Nassim Taleb — Meditations on Extremistan

Summary

Highlights

• Taleb challenges the traditional understanding of probability distributions, emphasizing the need for models that account for large, unpredictable deviations. 🔄
• The conversation delves into Taleb's critique of standard financial theories, exploring how they often fail to accommodate the true nature of market dynamics and crises. 💥
• Taleb highlights the dangers inherent in neglecting the power of rare but significant events, advising that financial models should incorporate these risks more effectively. 🚨
• There's a discussion about the relevance and limitations of conventional economic models when faced with fat-tailed risks, particularly in financial markets. 📊
• The talk explores the intersection of behavioral economics and real-world unpredictability, showcasing Taleb's skepticism about overly neat theoretical models. 🤔

Key Takeaways

• Understanding Extremistan: Taleb introduces Extremistan as a domain where extreme deviations are more common and impactful than expected, advising a cautious approach to risk. 📈
• Critique of Standard Economics: He challenges traditional methods in finance and economics for underestimating tail risks and overvaluing consistency over time. 💼
• Importance of Fat Tails: Taleb emphasizes the necessity of considering fat-tailed distributions when analyzing risks, particularly in fields like finance and pandemics. 🦢
• Precautionary Principle: Stresses the importance of precaution in the face of systemic risks, such as pandemics and environmental challenges. Accordingly, preventive actions should not be delayed by uncertainties. ⚠️
• Behavioral Economics Skepticism: Taleb critiques behavioral economists for underestimating the unpredictability of real-world events, arguing their models don't translate well outside theory. 🔍

Overview

Chapters

• 00:00 - 00:30: Introduction to the Podcast and Guest In this introductory chapter of the podcast, the host engages in a conversation with Nasim Nicholas Taleb, a significant influence on the host's thinking. Taleb's work, discovered by the host in late 2016, began with 'The Black Swan' and concluded with 'Fooled by Randomness,' although the host read them out of sequence. The host shares a personal connection with 'The Black Swan,' highlighting its focus on the concept of the platonic fold rather than merely unforeseen events. The chapter sets the tone for a deep dive into Taleb's ideas.
• 00:30 - 03:00: Discussion of Black Swan and Extremistan The chapter opens with the speaker expressing admiration for Nim, a person they are interviewing on a podcast, highlighting their magnanimity and kindness. It sets a respectful and positive tone for the discussion.
• 03:00 - 05:30: Heuristics for Identifying Extremistan The chapter delves into the concept of identifying 'extremistan', a term used to describe environments prone to extreme deviations rather than the metaphor of a 'Black Swan'. It emphasizes the importance of recognizing areas that are susceptible to large, impactful deviations and discusses the implications of such events. The focus is on understanding how to identify these extreme environments without relying solely on the occurrence of a Black Swan event.
• 05:30 - 09:00: Understanding Fat Tails and Probability Distributions Discusses the concept of asymmetry in probability distributions, particularly in thin-tailed distributions.
• 09:00 - 14:30: Theories of Financial Markets and Tail Exponents Explores models of financial markets, focusing on large deviation models and extremist stand models. Emphasizes the statistical properties of quiet periods and the necessity of assuming the second class of models unless there are strong reasons not to.
• 14:30 - 29:00: Behavioral Economics and Empirical Psychology The chapter explores the concept of realistic world representation and limitations of human perception. It uses an analogy of height to illustrate how extreme deviations (e.g., encountering a person significantly taller than average) are within the realm of possibility, thus demonstrating our understanding of typical physical boundaries. However, it also emphasizes the implausibility of encountering impossibly exaggerated examples, such as a person being kilometers tall, to highlight the inherent constraints of our perception and understanding of reality. Such insights are part of the broader discussion within behavioral economics and empirical psychology, focusing on how we process and interpret environmental information.
• 29:00 - 35:00: Critique of Superforecasting and Binary Predictions In this chapter, the discussion revolves around the concept of human cognitive limitations, especially regarding the ability to make predictions. The speaker argues that these limitations are inherent, much like the biological need for parental care in humans. The conversation touches upon the principles of maximum entropy, particularly in relation to Generative Adversarial Networks (GANs). It explains that a GAN can be considered a maximum entropy distribution when you have known mean and variance, and hence it imposes a kind of 'boundary' or limitation on the variance of the system. The implication is that human judgment in forecasting, much like statistical models, may have constraints similar to those encountered in GANs, which stems from the intrinsic limitations of our cognitive faculties and structured methodologies.
• 35:00 - 43:00: Precautionary Principle and Risks of AI The chapter discusses the concept of bounding variance, which is likened to bounding energy, highlighting that systems cannot possess unlimited energy. This idea is rooted in the understanding that many mechanisms have inherent physical limitations. The discussion is framed within the context of biological and physical knowledge, illustrating how these insights can rule out certain possibilities.
• 43:00 - 48:30: Statistical Consequences of Fat Tails and Historical Analysis This chapter discusses the statistical consequences of fat tails, focusing on historical analysis and complex processes like pandemics and financial markets.
• 48:30 - 51:30: Covid-19 and Policy Implications of Fat Tails The chapter discusses the economic implications of Covid-19, particularly focusing on the tail risks, or 'fat tails,' of extreme events in economic and health-related fields. The conversation highlights the absence of physical limitations on price, which can result in wide-ranging economic scenarios due to unpredictable events like a pandemic. The discussion draws analogies to statistical distributions, such as Gaussian processes, to illustrate the concept of bounded versus unbounded scenarios with respect to variance.
• 51:30 - 62:00: Random Questions and Insights into Social Sciences The chapter discusses the use of heuristics to determine the validity of beliefs, particularly in the context of social sciences. It highlights the intuition involved in recognizing credible information, using examples such as weight and height to explain how extreme deviations can be identified and potentially considered acceptable under certain circumstances.
• 62:00 - 65:00: Conclusion and Future Work The chapter explores the concept of scaling and extreme events within finance compared to physical human characteristics. It uses a metaphor of imagining a 5-meter tall human due to hormonal deregulation, pointing out that such extremes are not possible, like having a 500-kilometer tall human. However, in finance, it warns that one cannot rule out the equivalent of '500-km tall' occurrences, meaning extreme events. The chapter emphasizes the need for robust explanations and models in data that can convincingly dismiss or understand these deviations either through energy factors or more qualitative measures.

Nassim Taleb — Meditations on Extremistan Transcription

• 00:00 - 00:30 today I'm speaking with Nasim Nicholas talb he has influenced me perhaps more than any other thinker I discovered his work when I was quite young at the end of 2016 I read his books out of order I finished with for by Randomness and I started with the black one the correct order the black one was the book that got me hooked for me that book was not so much about black swans as about what Nim calls the platonic fold and this year I've had the pleasure
• 00:30 - 01:00 of meeting him in person he has a certain magnanimity he's been very kind to me so it's an honor to have him on the podcast welcome Nim thank you for inviting me so naturally I have many questions and I guess the theme of my questions is probably best summed up by the title of your technical book The statistical consequences of fat tales but I'd like to start a little bit abstract and then get more and more real so first question it only takes one Black Swan to know that you're in extrem
• 01:00 - 01:30 but if you're in a particular domain which has yet to experience a black one how do you know whether or not you're an extremist let's not use the word Black Swan okay and use extreme deviation all right Black Swan is something that carries large consequences it tends to happen more easily in an environment that produces ta large deviation so what I call extremist time MH so let's ignore the terminology Black Swan here because it
• 01:30 - 02:00 may be confusing and let's say that the following asymmetry let's present discuss the following asymmetry if I am using a thin tail probability distribution you say I can be always surprised by an out lier MH with respect to my distribution a large deviation you see uh that would destroy my assumption of using that distribution
• 02:00 - 02:30 if on the other hand I'm using a large deviation model or model that the extremist stand model the reverse cannot be true nothing can surprise you a quiet period is entirely within statistical properties so is a large deviation which is why you have to assume that you're in the second class of models unless you have real reasons
• 02:30 - 03:00 see to to a real uh robust representation of the world to uh rule it out right like for example we know that with height that you're you you're from Australia even I mean in Australia you may run into someone who's 2 meters 40 cm tall uh but have you I mean even Australia they don't have people five kilometers at all or 500 kilm stall yeah
• 03:00 - 03:30 why there are biological limitation the person needs to have a mother see for when we do if you use a maximum entropy representation uh the gaan is the maximum entropy distribution with known mean and variance so you're bounding the variance you see
• 03:30 - 04:00 if you bound the variance it's the equivalent of bounding the energy so you see what I'm leading at there's you can't have unlimited energy so the you know that lot of mechanisms have these physical limitations right you see so you can rule out based on knowledge of the process biological understanding physical understanding
• 04:00 - 04:30 but if you don't know anything about the process or the process is in multiplicative uh concern multiplicative uh phenomena such as contagions pandemics or simply processes that don't have limit to their to their movement like for example a price you and I can sell you know buy from one another this at the a billion
• 04:30 - 05:00 dollars okay there's no limitations there's no physical limitation m to the to a price therefore you could be an extremist stand and you cannot rule out a a thick tail distribution right so what you mentioned height as an example of a giian process yeah or actually Sudan more like like normal but right with low variance yes sure because it's bounded on the left yeah okay so what is
• 05:00 - 05:30 some heuristics you use to judge whether you have a compelling reason to believe that something has a g process it's not a I mean uh you see it when you you know it when you see it okay if if if we're talking about weight height such phenomena then you can rule out extremely large deviation yeah not completely but but those deviation that occur are not are still going to be uh like U acceptable in other words you may
• 05:30 - 06:00 have a 5 meter tall human with some kind of deregulation hormonal deregulation or something like that but you're not going to get a 500 kilometer tall human yeah in finance you can't rule out the equivalent of 500 kilm tall or 5 billion K all person yeah okay so basically you need to couple the absence of extreme events with some kind of very compelling explanation as to why the data explanation that rules out the deviation based on either energy or Y uh uh more a
• 06:00 - 06:30 knowledge of the physical process y the generator is physical after all so it's interesting that not only do power laws appear everywhere in the natural and social world but perhaps certain taale exponents appear to be intrinsic so last week I was chatting with your friend and collaborator rapael D and he mentioned that he has this view that the tale exponent for financial markets seems to be three and he's wrong
• 06:30 - 07:00 but but that's Rafael right there was a theory of why it was it's called the the the the the semic cubic Theory okay that he is following and it someone figured out that uh the tail exponent for U for company size start of companies was 1.5 M so therefore their orders are going to the
