma de decisiones y en él empezamos a hablar de este tema. Entonces, te conté un poco por encima los árboles de decisión, que son una de las herramientas más habituales para aplicar este tipo de razonamiento. Pero hoy me gustaría que intentáramos profundizar en qué significa, algunos de sus conceptos clave y cómo aplicarlo a nuestras vidas. 

When will the world end? How likely is it that intelligent extraterrestrial life exists? Are we living in a simulation like the Matrix? Is our universe but one in a multiverse? How does Warren Buffett continue to beat the stock market? How much longer will your romance last? In this wide ranging conversation with science writer William Poundstone, answers to these questions, and more, will be provided … or at least considered in the framework of Bayesian analysis. In the 18th century, the British minister and mathematician Thomas Bayes devised a theorem that allowed him to assign probabilities to events that had never happened before. It languished in obscurity for centuries until computers came along and made it easy to crunch the numbers. Now, as the foundation of big data, Bayes’ formula has become a linchpin of the digital economy. But here’s where things get really interesting: Bayes’ theorem can also be used to lay odds on the existence of extraterrestrial intelligence; on whether we live in a Matrix-like counterfeit of reality; on the “many worlds” interpretation of quantum theory being correct; and on the biggest question of all: how long will humanity survive? The Doomsday Calculation tells how Silicon Valley’s profitable formula became a controversial pivot of contemporary thought. Drawing on interviews with thought leaders around the globe, it’s the story of a group of intellectual mavericks who are challenging what we thought we knew about our place in the universe. Listen to Science Salon via iTunes, Spotify, Google Play Music, Stitcher, iHeartRadio, TuneIn, and Soundcloud. You play a vital part in our commitment to promote science and reason. If you enjoy the Science Salon Podcast, please show your support by making a donation, or by becoming a patron.  

A semana pasada tivo lugar en Santiago de Compostela a 4ª Reunión Xeneral da Rede Nacional de Biostatística Biostatnet formada por 8 nodos, un deles en Galicia, que agrupa a uns 200 investigadores de toda España. Conversamos con Carmen Armero, investigadora principal do nodo valenciano da rede Biostatnet. Unha das sorpresas deste encontro foi na cea do congreso en San Martin Pinario onde fixeron acto de presenza o mesmísimo Carl Gaus e Thomas Balles a quen temos o gusto de ter en Efervesciencia para protagonizar unha nova “Entrevista no alén”

A semana pasada tivo lugar en Santiago de Compostela a 4ª Reunión Xeneral da Rede Nacional de Biostatística Biostatnet formada por 8 nodos, un deles en Galicia, que agrupa a uns 200 investigadores de toda España. Conversamos con Carmen Armero, investigadora principal do nodo valenciano da rede Biostatnet. Unha das sorpresas deste encontro foi na cea do congreso en San Martin Pinario onde fixeron acto de presenza o mesmísimo Carl Gaus e Thomas Balles a quen temos o gusto de ter en Efervesciencia para protagonizar unha nova “Entrevista no alén”

When AI platforms are not busy beating us at Go or showing us how to drive cars properly, they are also changing the way that companies spend and track money. Talking with Manish Singh, an EVP at Oversight Systems, I learned how machine learning is both automating financial operations, and transforming the way we mitigate risk. Although Manish and I had fun talking about some of my favorite geeky AI topics (probabilistic thinking and the influence of Thomas Bayes), what you may find really interesting, is our discussion on how clerical jobs in the company of the future will not be simply automated, but elevated into something altogether new with very different skills and outcomes.

When AI platforms are not busy beating us at Go or showing us how to drive cars properly, they are also changing the way that companies spend and track money. Talking with Manish Singh, an EVP at Oversight Systems, I learned how machine learning is both automating financial operations, and transforming the way we mitigate risk. Although Manish and I had fun talking about some of my favorite geeky AI topics (probabilistic thinking and the influence of Thomas Bayes), what you may find really interesting, is our discussion on how clerical jobs in the company of the future will not be simply automated, but elevated into something altogether new with very different skills and outcomes.

