#28 Michael Mauboussin: A Decision Making Jedi
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Hello, listeners. Welcome to the Farnham Street podcast called The Knowledge Project. I'm your host, Shane Parrish, the curator behind the Farnham Street blog, which is an online community focused on mastering the best of what other people have already figured out. The Knowledge Project is where we talk with interesting people to uncover the frameworks that you can use to learn more in less time, make better decisions, and live a happier, more meaningful life.

On this episode, I have Michael Musabin. Michael is one of my favorite people to talk with. This dude is a true polymath. He's the director of research at Blue Mountain Capital Management. Prior to that, he was the head of global financial strategies at Credit Suisse and the chief investment strategist at Legg Mason Capital Management. He's also the author of three books, including one of my favorite multidisciplinary reads, More Than You Know, Finding Financial Wisdom in Unconventional Places.

As you'll see, when the two of us get together, we geek out over decision-making. This conversation will not disappoint.

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You were the first podcast guest we ever had on The Knowledge Project, which was almost two and a half years ago. Bring me up to date. What have you been working on in the last few years since we chatted last? Well, Shane, I think it's a lot of the same stuff. And really what I've been trying to do is deepen some of the themes that we've been working on. So just a couple examples.

One, I think we talked briefly about some of Kahneman's work on the inside versus the outside view and really the application of base rates to improve the quality of forecasts and decision making. And in that regard, we really try to do some deep dives, especially in corporate performance, to understand a large sweep of history in terms of corporate performance for sales growth and growth profitability, earnings growth and so forth.

Some of the more recent stuff I've been working on that's fun, I've been doing a lot of research on the concept of comparing things. And it turns out, right, comparison is actually something that comes quite naturally to us as humans. And it's been a deep study of cognitive psychology for decades.

But not surprisingly, we're not very good at it. So that's another area where I've been doing a really big deep dive. But more of the same, a lot of work on decision making. But again, thinking about the sweep of things from how are markets efficient or inefficient, thinking a lot about still issues around valuation and thinking a lot about what makes for a good business and sustainable competitive advantage. Wow.

Where have you landed on with base rates? And talk to me about a little bit of the nuances that you've come up with in terms of how you apply them to make more effective decisions.

Yeah, it's really super. So just to take one step back, the concept here relates to a notion that Danny Kahneman popularized, but it's been around for a while. He calls it the inside versus the outside view. So the inside view basically says, if I present a problem to you, the way we all tend to cope with it is to gather lots of information, to combine it with our own experience and inputs, and then we project into the future. And almost all problems

You know, we deal with in that fashion. And what psychologists have come along and said is, you know, what you should probably do is start with something called a base rate and basically asking the question, what happened when other people were in this situation before? And I should mention, it's not a natural way to think, right? Because you have to find the base rate and defer to it. And you have to sort of leave aside your own experience and inputs. And we all are sort of slow to discount our own views of the world.

So that's the basic idea, and I think there are a couple things that come out of this that are extremely rich. The first is how you should weight the inside versus the outside view. And the basic heuristic on this is that if there's a lot of luck, a lot of randomness, you should rely exclusively or almost exclusively on the base rate.

If there's a lot of skill, you should rely almost exclusively on the inside view, right, on your own experience. And actually, most activities in life are between those two extremes. And so you should blend the two in some appropriate measure.

Now what this introduces immediately, if you think about it for a moment, is a framework for not only understanding regression toward the mean, but also quantifying regression toward the mean. You go talk to the investment community, even like in athletics,

Everybody understands the notion, at least at a high level of regression toward the mean, but very few people know how to operationalize it. And this actually lays out a very specific framework for operationalizing it. Let me just give you one kind of example to try to make it slightly more concrete. In corporate performance, two measures that are very common for people to look at are sales growth rates and earnings growth rates.

And it turns out that sales growth rates are a lot more persistent. So you might say more indicative of some sort of underlying skill, perhaps related to the industry and so forth. Hence, they're much more predictable. So you don't have to regress growth rates as much as earnings, which themselves are actually not quite random, but quite close to random, where regression happens really, really quickly.