• 07:00 - 07:30 market hence by using a I mean by by using a square root model of impact in other words where where the the quantity impacts the price following some kind of square root um effect okay then you end up with markets having a what they call the the the the cubic from going from half cubic the cubic it is a nice Theory but I would uh I think uh the tail exponent in
• 07:30 - 08:00 financial markets is lower than that from experience mhm and I don't like these cute theories because the distribution of um concentration is not 1.5 half cubic in with technology it's much much higher but you also see it in other domains like a lot of people have commented on the fact that City size seems to have I would not I would not get married to these
• 08:00 - 08:30 okay is that because there's always going to be there's always the possibility of an even more extreme event to kind of screw up the exponent or or less extreme event I mean okay coming up with a with a an observation that's very noisy and generalizing to a theory of cubic or half cubic or there used to be a square law and lot of things I mean it's a very noisy representation okay okay so I have a couple of question questions about
• 08:30 - 09:00 Finance how long before 1987 did you realize that volatility shouldn't be flat across strike prices for options and how did you realize I mean I saw deviations right and I realized and I had uh you know uh an unintentional reward from having a tail exposure so I realized that I said okay you don't have to be genius to figure out if
• 09:00 - 09:30 the payoff can be so large right as to swamp the frequency so I think that I was pretty convinced by uh September 1985 after the plaza Accord had a 10 Sigma move at the time we didn't have access to data like today but you can I mean you but we saw prices and I noticed that effectively you had a um High
• 09:30 - 10:00 um frequency of these very very large deviations across stocks I mean you had mergers you have stuff like that so it was it was it was obvious and then therefore the black trolls or equivalent black trolls they call it black trolls but black trolls didn't really invent that formula they just Justified it the uh formula is from bash and others and collection of others
• 10:00 - 10:30 who rediscovered it or repackaged it differently um that that you need to have a higher price for tail options so I got in the business of collecting tail options but you one has to be pretty blind not to see that you have winner take all effects and finance which is not compatible with a gaussian representation right yeah it's pretty crazy how blind so many people have remained to that observation
• 10:30 - 11:00 so your books have become very famous universa has done very well Marx spitznagle has also written books which have sold well why hasn't the tail hedging strategy now being fully priced in by markets because of uh than because of MBA uh uh lecturing mod portfolio Theory because people get blinded by theories
• 11:00 - 11:30 and also because you uh if you're trading your own money you're going to be pretty rational about it if you're uh dealing with the institutional framework you need to make money frequently and the trap of needing to make money frequently will lead you to eventually sell volatility so there's no incentive to buy volatility for someone who's
• 11:30 - 12:00 employed you know for fin night period of time in a firm no stive right yeah are there any markets that do price in convexity they all do in a way but but it's uh they don't know how to price it right interesting so I have a question about Venture Capital but it perhaps has broader applications there there's a kind of inconsistency I noticed so on
• 12:00 - 12:30 the one hand as a consequence of the power or distribution of returns one recommendation to say Public Market investors is they may want to pursue a barbell strategy which you've written about so say you have like 90% of your portfolio and very safe things like bonds and then with 10% you can take lots of little speculative bets to maximize your optionality the same logic could also be pursued by say book publishers where you
• 12:30 - 13:00 might want to take because book the success of books is power distributed you might want to take lots of little bets to maximize your chance of publishing the next Harry Potter on the other hand I've heard Venture capitalists say that reason from the exact same premises the power law distribution of startup success but come to an opposite conclusion which is that they want to concentrate their bets really heavily in a handful of companies because okay the way uh uh you need to look at venture capital M is that it's
• 13:00 - 13:30 largely a compensation scheme largely comp like hedge funds compensation scheme okay compensation to the 2 and 20 no no the the mechanism so they don't make their money Venture Capital they don't make money by waiting for the company to really become successful they make their money by hyping up an idea okay getting new investors and they cashing in as they're bringing in new investors which I mean it's plain look at how many
• 13:30 - 14:00 extremely wealthy technology uh entrepreneurs are floating around while not having ever made a penny in uh that income see so the income for an for for for venture capital is uh comes from a greater full uh approach Okay so Ponzi kind of dynamic not necessarily Ponzi
• 14:00 - 14:30 but you're selling hope you package an idea it looks good so you sell it to someone and then they have a second round and third round they keep keep it around so you can progressively cash in got it it's not based on your real sales okay or your real your real cash flow your real particularly environment of low interest rates where there was no no penalty for playing that
• 14:30 - 15:00 game do you think there's any skill in v i mean they have skills but most of their skills are in packaging not and not for the things people think exactly right packaging because you they're trying to sell it to a another person it's a beauty contest you know the Kian the Kian beauty contest so they package a company and and we at little compensation of the adventure capitalist right you can see it I mean you have either you have you have financing rounds where someone cashes in at high
• 15:00 - 15:30 price we have a initial public offering so I come from old Finance old school Finance where you haven't really succeeded until the company gets a strong uh cash flow base all right so I have some questions about behavioral economics and empirical psychology behavioral economics I thought that was you know the center yeah not a be I'm not a behavioral economics podcast but I
• 15:30 - 16:00 do have a lot of questions about this so first question if I take the inserto chronologically you seem much more sympathetic to empirical psychology and the biases and heuristics research program in full by Randomness and at least by the time you get okay so let me tell you the secret of full by Randomness okay I wrote full by Randomness and became very successful at the first edition uh and it had no
• 16:00 - 16:30 references and I it had no behavioral just like aside from how hum don't understand probability minimal of that then I met Danny conoman in 2002 in Italy in Italy and uh and then okay I spoke to him he said you don't have a lot of references for stuff like that a lot of comments so I said no problem
• 16:30 - 17:00 so I went and I got about 100 books in Psychology I read it over a period of say six months okay went through the Corpus everything figured out you know they they think that their math is complex their math is Trivial and wrong and then I cited okay and I modeled the I remodeled the uh Prospect the Theory because prospect
• 17:00 - 17:30 theory itself because it's is a convex concave right it tells you itself that you're uh you know you you you should take if you're going to lose money you take a big [Music] lump uh if you're it's more effective to make money slowly because people like to make a million dollars a day for a year rather than 250 million and nothing okay
• 17:30 - 18:00 right and but it's a reverse for uh for losses and there a lot of things in it that's correct so I like that aspect so anyway so I and I start putting references on sentences I've written before not knowing anything about it which was not the most honor thing but it was uh to link my ideas to to that discipline right it is uh it's not like I I got the idea from these books I got
• 18:00 - 18:30 the ideas and then found confirmation in in these books then I met Danny from from the very first time I told them your ideas don't work in the real world because uh they underestimate people in the real world they underestimate the tail event whereas in your world they overestimate it but there's a difference that in real
• 18:30 - 19:00 world you don't know the odds and you don't know the payoff function very well in your world you know the odds and the payoff function so he liked the fact that I gave him a break in that sense and still used his prospect theory because the idea that losses are and the last domain is is is is convex I like the idea MH but by then I knew enough you know about the
• 19:00 - 19:30 psychology literature and about all these uh decision-making theories you know so by then I build myself a knowledge of that I revised food by Randomness such I put a section in the back okay connecting my ideas to that literature and and and I uh and then they started liking it in the world brother Chiller didn't like it he said you had a great book it was genuine
• 19:30 - 20:00 now you have an academic to that was Shiller right okay but but the other people liked it so that was my my first encounter with them was on prospect theory which I believe is correct for that function okay but not necessarily for the the rest the the uh the underestimation overestimation of probabilities in the making for reasons that are I show here
• 20:00 - 20:30 because you never have a lump loss except for lotteries typically it's a variable and there's no such thing as a typical large deviation right see it it is technical but maybe your viewers uh will get it better was an explanation we'll get there next yeah and then I started looking at Stu done in behavioral
• 20:30 - 21:00 economics such as the benan Sailor the benar and Taylor [Music] assumed that uh I thought it was a mistake basian Taylor assumed you know Gan distribution and then explained why people prefer bond to stocks that was the idea at the time and I said maybe it's right and then therefore it was irrational they they
• 21:00 - 21:30 they went from the standpoint as irrational to not have more stocks given the performance but I tell them the risk is not the one you see see you have tail risks that don't show in your analysis I told Taylor Taylor say well assuming a gaan that my theory works I say I'm not assuming you know assuming the world were were coconut you know a lot of things would work so the world is not a goian and you're recommending that for 401k and
• 21:30 - 22:00 stuff like that so then I noticed that's just you know first mistake in Sailor there are other mistakes in in that discipline uh like this idea of rationality yeah and to me rationality is in survival not in other things and I discovered uh and then I spoke to to smart people like can B more MH uh I when you speak to smart people you realize these people are not making the claims that are you know that are common
• 22:00 - 22:30 in that uh i' call IT industry in that field right uh for example uh and and there are things that are deemed irrational such as uh let me take simple example um the uh people use as a metric and and and it was not contested the transitivity of preferences that I prefer apples to pies pies to uh say
• 22:30 - 23:00 uh bananas all right okay but then bananas to Apples right so you're violating transitivity of preferences but I said no maybe that's not the way the world works if I always prefer apples to pie M and um presented that choice nature wants to make me eat other things and also to wants to uh reduce the stress on the
• 23:00 - 23:30 environment of people always eating the same thing so it's a good way for nature to make me vary my my um my preferences either to protect nature and to protect myself right so it's not necessary uh you know that transitivity of preferences is not a necessary Criterion for rationality and it's a way nature makes you randomize your choices for example for br so that's one
• 23:30 - 24:00 thing so if now if I were to structure this conversation about the defects of Behavioral uh behavioral and cognitive uh Sciences as linked to economics and decision Theory we have things linked to misunderstanding of probity structure and things linked to misunderstanding of the dynamic aspect of decision making what we call a aity right so let's put them in in these