Thomas Bayes foi um matemático inglês, eleito membro da Royal Society em 1742. É dele a lei segundo a qual a probabilidade de um evento varia conforme o conhecimento a priori que pode estar relacionado ao evento. Como ela funciona na prática? Qual a relação com o nosso dia-a-dia? E o que isso tem a ver com o horóscopo? Saiba neste episódio do Naruhodo! — no papo entre o leigo curioso, Ken Fujioka, e o cientista PhD, Altay de Souza. OUÇA (30min 08s) Naruhodo! é o podcast pra quem tem fome de aprender. Ciência, senso comum, curiosidades, desafios e muito mais. Com o leigo curioso, Ken Fujioka, e o cientista PhD, Altay de Souza. Edição: Reginaldo Cursino. http://naruhodo.b9.com.br REFERÊNCIAS Naruhodo #51 – Astrologia, horóscopo e mapa astral têm algo de científico? ==> http://www.b9.com.br/70398/naruhodo-51-astrologia-horoscopo-e-mapa-astral-tem-algo-de-cientifico/ APOIA.SE Você sabia que pode ajudar a manter o Naruhodo no ar? Ao contribuir, você pode ter acesso ao grupo fechado no Facebook e receber conteúdos exclusivos. Acesse: http://apoia.se/naruhodopodcast

Show notes at ocdevel.com/mlg/2 Updated! Skip to [00:29:36] for Data Science (new content) if you've already heard this episode. What is artificial intelligence, machine learning, and data science? What are their differences? AI history. Hierarchical breakdown: DS(AI(ML)). Data science: any profession dealing with data (including AI & ML). Artificial intelligence is simulated intellectual tasks. Machine Learning is algorithms trained on data to learn patterns to make predictions. Artificial Intelligence (AI) - Wikipedia Oxford Languages: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AlphaGo Movie, very good! Sub-disciplines Reasoning, problem solving Knowledge representation Planning Learning Natural language processing Perception Motion and manipulation Social intelligence General intelligence Applications Autonomous vehicles (drones, self-driving cars) Medical diagnosis Creating art (such as poetry) Proving mathematical theorems Playing games (such as Chess or Go) Search engines Online assistants (such as Siri) Image recognition in photographs Spam filtering Prediction of judicial decisions Targeting online advertisements Machine Learning (ML) - Wikipedia Oxford Languages: the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Data Science (DS) - Wikipedia Wikipedia: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data. History Greek mythology, Golums First attempt: Ramon Lull, 13th century Davinci's walking animals Descartes, Leibniz 1700s-1800s: Statistics & Mathematical decision making Thomas Bayes: reasoning about the probability of events George Boole: logical reasoning / binary algebra Gottlob Frege: Propositional logic 1832: Charles Babbage & Ada Byron / Lovelace: designed Analytical Engine (1832), programmable mechanical calculating machines 1936: Universal Turing Machine Computing Machinery and Intelligence - explored AI! 1946: John von Neumann Universal Computing Machine 1943: Warren McCulloch & Walter Pitts: cogsci rep of neuron; Frank Rosemblatt uses to create Perceptron (-> neural networks by way of MLP) 50s-70s: "AI" coined @Dartmouth workshop 1956 - goal to simulate all aspects of intelligence. John McCarthy, Marvin Minksy, Arthur Samuel, Oliver Selfridge, Ray Solomonoff, Allen Newell, Herbert Simon Newell & Simon: Hueristics -> Logic Theories, General Problem Solver Slefridge: Computer Vision NLP Stanford Research Institute: Shakey Feigenbaum: Expert systems GOFAI / symbolism: operations research / management science; logic-based; knowledge-based / expert systems 70s: Lighthill report (James Lighthill), big promises -> AI Winter 90s: Data, Computation, Practical Application -> AI back (90s) Connectionism optimizations: Geoffrey Hinton: 2006, optimized back propagation Bloomberg, 2015 was whopper for AI in industry AlphaGo & DeepMind

In the late 1700s, English minister Thomas Bayes discovered a simple mathematical rule for calculating probabilities based on different information sources. Since then Bayesian models for describing uncertain events have taken off in a wide variety of fields, not the least of which is psychology. This Bayesian framework has been used to understand far-reaching psychological processes, such as how humans combine noisy sensory information with their prior beliefs about the world in order to come to decisions on how to act. But not everyone is riding the Bayesian train. In this episode, we discuss a published back and forth between scientists arguing over the use and merits of Bayesian modeling in neuroscience and psychology. First, though, we set the stage by describing Bayesian math, how it is used in psychology, and the significance of certain terms such as "optimal" (it may not mean what you think it does) and "utility". We then get into the arguments for and against Bayesian modeling, including its falsifiability and the extent to which Bayesian findings are overstated or outright confused. Ultimately, it seems the expansive power of Bayesian modeling to describe almost anything may in fact be its downfall. Do Bayesian models give us insight on animal brains and behaviors, or just a bunch of "just-so" stories?