So, to me, it's first trying to get your arms around the idea of base rates, identifying the data, but then understanding how to intelligently integrate the inside and the outside view. And again, with this eye to really understanding not just qualitatively, quantitatively,

How do I find out where I am on this luck, randomness, skill continuum? I mean, as a knowledge worker, I would think that I'm prone to believe, maybe over-exaggerate the skill that I'm bringing to the table, and thus I would discount the base rate in favor of my own skill. Right, exactly. So there's a whole range of approaches to doing this.

I'll mention that there is a statistical method, so I'll start with something that makes my life easy to explain, but a statistical method to do that. And it's based on something, a formulation in statistics that can be very useful. And this formulation that says that independent, actually independent, the standard deviation of independent distribution A squared

plus the standard deviation of independent distribution, B squared, equals the standard deviation squared of A plus B. So basically, it's called like the Pythagorean theorem of statistics. You say, all right, what the heck do I do with that? And the answer is you can apply this, for example, to professional sports leagues.

So let's take an example, say the NBA, National Basketball Association. You say, all right, well, how would I apply this formula? Well, you actually know what the standard deviation or the variance is of the win-loss records of all the teams in the NBA. So that is a known. And then you also can estimate through binomial distribution what the league would look like if it were totally dictated by luck, right? So instead of the teams actually playing, they just flipped a coin, right?

So by inference, you can then back into the variance of skill and see how much skill contributes to the outcomes. And, you know, by the way, you can rank professional sports leagues on this.

And not surprisingly, probably the NBA is the most, let me say it more carefully, the NBA is the sport that's furthest from randomness, furthest from pure luck configuration. And sports like actually ice hockey and baseball are actually quite much closer to being random. So that's one thing. Now you might say, hey, as a knowledge worker, I can't, you know, I'm not getting win-loss records and so forth.

There are some other things you can do as well. One example is how predictable is your field? How good are experts at anticipating particular outcomes? How much do experts agree? Because usually if there's a lot of skill or a lot of very little luck or randomness, experts will come to the same conclusion about similar topics.

So there's some other more qualitative ways to do that, but those would be some of the things to think about. And I'll also say one more thing that's really interesting about organizations. This is my observation. I don't know that I can prove this, but my observation would be usually when you're a lower-level worker,

your job actually tends to have a higher skill component, right? So you're doing something, you're an accounts receivable clerk and you're collecting accounts receivables, right? We can measure what we're doing. It's pretty much skill-based. But as you move up through the organization, it's not uncommon for the frequency, the types of decisions you make to go down, but the importance of the decisions to go up

and also the luck of the outcomes related to those decisions to go up. What's also ironic, of course, is the remuneration tends to be higher the higher you are in the organization. So CEOs are often making fewer sort of big consequential decisions than people below them. They are paid more, but also those decisions tend to be more luck-laden, which is really interesting. So sort of a funny thought about how organizations can work.

Is there also a bigger variance in the types of decisions that you're making? At the high end? Yeah.

Yeah, I suspect that's the case, right? Because conceptually, at least someone who is a CEO is making decisions really about resource allocation. And that would be, you know, financial resources, human resources and so forth. So, yeah, I suspect there is a lot more variance as you move up. Yeah, which is all really super interesting. And the question is, are the are the what you learn at lower levels or as you as you grow up?

in an organization are those things applicable as you get to that senior job? So it's really, it's a, these are all super interesting questions. So when you're thinking about organizations making decisions or maybe senior managers or CEOs, do you find that there's often a process involved in those decisions? I think it runs the gamut. Um,

And, you know, one of the things that I've always found to be fascinating and actually somewhat disconcerting is that many executives will say that they, and by the way, majority of executives will say that they rely or lean on their intuitions for making their decisions. Right. And, you know, the definition of an intuition is sort of a sense of what you should do or view of things without really tapping your conscious thinking.

And if that's the case, that to me would suggest that there's much more that we can do to improve.

That said, in other fields, for example, money management, the business that I've been associated with, you learn to, over time, think about distributions of outcomes and the probabilities attached to those distributions and really trying to think more of an expected value type of framework. I think it runs the gamut, but clearly, I think that with a lot of the work by

you know, Kahneman, Traversky, and of course, Dick Thaler winning the Nobel Prize a few weeks ago, has put into sharper focus some of the virtues of thinking about decisions more systematically and, you know, trying to address or remove some of the biases. So how would you advise me hypothetically if I was, I had a company, we, you know, we're a Fortune 500 company, we don't really have a decision making process.