• 24:00 - 24:30 let's use these categories uh so we have the equity premium bias comes from Equity premium the fact that uh people don't invest their explanations come from poor outstanding of property structure uh the aspect of PR theor that is wrong comes from misunderstanding
• 24:30 - 25:00 prob structure that if you you have an open-ended distribution with fat tales then you have the same result uh the idea of uh uh what's the other idea is the fact that people uh if you go 10 choices the one over end okay one over n is optimal under fat tails right but um so this again uh I think sailor has one overend
• 25:00 - 25:30 papers saying that you know you should reduce people's choices because they spread them too much but that's an optimal strategy there's another one about probability matching where you think that probability matching is irrational probability matching means that if something comes up 40% of the time and something comes up 60% of the time that you should invest 100% of the time in the higher frequency
• 25:30 - 26:00 want but in nature and in you know animals but but also humans do probably mhing and when you write the math using uh entropy like uh you know in this U Kelly uh style modeling uh you if I have 10 horses and I got allocate among the time
• 26:00 - 26:30 horses if I maximize want to maximize the expected return how do I allocate and proportion probably of of when so this is these are the error link to probabilistic structure there's another one also there is inter temporal choices like if I uh if I tell you do you want to Mass today or two massages tomorrow
• 26:30 - 27:00 we like to say okay two massages tomorrow or let's assume that that you when facing this choice you take the two massages tomorrow that not one today but if I tell you in 364 days a choice of One Versus two you say you would reverse no you're possibly actually let's say that that you have it the other way that you take one the one today rather than two tomorrow but you reverse that's not if you use a different probability
• 27:00 - 27:30 distribution different preference structure right plus there is another one that what I mean how do you know the guy the the person offering you that bet will satisfy tomorrow you see as I see the bird in a hand is better than right then some attract one in the future on some tree okay okay so uh so if you say the person is full
• 27:30 - 28:00 of baloney all right maybe full of Balon I'd rather have one today okay let me take it today or may be bankrupt but if he 364 6 365 days the effect is not that big yeah yeah so it depends on what kind of uh preference structure you have or what kind of me errors you have in your models so this is the first class misunderstanding probability and we can go on forever the second one is more
• 28:00 - 28:30 severe misunderstanding of Dynamics like we had a Twitter fight with Taylor while running a ruri where he couldn't understand why you can refuse a bet a 55% you know win versus 45% probability of uh of losing that someone can refuse such
• 28:30 - 29:00 a bet and be rational okay number one is realize that of course you can reduce you can refuse such a bet because you got to look at things dynamically yeah if you keep taking those bets eventually blow up no I take risk in life of that nature all the time yeah you see so and it would bring you closer to an uncle Point yeah I could probably do it for a dollar but maybe not $10 or not $100 certainly
• 29:00 - 29:30 not million dollars yeah see so he he couldn't understand the erotic erotic thing and that's the Kelly Criterion shows it clearly but Kelly Criterion is just one example of of getting that result for that optimizes for growth my whole idea is surviving it's like it's simple like saying hey you know what the trade-off of smoking one cigarette look at how much pleasure you derive versus how much risk you're taking so rational so yes but do
• 29:30 - 30:00 you know people who smoke once you know you got to look at the activity not an episode and he couldn't get it that's that's one example uh there are other similar examples oh let's let's talk about mental accounting uh I think he started was mental accounting sailor and he uh find it irrational that uh say uh husband and wife wife have a joint check-in account the
• 30:00 - 30:30 husband visits the department store sees a tie doesn't buy it it's too expensive goes home and then see this gift and got all excited that he got it from his wife for his birthday okay so you know that mental counting is uh irrational I say okay but how many
• 30:30 - 31:00 birthdays do you have a year okay yeah so it's it's not frequent so you know so this is where you know you got to put some structure around the mental accounting another mistake he makes the not St mistake the mistake is H that it's irrational when you go to a casino to increase your uh your betting when you win money from a casino that's mental accounting that money won from a
• 31:00 - 31:30 casino should be treated from an accounting standpoint the same way as money that you had as an initial endowment okay you think about it if you don't play that game you're going to go bankrupt this is what we call it because playing with the house money so it's not R so practices that have been around for a long time are being judged by that industry I call it an industry because you became an industry just producing papers um and and uh
• 31:30 - 32:00 they they don't have a good understanding of of the real world so and not a good understanding of probability Theory so so this is why uh we shouldn't be talking about it and actually I I hardly ever talk about them anymore I mean I discussed them initially when I went in and and and found effectively yeah we are Fooled
• 32:00 - 32:30 by Randomness but not in the way they think yeah and they are more Fooled by Randomness in other areas so let me pull out of this so I pulled out of this but in my writing I hardly ever discuss them yeah at least I guess like distinguishing empirical Psychology from behavioral economics my quick take on empirical psychology is that a lot of thetics that say Danny and Amos found are actually disc ively pretty good approximations of how humans think but
• 32:30 - 33:00 the problem was the additional step they took of then labeling those as you know the use of many of those heuristics as irrational against their normative use use the word irrational they but but yeah they were careful they were careful with that but they still they still indirectly use it only because they had war with some gig some uh no after the the I think was it the uh not Lisa paper the the one who the
• 33:00 - 33:30 the bank teller the bank teller Linda the Linda problem yeah yeah they had a lot of problem with the philosophers who and then they avoid use the term in the whole industry term rationality right but effectively they find it is not uh uh something yeah rational but they don't use the word rational yeah yeah the the okay but but you forget forget a few
• 33:30 - 34:00 things that uh one I had a that a lot of people in the U advertising industry new STS and then also even in the psychology literature a lot of things have been done but their Mark is to show how decision making by human is messed up it's like what tki said I don't specialize artificial intelligence naal stupidity all right but effectively
• 34:00 - 34:30 they are the ones who are stupid I mean people in that industry not the not humans I who've survived doing these things uh and also there's the school of G renzer who finds you know that these juristic are ecologically rational are rational and and but but you don't have to go out of the way to to to show that these things are rational I just don't want my problem is that I don't want the practitioners of that field who
• 34:30 - 35:00 understand barely understand probability to get anywhere near the White House yeah and we dangerously uh uh came closer but but we were uh during covid I mean first remember that we had C sunstein who to me is about as dangerous as you can get okay what I called actually I wrote iyi the intellectual yet idiot based on him and uh and Taylor right because I knew Taylor well son Ste I met once but it's
• 35:00 - 35:30 sort of kind of thing that that sort of like instant uh Revelation oh that he he is it right the way they reason okay and so I uh you know we had these people advising initially against reacting to co again misunderstanding of probability why they say well this is the empirical risk uh and the risk of Ebola you know
• 35:30 - 36:00 is very low compared to the risk of uh falling from a ladder yeah yeah that they were they were on it I started the war against that was before and when Co started um sunstein was advocating you know was was advocating ignoring covid because he said look how the risk are low he he he mixed a multiplicative process yeah with a uh additive one and by the way way now is you ask me to figure out the
• 36:00 - 36:30 difference is for me to get you get fat tails via multiplicated processes MH not all F fat tails come from multiplicative processes but you need the but multiplicative always generates some kind of either log normal or fat tail but log normal is very fat tail by the way yeah and at high variance it acts like a power LW right where is it low variance it acts more thin tail it it looks at low variance like yeah it's strange isn't it it that's
• 36:30 - 37:00 long normal there was an Australian Australian an Australian person I think his name was hay who spent all his life on a log normal oh really yeah are there examples in the real world of log normal distributions yeah of course the the there was there was a big dispute between M BR and anti-al BR saying that from jbra JRA look at wealth yeah what happens with the think when you start multi applying you see you you you you you get
• 37:00 - 37:30 a log normal right naturally oh okay so what and the way is technical sorry no technical is good yeah Technic so if I take a uh G distribution and take the exponential of the variable you see because you know that that the the the log is additive right okay okay so when you multiply so you take the exponential you get a log
• 37:30 - 38:00 normal distribution MH X okay logal distribution and and the MU and sigma L distribution are preserved right they're not the the mean and variance of the log normal there mean a variance of the log of the log normal okay it's misnamed should be the exponential but there was another name called exponential for another distribution Okay so gaussian you exponentiate you get log
• 38:00 - 38:30 normal now there's a distribution is thin tailed but slightly F tail than gaan barely right the exponential the gamma you know that class okay you exponentiate what do you get a power law you see so you're very so which one you're exponentiating your base distri ution needs to be gaussian for it to end with a l normal right or fatter tail
• 38:30 - 39:00 than gaussian okay and the next class is a gamma or you know the uh the exponential and you get a parto right yeah and then of course there's a exponential of a parto it's called log parto okay and here as they say no longer caners you're not cancas anymore this is a little B above my pay grade but I I it seems to make sense so
• 39:00 - 39:30 just a a couple of final questions on behavioral economics and then I want to move on to some other stuff which results in behavioral economics do you think are robust we've spoken about the loss do they call the asymmetric loss function and Prospect is there anything else because uh no no nothing else uh let me think about
• 39:30 - 40:00 it I think that I mean I mean we know a bunch of things that that that that that are part of that school but they're not Central to like for example how people react framing how people react based on how we present things to them how they a lot of these things work but whenever they make a general theory with a recommendation that connects to the real world they get it backwards right I mean back I mean I took sailor I told you sailor all his papers okay you interpret them
• 40:00 - 40:30 backwards if he says okay you should have uh a concentration okay an optimal concentration of stock you go over one over end you saw my podcast with Danny Conan last year I did not see it I just read the segment where he said that that he accepted that uh I mean he said it publicly but he had told me privately yeah I agree doesn't work in yeah in under fat
• 40:30 - 41:00 tals it turned out to be one of his last podcast interviews what what did you make of his answer I mean obviously you you already knew the answer but he made it public he made it public he made it public yeah he said in talib's world I mean I'm talking about the real world I don't have own the world I I I'm not a in the world you live in it's also the world the rest of us live in but it showed great inte it shows integrity is uh I
• 41:00 - 41:30 mean uh it shows also no it shows uh realism and it shows also um he didn't want to upset me because he was always scared of me going against them oh okay see right he even though he's not on Twitter he definitely I mean one thing about him I'm I'm certain that he knows everything that was said about him on Twitter okay I mean I'm saying he
• 41:30 - 42:00 he's he does believe he should be up there I'm saying he's normal he himself would tell you I'm I'm I'm normal yeah I told him why did you write a book if you know that uh you have U uh you know a a a loss of version in other words one bad comment hurts you a lot more than a lot of Praise um he at me say I shouldn't have written a book [Laughter] that's funny yeah I don't have the same
• 42:00 - 42:30 loss of version right I I I don't mind uh I have the opposite function oh really yeah I thought few a little bit of Praise from people yeah all right is for me is is is is is is is offsets pages of uh of of hate oh interesting yeah but you definitely have I assume you have loss of version in other no no of course of course but it's not the same kind of loss of version
• 42:30 - 43:00 reputationally got it yeah you see that's my idea of antifragile right because I didn't start as an academic start in the real world yes I mean look at it now I mean I started uh when Gaza started I felt honorable to uh to go in and defend the Palestinians when nobody was defending them it took a while for a lot of people to jump on the train and in the beginning I had probably
• 43:00 - 43:30 15 uh people attacking me for every one person supporting me and now of course has switched because maybe they found it less effective to attack me uh they can't intim people tend to attack those can be intimidated so there's this sense of Honor you know that that sometimes makes you feel rewards from saying something not popular or or risky right worry about
• 43:30 - 44:00 integrity not reputation yeah I mean as as as you advance an age you you go back if you're doing things right you go back to your uh your childhood values you see your childhood values you know or about honor and uh and and think can a stand when needed and and then you continue and every time
• 44:00 - 44:30 you know I take a stand I feel uh it's existential I feel um I've done something right what I'm saying that Danny doesn't have a same representation and someone complained about him among his Circle friend jokingly he said for me uh happiness has different value for Danny is eating mozzarella and Tuscany that's his idea of you honic honic so
• 44:30 - 45:00 therefore he analyzed everything in terms of honic treadmill yeah but after deep down Danny was not like that right he he realized that that was you know not what he was life was about yeah it's more about goals and maybe but he and Val but he was he was an atheist you know that and and the first time I met him he ate Bruto someone said there's not a single
• 45:00 - 45:30 religious bone in my body so uh realize that has different customer right and when you're uh not religious there a lot of good things but there could be bad things that you start you're too materialistic about your view of the world and you're coming here to maximize uh Mas prut it's very different yeah starts to taste a bit boring after a so
• 45:30 - 46:00 if your sympathy towards biases and behavioral economics was something you changed your mind about are there any other big things in the inserto that you think you got wrong or you've changed your mind about no no I didn't change my mind I you read if you know GO RAD the the full by Randomness yeah read it I mean and you'll see that there's nothing it's just has changed my mind about about one sentence about praising that industry yeah okay I changed my mind about the industry but what I wrote
• 46:00 - 46:30 about I didn't change my mind okay okay because I used I used them for some of the ideas I had when there was no scientific literature on them but that didn't change my mind okay I want okay my whole point as I started by humans were idiots when it comes to fat tales okay particularly under you know modern structure because the way we present probability to them and and uh conmen like that but the idea that that
• 46:30 - 47:00 humans should uh I never had the idea that humans should uh avoid one over end should avoid mental accounting should avoid uh oh I don't think exactly exactly I I never so I never changed my belie I never believed in the equity premium puzzle contrary so but I found initially in the industry things to back up although in the industry they believe and and people who hate tail tail tail options keep sighting the
• 47:00 - 47:30 industry right because in that very paper that I like for the convexity of the function uh Conan you know shows that people overestimate the odds you see so I praise that never change my mind on the paper you see I never she it's completely wrong it's only because you're clipping the tail right that that it shows that the the missing Tale shows in the uh in the
• 47:30 - 48:00 probability jumping up well let me ask generally then are there any big things in the inserto that you've changed your mind about import important things nothing beyond the sentence okay okay yeah so so far I've corrected a lot of sentences here and there uh like I've removed that sentence uh I I I said something about tetlock uh that I I qualified and say okay his when he said his study
• 48:00 - 48:30 saying that uh that that forecasting people can't forecast or that's you know the industry uh was was was was okay but not the consequences that every he drove it to weird conclusions you see so I I kept uh you know yeah taken from the industry that people can't forecast very well we can't forecast very well but they never want the next step is that you build a world where you're
• 48:30 - 49:00 forecasting doesn't matter and or you have payoff functions that are convex that where where forecasting errors actually fuel uh expectation as words improve the payoff improves from that all right well let's talk about forecasting so I've got some questions about forecasting and then about the precautionary principle then War then pandemics okay okay so if you had to boil it down how would you describe the
• 49:00 - 49:30 substantive disagreement between you and the broad intellectual project of super forecasting I I just about binary versus continuous payoffs yeah there's one thing with first of all the quality of the project aside from the and the discussions they didn't understand hour because I got bunch of people involved with me and the repli is uh our insults you see so the first no
• 49:30 - 50:00 the first one is binary versus continuous okay and I knew that as an option Traer that the naive person would come in and think a an out of-the money binary option would go up in value when you fatten the tail in fact they go down in value when you fting the tail because the binary is a probability so I mean just to to give you the intuition if I take the the the the gum curve plus or minus one Sigma is
• 50:00 - 50:30 about 68% if I fatten the tail exiting in other words the probabilities of being above or below actually they drop you see why because you have the the the variance is more explained by rare events the the the body distribution goes up yeah the shoulders n exactly you have more ordinary because
• 50:30 - 51:00 you have high high inequality and the deviations that occur are much more pronounced right okay so and that we know so so in other words uh you you you're making the wrong bet using binary options or using anything that c clips your upside that we know as option Traders and rookies usually or people who are not opt Traders sometimes be in economics or something they always always express their bet using these right and we sell it to
• 51:00 - 51:30 them because it's a net of two options so and there's a difference between making a bet where you get paid $1 and making a bet where you get paid a lot and in full by randoms I explained that Difference by saying that uh I was bullish all right the market but I was short how well I was bullish in a sense what do you mean by bullish I think the market had higher probability of going up but is the expectation being short is bigger so these things you know don't
• 51:30 - 52:00 translate well outside option trading and and and of course uh uh these guys don't get it okay and forecasting that the other one is they subselect events you can forecast because but they have they're inconsequential you see they're very small restricted question they're consequential so so so and also
• 52:00 - 52:30 they events there's no such thing as an event like for example will it be a war yes or no I mean there can be a war could kill two people could be a war kill 600,000 people yeah so in extremist that's one thing one sentence mandal bro kept uh repeating to me there is no such thing as a standard deviation in extremist stand yeah you see so you
• 52:30 - 53:00 can't judge the event you know by saying oh there's a pandemic or no pandemic because the the size is random variable let me give you an example if they were you know if you have you know scale that's the idea of having scale free distribution versus uh on scale uh the ratio of people was 10 million over people's 5 million is the same as a ratio
• 53:00 - 53:30 approximately 20 million over 10 million MH this is a par sorry that's searo it's almost how you define it but look at the consequences of that the consequence of that that they they they uh it tells you that there's no standard event right there's no typical event exactly no typical event you cannot say the typical event no large deviation so to give you an idea if I take a gaussian the expected deviation above 3
• 53:30 - 54:00 Sigma is a little more than three sigma and if you take five Sigma it's a little more than five Sigma right it gets smaller it's above zero Sigma is about 0 eight of a sigma as you go higher it get it shrinks it's like for saying what's your life expectancy at zero is 80 years old but at 100 is two years two is 80 two additional years so as you increase
• 54:00 - 54:30 a random variable so whereas an extremist time the scale stay the same yeah so the expected uh life if we were uh distributed like company size uh the SI the the expected company you know as I said what's the expected company higher than uh was 10 million in sales 15 milon million 100 million sales 150 million the
• 54:30 - 55:00 average uh two billion in sales 3 billion all right so it's a same like saying oh he's 100 years old he has another 50 to go how many he's a thousand years old another 500 to go okay you can't apply the same reasoning with humans we know what an old person is okay uh because as you raise that number things shrink for extremist stand as you raise that number things don't shrink as a matter of effect and proportionally they stay the same but but in absolute they they explode okay
• 55:00 - 55:30 so this is why the explosion tells you that there's no uh standard large deviation so and that was Mandel br's uh sentence and and just looking at the world from that standpoint that uh there's no characteristic scale changed uh my my my work better than a
• 55:30 - 56:00 crash of 87 because now I had a framework that is very simple to refer to and they are probability basins so this is why I learned a lot uh you know working with metal bro and people weren't conscious of that uh Stark uh difference uh like operationally this is H I wrote this book statistical consequence of fat tales and this is why I gave the dedicated the Black Swan to metal Bron based on that
• 56:00 - 56:30 idea that that the characteristics scale that I explained in in the Black Swan if you use that then you have a problem with forecasting you see because it is sterile in the sense that what comes above has a meaning see is it higher than 10 million higher than 100 million it has a meaning so this is where I've written another thing about forecasting a paper and I think we insulted the
• 56:30 - 57:00 tetlock only because it's good to insult U people who uh do such work uh and also only insulted them because he spent like five years you know that's why I call him the rat someone stabing in your back so we explained that and I called it uh what do I call it on the U about a single forecast a single forecast Point
• 57:00 - 57:30 forecast right on why one should never do Point forecast for a fat tail variable what was what was the title of the paper single point for cost VAR yeah but I I forgot the beginning on the uh inadequacy or something and and in it I wrote it with chirillo and yanir Maran who then active on covid because we did the data we published the natureal scci natural
• 57:30 - 58:00 physics paper on distribution of uh of uh people killed in pandemic and guess what a tail exponent is it's like less than one isn't it it's half yeah Less in on like the levy infinite man yeah the the it is actually slipped not infinite being some transform becomes infinite being but that is the same with Wars right because you can't kill than kill more than a population but it tracks for a large part of it and if you
• 58:00 - 58:30 do a lock trans for then it's very robust anyway so we were then involved in pandemics and all the people saying oh he's super forecasting that how many people would be killed in the pandemic right and then say no it it's foolish to forecast and it's even more foolish to critique someone's forecast he Mis forecast because 95% of the observation will be below the mean yeah