How do we put one in place? What does that look like? And how do we, how do we use that to get better over time? Right. Exactly. So, you know, there's, um,

I mean, the overarching theme here would be whether there, and by the way, we should acknowledge at the outset that there's probably a continuum of decision types, some of which are very simple, cut and dried, and there are probably right or close to right answers and there are wrong answers, and other decisions that are inherently probabilistic and harder to, right? I will say that, so I think the broad concept would be, are there ways that we can be systematic in

evaluating the goodness of a particular decision, right? So by goodness, you need to really think about two things. One is what are the inputs and what are the measures by which we judge this, right? So in certain applications, that's pretty straightforward. For a company, it might be, you know, creating value. It might be for a portfolio manager and investing, it would be creating a more attractive portfolio. But you need sort of good inputs and some measure of

Now, one of the things, and the key is to try to be as systematic about that as possible and, in a sense, write down. So one of the things that's really captured my imagination since we have spoken last was this article published about a year ago by Danny Kahneman, son of his colleague, on noise in the Harvard Business Review. Did you see that article? Do you know that article? No, I don't think so.

So here's the basic idea. You know, Kahneman obviously is renowned for, and so right, renowned for his work on heuristics and biases. This idea that it goes life with rules of thumb that are incredibly time-saving and by and large pretty accurate, come with inherent biases that lead to suboptimal decisions. So that's fine, and hopefully everyone's got the memo on that.

This newer work actually is a somewhat different of a somewhat different ilk. And he says, you know, so I should probably give an example. So they so they went to a large insurance company. And, you know, these guys, these firms typically have, for example, insurance adjusters, people who will settle your claim if you make a claim.

And, you know, they're used, you know, they're trained the same way. They have the same software. You know, basically, you should expect that maybe not perfectly interchangeable, but these folks should be basically doing similar jobs, similar tasks, and should have similar outcomes. And so what they did is they gave these people...

claim, a set of facts. And they first went to their managers and they said, hey, you know, how much variance do you expect in these outcomes? Right. Again, playing the same way, same software and so forth. And the manager said, you know, a priori, you know, five and 10 percent variance. Some people have good days. Some people have bad days. Right. But there'll be a little bit of variance, but pretty tight. Right. For the most part.

When they got the numbers back, they found the variance between 40 and 60 percent. People were just completely inconsistent in applying the basic rules and the basic algorithms. So economists come to call this noise. And it's this basic idea that even when there are systematic ways to do things, as humans, we often don't stick to the script and there's a huge amount of variation.

So that would be something I would really want to examine in an organization, and that's from top to bottom, is if there are systematic processes that make sense or different levels of decision-making, can we identify those? Can we articulate those? And now, perhaps most importantly, can we make sure that our employees cue to those? And, you know, there is a series of papers that came out of Wharton that I thought were awesome

algorithm aversion. You may have seen these. And the basic idea was that as human beings, we tend to be quite adverse to sticking to a simple algorithm. And in fact, the algorithm goes wrong. We get very upset and are very dismissive.

And it turns out, they said, there's a way to overcome this, which I think makes enormous amounts of sense. And they said, the way you overcome this is you say to people, your first answer is going to be what the algorithm tells you, right? Just apply the algorithm. And then we'll allow...

will allow you to introduce a little bit of judgment. You can tweak the response. They said, it doesn't matter how much you let them tweak it. They can tweak it just a tiny amount, but you can tweak the response. So then there's a sense of volition that humans involved in some way, shape or form, but you do stick closer to what is ultimately a better solution. So, so this idea of algorithm version and, and, you know, it,

being systematic. I think that would be the approach that I would try to take if I were starting with a clean sheet of paper in any organization. What sort of things would you incorporate into that process? Would you have a checklist for

that level of formulaic systematic process where like what are the base rates or would you have more intuition based where people are feeling but the process is generalized but outlined but you kind of feel your way through it yeah i think that well you probably know where i'm going to come down on this but it depends a

But I think checklists are really powerful. And checklists are mostly, I mean, I think that most people don't like checklists because they feel constrained to some degree. But what a checklist does is compels you to make sure that you've covered all your bases. And certainly the lessons we've learned in medicine for checklists have been extremely powerful. And I think that the applications should go beyond that.