• 58:30 - 59:00 it's crazy you see so so you know if you have a uh it's exactly like my trading if if 98% of the time you lose money you can say well forecas is gonna lose money this year yeah get the idea it's meaningless actually on that it's funny to think that Winston Churchill probably would have had a terrible Bri score like he was wrong on all these questions like the gold standard Winston Churchill gold standard India gipo that's that's one
• 59:00 - 59:30 that's very close to home for Australians like he was wrong on all these calls but he was right on the big question of Hitler's intentions so he was right in payoff space like when it mattered yeah in payoff space matter yeah he was wrong and the in the small it's like like you you lose the battle and win the war yeah it's like revers of Napoleon yeah Napoleon is only good at winning battles yeah and and he he he won I don't know if you're numerically look at how many battles he won he did pretty well he did well except for waterl right
• 59:30 - 60:00 the reverse Churchill yeah the the the reverse CH and and he's hyped up because I say look how many battles he won they were significant maybe compared to the rest and and he was and and after a while actually he stopped winning them it was became harder because people learned from him yeah so so there's there's one one thing about uh you know frequency space is is a problem because in real in the real world you're not paid in frequency right you're paid in
• 60:00 - 60:30 dollars and cents yeah reminds me of that anecdote in filled by Randomness the trader who I assume is you is simultaneously bullish on the market going up over the next week that was the one I explaining but also short the market yeah that was the one I was explaining and frequency space I'm bullish and uh payoff space I'm bearish so but do these binary for fors have some I agree that the the value is is is
• 60:30 - 61:00 limited but don't they have some value like I feel like if someone ien seen I haven't seen many uh functions because it assumes that you get a lump sum I mean for elections the binary and there's another bias that I wrote a paper on about how to Value uh election to integrate the variance in the price but you don't have a um good understanding of how to translate binaries into uh real world and then we
• 61:00 - 61:30 discovered another thing also with the binary in the fat tailed the variable if you want to get the exact probability you see it doesn't match the the payoff to give an example uh let's say that uh I have a uh I'm I'm good at forecasting uh the rate of uh the rate of uh growth of covid
• 61:30 - 62:00 okay you cannot translate that into how many people will be killed because the the the rate of growth is the rate of growth you see if you have to translate it the number of people you take the exponential rate of growth MH you say wtal w0 e T okay okay and an small error in
• 62:00 - 62:30 R it can be uh cailed but if it's exponential WT will be parto see so you can have infinite uh an infinite expectation on uh on W with a finite expectation in R right this is the problem uh we tried to explain it in that paper it didn't go through so now what we discovered also later on and this also applies to something what I call
• 62:30 - 63:00 the varar Dilemma that people thought we were good at Value at risk and not good at C value at risk is saying okay you have within 95% confidence you won't lose more than a million MH and we thought that I thought it was flawed because that's not the right way because conditional on on losing more than a million you may lose 200 right okay and so so so that remaining 5% is is where the action was but someone pointed out in a group
• 63:00 - 63:30 discussion group or discussing um the answer to tlock and then mentioned that my application of of the exponential transformation also applies for value at risk because he said if you want to get the the probability you know the probability is distributed in cailes MH because but it's pounded between zero and one exactly it isail right okay it's a frequency it's like this is why they have R score all that thing yeah but then the transformation
• 63:30 - 64:00 of that probability okay outside the gaussian okay you have what you have you have the inverse uh Pro you see you want to go from a probability to the to X rather than if you going for probability you see that transformation of course is is a uh con con is a concave convex function right so it is explosive okay you see so in the say
• 64:00 - 64:30 for comparing your approach I guess extreme value Theory to not extreme value Theory okay sorry okay comparing how you think about forecasting or the impossibility of forecasting to the super forecasting approach how important is it as evidence the fact that you have made a lot of money and as far as I can see there are no fabulously Rich super forecasters
• 64:30 - 65:00 yeah well I was was say something that people good at forecasting like in Banks they're never rich I mean they make them talk to customers and then people customers remember oh yeah he forecast this but the there's there's another thing I want to add here about the function if you take a conx function uh and you're betting against it and we saw that we were doing ruy in the same week we had a fight with
• 65:00 - 65:30 the uh Richard Taylor so I showed something that I showed you in ruy yeah that you could if you have a function let's say that uh you're predicting volatility right and you're an option Trader and you're you're that was a fixed thing and the volatility comes steadily all right you're going to break even all right so in other words you're you're uh you know
• 65:30 - 66:00 let's assume that the level of Al break even now if Vol comes unsteadily you lose your shirt I you can move up the expectation by showing that hey you're predicting uh steadily and you make $1 but the volatility comes in lumps because you're the way you can express a bet against volatility is going to be linear it comes in lumps come the other way MH so I said okay I'm 30%
• 66:00 - 66:30 overestimating volatility and I'm making money all right he is buying volatility 30 he's selling volatility you know with a big discount and losing money so so this is where uh I take that function and the function is you have uh you you break even uh at one so you have 51 and two Z you make money but if you have uh six
• 66:30 - 67:00 zeros and one five you lose your shirt MH in square so so you realize that that's my my my thing about there's no uh I've never seen a rich forecaster so if it came to light in a few decades time that super forecasters had been doing really well not blowing up would that update you in favor of super forecasting we're saying ifs okay let me see I mean I I don't like these uh uh conditionals
• 67:00 - 67:30 right so when when when you see super forecasters find a way to make money outside being paid you know to forecast but but like the function makes money then then it would be very interesting okay but I think that that in the real world we view this thing differently you cannot isolate the forecasting from the payoff function right so this is what what my my central problem is and we tried to explain it to tetlock I even brought my friend Bruno
• 67:30 - 68:00 the pier somehow Conan invited us to launch one actually I invited end up inviting but said let's have launch was T like he wants to discuss his super forecasting thing um I brought Bruno deer who's a friend of mine and he's he's a guy has zero one paper the most influential paper I think in and all of the of History one paper nothing else and it was published in a
• 68:00 - 68:30 magazine called risk magazine all right he the guy's you know to talk about he figured out of course uh quickly the difference between uh between binary and vanilla and stuff like that he's any so we had L we realize they don't I mean Danny do make claims but deathlock didn't get didn't even know what we're talking about right
• 68:30 - 69:00 so but there something how do I know if someone understands probability they understand probability if they know that probability is not a product it's a kernel is it's something that add up to one all right so so whatever is inside okay cannot be isolated it's a kernel okay you see it is a a a thing that adds up to Wi like saying densities are not
• 69:00 - 69:30 probabilities MH but they work well within a we even had at some point people using negative probabilities just like in quantum mechanics they use negative probabilities and and smart people understand that yeah you can use NE because it's a kernel okay the constraints are not on on on the inside say on the summation on the raw summation right so when you say but it is a kernel therefore what are these properties okay completely
• 69:30 - 70:00 different so you should look at what you're doing with probability it's on by itself doesn't come alone so you're multiplying within an integral P of X with some function G of X yeah okay P of X by itself has no meaning yeah all right G of X all right has some meaning now if you're doing a binary G of X is an indicator function if x is of 100 zero or one whatever what however you want to phrase it or it could be
• 70:00 - 70:30 continuous could be convex could be concave could have lot lot of other shapes and then we can talk but talking about probability itself you can't yeah you can't separate P of X and talk about that by itself exactly you can't talk about that by yeah that's that's the whole point of it probability density function yeah density not probability yeah for the mass function it may resemble the probability for the frequency to be there but it's just like something that has one attribute that uh it's a
• 70:30 - 71:00 derivative of something that's never decreasing and and and uh and a function that is never decreasing and goes up between zero and one so it's a derivative of a function right because you reintegrate to use it so that's the way you got to look at it yeah so and we we our option Traders don't talk about probabilities we talk about value of the option and the other option is like that part of distribution is valuable because you get a continuous pay off there
• 71:00 - 71:30 yeah I've got some questions about the precautionary principle so I want to I want to stress test it with you or explore its application in practice so I want to get your take on this critique of the precautionary principle so the critique would be something like it's it's it's possible to tell a story that all sorts of risks might be multiplicative systemic risks and ultimately policy makers need
• 71:30 - 72:00 to prioritize between those risks because contingency planning I believe I believe in survival so if you don't take it seriously Society doesn't survive just want to structure those who don't survive don't bring down the whole okay thing okay because I think that the the two things the the precaution Principle as understood and there's what we call the non-naive precaution principle has restrictions on what you got to have precaution yeah about because a lot of people couldn't get it that that like
• 72:00 - 72:30 why are we so uh much against techn we're not against technology we're against some classes of uh uh engineering that have a reversal effect and it was a huge standard error and when I discussed on uh the podcast or the probability book whatever you want to call this were with Scott Patterson discussed the M
• 72:30 - 73:00 story what what callus a great family fine was trying to get rid of sparrows sparrows yeah okay and then they killed all the sparrows or they tried to kill as many sparrows that they could and sparrows eat insects right so they had environmental problem with insects proliferating on the train and and then they didn't see it coming right now you say okay this is case that's clearcut of disrupting nature at a large
• 73:00 - 73:30 scale you see and something we don't quite understand this is exactly what our our precaution is about except that we added multiplicative effects like we don't exerise precaution on nuclear this is why we're trying to the way I wanted our precaution principle to work is to tell you what is not precautionary and for US nuclear was not precautionary why because you can have small little reactors and that one explodes in California doesn't impact one in bot the
• 73:30 - 74:00 harm is localized exactly it's localized so unlike uh pandemics yeah so to focus on technology my understanding is that you you wouldn't seek to apply the precautionary principle to the development of a technology that could pose systemic irreversible risks just to its deployment because otherwise you would
• 74:00 - 74:30 be going back and and like setting fire to mle's he plants because that knowledge could ultimately lead to GMOs so there's obviously got to be a l we're against uh uh PL implementation of GMOs in nature we're not against research about whether you can modify okay but you can't stop research you can't people you know can do rese building there yeah got it so applying
• 74:30 - 75:00 that to artificial intelligence obviously as the technology currently stands it doesn't warrant application of the precautionary principle because it doesn't impose systemic harms yeah if we got to the cusp of the technology being able to recursively self-improve which the most plausible way that would happen is that we could use AI to automate AI research itself I I I have problems with discussing AI in terms of precaution
• 75:00 - 75:30 because I don't medly see anything about AI why you should stop AI that it will self- reproduce given a robot cannot climb stairs so you're afraid now know you're afraid of robots scared of robots multiplying and becoming a robot Colony that would take over the world I mean these things are stres of imagination we have bigger problems to worry about I don't think most people
• 75:30 - 76:00 who think about AI risk V robotics is a constraint so what is what is because technology would would the whole thing would become risky as technology becomes autonomous right so in other words uh that's my my understanding that that's that's what they're worried about okay and and becomes autonomous it has to first of all you can shut down your computer all right and and it no longer impacts our our our our life here you
• 76:00 - 76:30 know can't hit the water because it's down the computer the other one for it to be systemic and taking over the whole planet the information systems I mean it's very strange that people could understand G threat are now obsessing over AI because it's tend to surprise them when they uh when they ask it a question it t to if you're surprised by AI you you you have a problem okay mean
• 76:30 - 77:00 maybe this for me an intelligence test figure out what AI can do or cannot do okay there's a lot of things it can do that helps okay okay but for it to become a um uh how can I autonomous in other words a colony of just like humans like biologically equivalent to human you see so have so many steps to make yes but all that needs to happen is the first major step is it needs to automate AI
• 77:00 - 77:30 research itself and then as soon as it can make itself smarter through recursive self-improvement all the other problems like robotics become much easier to solve okay let's see if it can do that could if it could let's worry about let's worry about it then you put constraints you can't put constraints ahead of time okay on a research you got you got to worry about an event happening okay right you got to see what talking speculatively okay one one quick final side note on AI a lot of people
• 77:30 - 78:00 have remarked on the fact that llms haven't produced any original scientific insights yeah and that maybe because they're fundamentally um gaussian uh have you thought about no no it's not that's not the reason it's because they are the they may actually produce Insight uh because of the randomizing uh stuff and may make a mistake one day right but so long as they don't make mistakes just representing what's out there yeah it's a probably weighted thing okay as a
• 78:00 - 78:30 matter of fact it's a reverse of scientific research because how does uh llm work it works at reflecting what makes sense all right probabilistically so I try to trick it by asking it you saw on Twitter in the beginning say okay how I'm going to trick it because that's if you know how it functions and and again thanks to U my uh genius friend Wolfram I got how was it there I got
• 78:30 - 79:00 this blog post he sent me I read it and I got the book said okay now I know it works all right it works by probably matching by the way right it doesn't give you the same answer all the time and it's not going to do all the homework so it it it doesn't have to connect the pieces directly so use probalistic methodss so reflect of consensus so you I asked it uh during the the Congress of
• 79:00 - 79:30 Berlin there was a war between the otoman Empire on one hand and then you had Greece on the other hand among other allies and there was a fellow uh K theodori who the father of the mathematician Cara theodori um who was representing you know someone there who did he represent they say oh he's a foreign affairs minister of
• 79:30 - 80:00 Greece you see it's not like a search engine giving you facts it is using probabilistically how things occur uh together he has a Greek name therefore he in fact he was representing the other side the opan Empire MH as a matter of fact it was uh I think there Victorian days that he said oh uh meeting with a representative of the Muhammad and world was an article in the times and
• 80:00 - 80:30 his name was that Greek name I think is it Constantine kodori or his son is constan whatever so I asked chpt made that mistake well so how do you make money in life how do you really improve how do you write a book how do okay think people didn't think about because if you're if you're going to start a business that makes sense guess what someone else thought about it okay
• 80:30 - 81:00 I think something and and chpt is designed to tell you what makes sense based on current information right not look for exception there may be a possible modification I don't know to make sure GPT only tell you what make what makes no sense right and and that would hit one day but it's like our usual addage Universal is if if you have a reason to buy an option don't buy it yeah because other people will also have the same reason yeah okay yeah so it's
• 81:00 - 81:30 the same thing with starting a business you're not going to make money on a business that makes sense yeah because a lot of people have tried it maybe some Pockets here and there people have tried it say so the the the idea of of CH GPT coming up was genuine insights okay is exactly in Reverse of the way it was modeled mhm and I like everyone it was vague for me until I saw the book by uh by uh by Wolfram but
• 81:30 - 82:00 couple years ago two summers ago or last summer it was it was the guy is very clear he thinks like he's very systematic and extremely intelligent yeah I never met anybody more intelligent than him yeah I did a 4 and a half hour podcast with him last year yeah in Connecticut and it was one of the most surreal experiences I've had really the guy is you write down the formula he gets it right away he
• 82:00 - 82:30 understands things uh like effortlessly yeah and he's his intellect isn't domain dependent he's he can apply it across all aspects of his life yeah he had I I I mean I I don't know I don't want to but like he he thinks about business really well as he has a he has a business yeah yeah but he's uh regimented right in the way he operates and collects data on himself sorry the way he collects data on himself yeah no he's uh but anyway so he's um I mean I I
• 82:30 - 83:00 I enjoy hiking with him yeah once a year um and I anyway so so so thanks to him now you have an idea how these things work okay it was clear I mean maybe there's some other text but but if when when I if I need a text I'd rather read his treatment yeah because of the way uh I got used to thinking and also because I don't haven't seen the quality
• 83:00 - 83:30 elsewhere yeah it's a great book his uh primer on llms so in the same I have some questions about war some questions about Co and then we're finished yeah so one of the deepest things I've picked up from you in recent times is the concept of the Shadow mean and I guess the intuition here is that we have some historical data for some phenomenon whether that's Market drawdowns or deaths from war or death from pandemics and those data can appear to follow a thin tow distribution but it's
• 83:30 - 84:00 naive to assume that the process that's generating them is actually thin tailed because in the background behind the curtains of reality could actually be a fat tailed process that's generating the data it's just that it takes a really long time for extreme events to show up so fat tail distributions can masquerade as thin tailed ones and bringing this to statistical moments the mean of the data we've observed is better thought of as the sample mean and you have this approach where you work out what you call the
• 84:00 - 84:30 shadow mean which I guess is equivalent to like the population mean that is the mean of the process that's actually generating the data um and you've done this for Warfare and I want to talk about that specifically but just first generally for others who may want to explore this approach can you outline the basic steps in your process is it number one estimate the alpha number two PL so let's explain U to the viewers what uh or listeners what do I mean by sh
• 84:30 - 85:00 mean let's take a one tail distribution okay you have the visibly in a sample of 30 observation you're not going to get events that happen less than 1% of the time you agree yes so for a gaussian it's not a big deal because these that happen and less than 1% of the time have less impact on the probability gets increasingly smaller so it doesn't
• 85:00 - 85:30 matter much so with a small sample it don't have a big Shadow mean effect actually with a gaussian it has to be a one tailed gaussian so so low variance like normal right like height okay yeah so you observe a bunch of people and you have an idea what what the average height in town is okay now when we talk about things that are open-ended
• 85:30 - 86:00 and fat tailed okay visibly most observation will be below the mean MH so when you compute the mean it's going to be biased down yeah from uh empir what they call empirical observation yeah so the empirical distribution is not empirical and that's what is central for us yeah so I take the S&P 500 and uh you can figure out that uh the the if you want to stress test it over the next
• 86:00 - 86:30 X days taken the past 10 years low the past years worst deviation past 10 years is not represented because of insufficient sample as you go further in the tail you take Industries like biotech for example it is a heavy tailed industry so What You observe is less than uh I think I wrote it in the blacks Swan The
• 86:30 - 87:00 observed mean underestimate the true mean whereas for insurance it overestimates the true mean right for banking because one is to the right one is to the left uh so I uh looked at what has a positive Shadow mean and one has a negative Shadow me if you're selling
• 87:00 - 87:30 volatility you have a shadow mean that's going to be way lower than your your observed mean if you're talking for Wars even without surviv sh bias which is another story we have a uh the process that vly ner than what we observed about three times NOA okay three time next year yes so in other words um the the the historical
• 87:30 - 88:00 process underestimates the true process and and we published in uh we published about the shadow mean in in in in various venues we have a paper in U in phys a on Wars but we applied it in quantity Finance to uh operation loss mhm I published in Journal called quantitative finance and and we applied it to other domains but that's an idea that I wrote
• 88:00 - 88:30 about in the Black Swan but tell where is the invisible because visibly the by definition the 100 year flood is not going to be present in fiveyear data okay so you have a shadow mean if you limited to five years yeah so the the other big innovation of the work that you did on war was this concept of inter arrival time mhm and if I remember correctly the the
• 88:30 - 89:00 waiting time for Wars with deaths above a threshold of 10 million people is bit over 100 years yeah and that means that big because we haven't just because we haven't observed any like the last the last conflict with deaths of more than 10 million was World War II nearly 80 years ago now but we can't infer from that that violence is deing decline plus another thing that we discovered that's very robust is inter Ral
• 89:00 - 89:30 time is has an exponential distribution like a p you know the inter Ral time of is uh it means it's memoryless right in other words if it arrives on average every say 100 years and then we haven't had one in 100 years okay you don't say oh it's coming it's memoryless so you wait wait another 100 years the expectations stay the same
• 89:30 - 90:00 yeah yeah so what structural explanations do you think are generating the fat tailedness of war is it just the development of increasingly destructive Technologies and then maybe also some globalization and the fact that violence can spread mimetically I don't I mean I I I I looked at the data I reflected the data violence is about decline I did not put my concerns and my concern is that in the past to do what's being done in Gaza now
• 90:00 - 90:30 required much more so we have a lot more destructive uh the ability I mean to kill is greater in the past uh you know would take a long time to kill you know so many people yeah do it manually yeah and now we industrialize the process which is very sad yes and then I have I've started branching out not to foreign policy realizing that that effectively there's
• 90:30 - 91:00 some things in that sjd Society of judgment decision making when they analyze the Vietnam War and and there are lot of lot of good things in that that uh in that industry and uh all the biases you realize that we have the United States the most dyn country very vital was completely incompetent State
• 91:00 - 91:30 Department so you realize the decision for War I mean think of Afghanistan how naive it is not to figure out what's going on so it's going to make mistakes of course more mistakes of course and these alliances like you back up not understanding consequences so sort of like M sparrows you back up Bin Laden not realizing that that you help and let you build a machine that will turn against you right it's like the Hydra like the Hydra you cut off right back no
• 91:30 - 92:00 no but but but they create so a interventionist foreign policy yeah on the part of the United States and then over spreading democracy or stuff like that is actually more dangerous than just isolationism so the culture is very different today right which is why you know outside of our statistical world work had to say that there's this incompetence dressed in sophistication that makes the world more
• 92:00 - 92:30 dangerous so then if we go if we move back through the historical data the wars become less fat tail As you move into the past no the the the fatness is the same what we call the scale MH the alpha doesn't change the scale changes so I think one of the things that you and Professor Pasqual too found was that in the past death counts were exaggerated both
• 92:30 - 93:00 because conquerors and victims had incentives to exaggerate obviously the the conquerors want to appear more intimidating no no what no no this I made this comment later on after looking at the data by because when we analyze past Wars we we try to figure out a robust way to look at the structure of of uh the random variable by taking for every war different
• 93:00 - 93:30 accounts and then randomizing between the high and the low say algeria's War the French had 280,000 for example theeran had 1 million since then everything had been revised so we took both numbers and randomized so we created 150,000 histories between all the numbers that we had with permutation from within the high and low estimate and we figure out that boom they all give the same Alpha
• 93:30 - 94:00 right so we were we uh we but the motivation was that people lie about numbers and do that is that true and and and and and ours is to remove the effect of of of different estimates of them or their enemies you see okay so aside from that in a nonprobabilistic way I myself observed that a lot of people like to exaggerate their their killings yeah like gen ishan
• 94:00 - 94:30 because it was optimal MH you know you don't have if people think that that you're going to kill a lot of people they won't oppose you so which is why you do a lot of uh stuff for sure yes a lot of Devastation for sure yes that makes sense victims exaggerating their suffering was less intuitive to me but then I remembered you know Tom Holland's work or Rene Gerard's work or even your treatment of Christianity and skin in the game I realized what makes Christianity unique is the valorization
• 94:30 - 95:00 of the victim and Christianity and Sh Islam right the only the two religion yeah that uh that that that have this um glorification of victimhood yes which is is chanan sh Islam yes Islam when they have a martyr you know like and there still been after the murder of uh Hassan and Hussein you know 1300 years of
• 95:00 - 95:30 mourning or stuff like that a glorification basically for for just being killed yes so I was wondering if if the glorification of victimhood if the spread of Christianity is um maybe what was driving the exaggeration of death counts on the victim side I I don't know we we don't have good records of what happened in the period right before Christianity dominates simply because we had a big transition and the history is written by
• 95:30 - 96:00 the winners of course by the Christians yeah so we don't have a clean record what happened before but we know that there are some purely uh uh invented fabricated series of events of um of martyrdom in uh in what's now North Africa and and Southern Mediterranean and Roman Southern Mediterranean yeah so we know a lot of
• 96:00 - 96:30 them existed and we know a lot of them these thingss didn't exist or exist under the same story in 17 different places right or seven different places right so we know that either existed too much or did not exist yeah yeah so one of the implications of your work on war with pasal is that that because of these inter arrival times we really should wait about 300 years
• 96:30 - 97:00 without seeing a conflict of the scale of World War II yeah if you had to wasit 300 years then you'd say oh the the distribution has changed yes then we could say but we have had no information statistically from the past 80 years yeah and that was the the thing about the Pinker thinks that uh the world has changed and he couldn't understand our insults just like tlock they couldn't understand the statistical um uh
• 97:00 - 97:30 claim yeah against that yeah so you think that I mean it's possible that the data generating processes could change it's just that we haven't seen anything that would overturn the null hypothesis exact that exactly that exactly the point that's one way to look at I don't like the null hypothesis story yeah because that's for mostly for Applied statistician working and
• 97:30 - 98:00 and in the medical lab all right and or or psychology department but but but that's pretty much but the TR of it is there that's the intuition yes yes and so we have we have no statistical grounds on which to say violence is declined none yeah yeah that's that's it's and we didn't even go the Second Step I se it has increased which is what I saw but I don't want to make that point statistically yeah well it's it's um
• 98:00 - 98:30 super interesting and important work I want to talk about Co so oh actually so one one maybe can I just ask you one technical question on the the war stuff before we move on so I'm not sure if this is an interesting question but let me test it on you so generally how much does it change the conclusion of analyses like yours with Pasqual on war if you impose soft
• 98:30 - 99:00 ceilings like 8 billion deaths zero okay because you stress tested it for war no no no that that soft ceiling yeah you mean is is it's only an artifact yeah okay to show that it in log space it is a power law okay but but you have to go very up to to five five billion doesn't make a difference whether it's ceiling or no ceiling okay for both yeah doesn't make a difference because the ceiling is
• 99:00 - 99:30 continuous it's like a log function that turns uh uh the max into Infinity okay okay but but you as it only happens asically okay okay all right yes I want to talk about covid so in late January 2020 you wrote a memo with yanir a mutual friend yeah it started uh yeah and I mean we yanir and I were
• 99:30 - 100:00 concerned about ebola before that yes back in 2014 yeah we we were obsessing over pandemic because I wrote on the Black Swan yeah and it was picked up by a bunch of people in Singapore so we were like all concerned about you know the big pandemic because it would travel faster than the Great Plague yeah so this is why we were very concerned when it started and you wanted to invite people to kill it in the egg
• 100:00 - 100:30 yes and you wrote this memo which was then shared with a friend in the White House yeah can you tell me the story of that and is there anything you can share that you haven't shared publicly before no no it's that doesn't the the the the the paper by itself is meaningless because we would have written one and you're and I uh separate but the there was no no particular um novelty to that idea sure but when we start seeing what's happening in China realized that there was a problem and then I start
• 100:30 - 101:00 looking at at at ways to how do you mitigate something that's fat tailed you lower the scale how do you lower the scale by by by cutting off the distribution of the parts MH okay reduced connectivity reduced connectivity and uh it's very strange that the Trump Administration did not they they I mean they spent all this money all right on giv money handing out money all of that it didn't hit him that that you're most
• 101:00 - 101:30 effective by having uh controls at the border or you test people I mean that in the past we used to have very effective lazarettos where people were uh confin the quarantines and and now we can do it more effectively was testing do you think your memo with yir is what convinced the White House to
• 101:30 - 102:00 close the borders to try care less on White House okay something disgusted me about the Trump Administration uh the the that that I don't want to you know you just do your duty and you move on do you sense that governments and policy makers saying the US have gotten any better at thinking about how to deal with tail risk no I think if anything their effort to deal with risk increase their risk because you end up with people like C sunstein
• 102:00 - 102:30 and these uh Pizer are call them they make you stupid for worrying about thing because their textbook tells you they shouldn't worry about it and they don't understand fat tales once you understand fat tailes Things become very easy start thinking differently about AI differently about other things see you tell me I tell you yeah once AI stops multiplying let me know right and stuff like that this is my department
• 102:30 - 103:00 fat tailes and precaution requires fat tales yeah I mean you could have precaution at different levels but the one we're concerned was at a higher micro level with price fat tails do we need any new social institutions to better deal with fat tails I have no idea okay at this point I'm too disgusted with these uh bureaucrats and the way they hand on both sides V negative or is the way they exactly I mean you want a simpler world
• 103:00 - 103:30 yeah creat a complex World institution that make it more complex sort of like you as foreign policy you you go to Afghanistan then you have to handle Afghanistan so it's like you get involved into a series of uh of uh feedback loops you never thought you'd get into yeah so in the same I'm finished with my main questions I had a few random questions let's continue yeah it's just a random sampling of different
• 103:30 - 104:00 things I don't do the podcasts and interviews so anyway well I very much appreciate you speaking with me so okay what's the biggest thing most people in social science get wrong about correlation that's an important question they don't know what it means there I mean there are SGD people who really think that um experts have a problem and and and they are good results there and uh they they they uh
• 104:00 - 104:30 ask the people do the regression MH what what does it mean and they can't explain their own results they know the equation they couldn't explain the graph how much this represents that so uh there there are a lot of incompetence and and and social science and they use metrics they don't understand H people lot of people thought correlation was was an objective
• 104:30 - 105:00 thing measure that depends on some sample and then has a very limited meaning and also they don't realize that when you do visually that correlation with 50 is not halfway between zero and one it's much closer to zero you have this saying so people are familiar with the phrase correlation isn't causation you have this phrase
• 105:00 - 105:30 correlation is in correlation exactly I had a lot of Twitter fights with with with people and that was fun because I didn't know that people didn't think of correlation that way the uh that's another thing if you look in in in finance naively you see that uh the effect of the mean of correlation appears to be like uh say X and Y are correlated your your expectation of
• 105:30 - 106:00 Delta X is going to be row you know uh Sigma X over Sigma y right based on uh time Delta y right you're linking You observe the effect of X based on observation of Y but for betting and decision making it's not that it's more like uh a a fa Factor uh that's uh something like row Square
• 106:00 - 106:30 1us row square or like similar to the minus log 1us row Square uh so in other words very nonlinear in other words low correlations are are noise and uh and again 50 is not halfway between zero and one right and and one is infinity that's for decision making as and you put that into your your uh either Kelly Criterion or any form of decision making and then you realize how
• 106:30 - 107:00 much more I should bet on on X knowing y or on something knowing some side information and samplified when I I made a graph showing how it has this uh how visually you can see it you see you can Mutual information which is an entropy measure is V more informative that's in a linear world and now you go nonlinear visibly if you have a v curve zero
• 107:00 - 107:30 correlation and infinite Mutual information so there's that mistake was correlation but there are other mistakes in correlation not well explored I didn't go into it because I'm to cycling I'm I'm too lazy to go into it but I I showed that basically it's not um is it's not it's sub additive right give you an example if I take a correlation of a row it's not going to be a row in a
• 107:30 - 108:00 positive if you sum up the quadrants positive you know uh X positive y positive x negative you sum up the quadrant you don't get row because visibly the mean is shifts according to every quadrant so it's going to be sub additive in absolute terms which is a problem it tells you that subsampling taking a correlation sub sample that would give you a correlation of the whole and that's not well well known and I I wrote a paper I
• 108:00 - 108:30 don't feel like publishing it because uh the problem with referees is it's hard to get good referees so on the last paper we had a guy says telling me uh I'm substituting correlation with mutual information and say do you have evidence uh that correlation is a metric you don't say you have evidence scientific evidence that correlation works it is a metric right by definition so you can use it for evidence so so so I said okay you
• 108:30 - 109:00 got to give up on publishing too much because of contact with referees who are not sophisticated unless you find the journals that have the good referees so maybe I'll I'll publish these results because the implication the Practical implication is mous and maybe I'll put it here second Ed Third Edition I add correlation smart people get it smart people but you have to know Mass to know that correlation is
• 109:00 - 109:30 not yeah what what what what what what it means right and then you regression the regression was an R square of or five they don't realize they think yeah anything above 0 five is kind of celebrated in Social I see but the problem is if you include model error okay it dilutes a0 five big times right it's crazy I mean there's just so much of social science is built on Cor is so h plus the other thing is how to
• 109:30 - 110:00 translate a result let's say that you see papers you see a huge Cloud okay and it tells you oh look IQ and education you or IQ and wealth right okay very good or IQ and income first of all it's wrong income is fat tailed IQ is by Design cailed so you can't regress yeah but let's say we did that they got a big noise in other words if you hire someone based on IQ such you know you you get such a low
• 110:00 - 110:30 probability in your favor for sample of one you need a lot large numbers they don't get it so you know all you should hire or no because the the with such a weak correlation the law of large numbers okay doesn't start acting till you hire I don't know whole pound or something you see you get the idea so so you're you're getting noise right so that metric is
• 110:30 - 111:00 noise unless you you're you have a wholesale yeah because of IND V visual variations yeah so within so so the way the law of large number works I explore it here even for thin Tales right it's misunderstood what I call the reverse uh law of large numbers if if you take a property say how much hypertension is going to be lowered by this medication right and reverse it and look at what
• 111:00 - 111:30 are the odds of working on your patient you got a complete different answer from the one they think because on average it works say four points but but some people it's going to be a lot higher and so forth so this is where uh the the interpretation of the statistical claims that they remain it can be messed up right I mean I saw in the IQ first of all they they don't know how to compute a lot of things and
• 111:30 - 112:00 they don't know how to read correlation but also how they interpret it because tell them okay put a graph with a noise and you see a graph and you realize at the best of their claims in these paper that show the effectiveness of using [Music] IQ even you know with the circularity in fact that if you have if you're good at taking exams you're going to have a high IQ but you're also going to get a good college degree and that helps your income in the beginning right we're not
• 112:00 - 112:30 talking about wealth or stuff so it's for employees so even taking all of these you look at the cloud and say well you know what you can't use it for any individual hiring see you need a batch MH and then they start there a lot of other things in in IQ that tells me that either these people I used to think that they're uh mean like in other words like a lot of race science comes
• 112:30 - 113:00 from people being uh you know having some kind of problem all right sociopathic problem so I thought that but I think no that's just plain dumb and then and and and you can see it in the real world think about it if these people know anything they go make money and then will continue doing psychology but they can't it's very true okay next random question yeah maybe you know the answer
• 113:00 - 113:30 to this maybe not but historically culturally how do you explain the perspicacity of the Russian School of probability what was in the water in Russia no they they I mean schools emerged when he start having norms and and groups of smart people together and there's a depth in a Russian approach to to mathematics but during the Soviet they
• 113:30 - 114:00 had to make themselves useful because science had to contribute to society so they it can be remarkably practical while at the same time there's that constraint and I mean the the they were the a lot of it is French as well I mean when you look at the big results okay you always have a you know we have a combination of uh but I think the Russians have contributed the most of probability followed by of course uh the
• 114:00 - 114:30 French and the of course English school of probability is just like gton and uh all these regression all these things that are bad come from this English school probability and usually they had agenda that goon wanted to prove that Irish were stupid measur Ukrainian right and the linear regression the hypothesis testing the Fisher thing all that all these are completely different from yeah the but
• 114:30 - 115:00 there one is probability the other one is is what we call standardized statistics but you cannot go non-standard statistics without knowing probability so we have a class of people who can only use gaussian and and and the I have this theory that every single problem needs a new class of estimators adapted to the problem seems like a pretty good
• 115:00 - 115:30 heuristic yeah so so if you don't know how to redo an estimator how to redo the theory yeah you see the only thing in common is a lot of large numbers that's it right and you want to know what it applies to so when you ask me something about the alpha the law of large numbers sometimes works a lot better for the alpha than that's for the mean yeah because the the um the T exponents follow with int distribution right it follows an inverse gamma distribution and you you get it it's the process is a
• 115:30 - 116:00 specific type of th yeah yeah yeah if you get it if the process is clean yeah okay you have a a it's remarkable how quickly you get the alpha yeah I show you at Ry reverse try to get the means all over the map yeah you get the alpha always within like yeah it's really NE it's really neat yeah standard ER on the alpha is low yeah that on mean is huge yeah yeah so uh you think you think Hayek's knowledge argument can't support prediction
• 116:00 - 116:30 markets and obviously hak argued that knowledge was Consolidated through prices and arbitres trading products services financial securities yeah is the principal difference there just that these things that hyak was considering were continuous and that logic can't be extended to aggregating Binary forecasts or what's the no the difference idea is that no it's more it's explicit versus implicit that for him knowledge is not
• 116:30 - 117:00 explicit that is implicit the difference between uh knowledge that can be uh uh taught and and and formalized and knowledge that is embedded in society and that one Express itself from the manufacturing and then the end price and why a systematized economy your system izing something that is not uh explicitly uh LED itself to explicit phrasing is is is
• 117:00 - 117:30 what uh what harmed uh the Soviet okay so I would not uh I I would not I would say that this applies to probably the wrong way for you that using a probabilistic model is trying to be systematic about things are too rich for you to express them systematically okay you see so in other words his knowledge is what's embedded in society not what is
• 117:30 - 118:00 formalized otherwise the Soviet would have taken the formula and applied it okay maybe I'm maybe I'm too slow today but so how does that how does that preclude extending the knowledge argument to prediction markets because we're not just talking about prediction we're talking about function of predic okay they're all embedded you can have what appears to you bad predictor in frequency space but the function turns out to be better M got it see and the
• 118:00 - 118:30 you don't know the functions you don't know the it's still systematizing something that that should be yeah you know not I mean you should look at the result of the process not the exact yeah replication of that process in lab environment yeah okay I'll ask my I'll ask my last random question so I know that generally you prefer mean absolute deviation to standard
• 118:30 - 119:00 deviation why has standard deviation become such a traditional measure like historically how did that happen Okay because uh I think I discovered here a paper by Claim by fiser I think right who found that in the gassian case it's more efficient than uh mean absolute deviation yeah uh yeah because again I mean to tell the viewers a lot of people mistake one for the other standard deviation is the square root of the sum of the average
• 119:00 - 119:30 some squares it's not you know so it doesn't have a physical intuition yeah is what a standard deviation is what is med is the average so for example if you have the process right with all the observation at zero and uh and one observation at a million for an average of a million this the standard deviation be 500 times being deviation right and the gaussian world is about 25% higher like Square uh
• 119:30 - 120:00 square root of uh you know the usual S2 over Pi is mean deviation of standard deviation the so this is I would I would I would say that that's another basic thing that a lot of people we wrote a paper people don't know what they talk about when talk about vity because they would use we're talking about people who are
• 120:00 - 120:30 practitioners and people who are students PhD students mathematics of finance and then we asked them to try to interpret some kind of financial data where you're showing standard deviation volatility and then they would give you mean deviation interpretation mhm so yeah yeah it's more intuitive than standard deviation yes yeah no so there and there's a wedge both of the fat tals the way I'm interested the measure not because of
• 120:30 - 121:00 you know to pick on on practitioners who make mistakes but but because the ratio stand deviation me deviation is uh the best indicat of fatness yeah see yeah and for gaussian it's I said 25% higher for for for for Koshi is infinite yeah not infinite I mean for something that has not Koshi anything with with an alpha
• 121:00 - 121:30 below two MH it's going to be infinite MH because one is infinite the other is uh fin final final question is there is there anything you can tell me about your next book the lydian St I have no idea what my next book uh what what shape will take for last uh three books uh last two books scin and game and and this one uh I had no conversation I was just finish the book yeah and I don't
• 121:30 - 122:00 like this you know you got to write a plan people got excited they book stor all that I'm I'm working now on on really uh leaving good words so next book has to do with time with time scale and uh and and probability okay there's a lot of entropy uh stuff in it but but I'm I'm at a point where I'm writing for myself now what what makes it most fun that's cor and there's nothing more fun than this because you know an hour two
• 122:00 - 122:30 hour day of math you feel rested after that yeah you see whereas so so I'm doing more math great well I wish you much more math and much more enjoyment yeah but I'm not I don't want to be identified and and I don't I'm agree to say I'm a mathematician I'm just enjoying using it for problems that are non- mathematical in nature so it's not like I'm trying to improve the math I'm I'm using it but
• 122:30 - 123:00 Math is fun and relaxing yeah so this is why I like it yeah well in the same you've been so generous with your time thank you so much it's been a real honor thanks thanks thanks for inviting me and and hopefully uh next time we do a podcast you reverse you start with random question and then you go to structure okay sounds good that's more thanks bye everyone thanks n same