No, you know, intuition, I think we've talked about intuition before, but I mean, the key to intuition is that it exists. It can be very powerful, but we have to be very careful about where it applies. And I think the general rule is intuition can be developed.

in realms that are stable and linear. So there's a repeated pattern and things don't change that much. You can train your intuition. You have to train it, but you can train your intuition. It can be very powerful. So, you know, the canonical examples, obviously chess players and, you know, grandmasters are amazing. Their ability to chunk, their ability to see what's going on quite accurately.

quite rapidly. But, you know, it should be noted that it's not some sort of superpower force of these grandmasters. If you change the rules of chess or change the size of the board, you know,

all bets would be off and they'd be back to square one. So yeah, as you know, I probably tend toward the more structured. Now, all that said, right, you need, there are many things in life that do change over time. So they evolve over time. So you got to be careful about getting too rigid and having a mismatch between your decision processes and the

in the environment. So there is that blend as well. Let's talk about intuition for one second there. How do we develop intuition? I mean, I'm thinking about myself in the context of working for an organization, and I have all of these people around me who might be domain experts, and they have really good intuition in the particular domain. How do I acquire a better knowledge of that domain? So how do I improve my intuition in other people's domains?

Yeah, I think that's a really interesting question. Once again, I think that, you know, there's just some great quotes on this that, you know, just because you've been doing something for a long time doesn't mean you're an expert, right? So I think we should first be very careful.

I don't know if skeptical is the right word, but we should just be careful about suggesting people have domain or don't have domain expertise. That said, to me, again, there are sort of two conditions. By the way, there was a great paper written by Danny Kahneman and Gary Klein in 2009, and the history was really interesting because Kahneman was obviously –

of the belief that people can be suboptimal in their decision making or they have these biases. And Klein was, you know, sort of celebrated the notion of intuition and how intuition could work. And, you know, they were both very thoughtful, respectful guys. And they said, you know, maybe we should try to work together on something and see if we can find some common ground.

And I think the common ground that they came to, which I think makes sense, is that expertise applies when you have this sort of stable regularity in the domain itself.

And where you're trained with really quality feedback and accurate feedback. So I think it's those two dimensions is, A, it's going to be to some degree domain specific, and B, it requires training. And that goes back to, you know, the Andrews Erickson work on

you know, I don't want to say 10,000 hours, but basically deliberate practice, right? So deliberate practice being at the boundary of your capabilities with quality and accurate feedback to improve. So, you know, can you sort of look over somebody else's shoulder and acquire their domain expertise? I

I think the answer is actually probably not unless you are willing to dedicate some amount of time to getting trained and through deliberate practice, which is a non-trivial thing. So, yeah, no, it's interesting. And then, again, the degree to which you should rely on others also is a degree to which, again, those two characteristics have been satisfied, right? It's a fairly stable domain and they've been trained effectively.

I want to dive into that for one second. I mean, we improve through deliberate practice. Someone who can't run a few miles can work at it and get a little bit better. There's this feedback loop going on, but I want to talk about knowledge workers. Say I'm a manager, maybe a portfolio manager, maybe a people manager in an organization. Let's also say I'm above average in my performance, but I

I hit a rut and all of a sudden my performance is worse than normal. I'm underperforming. This is normal. It's natural. I mean, it happens. But at the same time, I feel smarter and better because I'm always learning. But history is replete with people like me being stubborn or lacking humility and missing a big change. Yeah.

how do I know I'm not one of these people? Like, how do you measure whether you're in a typical trend or you've lost the skill advantage? In his book, Ed Thorpe talked about card playing and how he hit a rut. But as long as there was, he was consistent with the odds, he knew he was fine because cards are a physical system, but managing people is more of a biological system. So this type of analysis isn't as relevant. How would you kind of advise me? Yeah. You know, so Shane is such an, I mean, I,

I don't know really what the answer to that is, but it is an interesting, and I think a little bit of what Thorpe was saying would apply or some of those lessons might apply.

And, you know, I think one of the observations we can make is if you are operating in this completely skill-dominated domain, outcomes are all you really need to know about, right? And so to your point, if you're performing well or performing poorly, that's almost a pure reflection of your skill. And in those domains, it's easy. But as you get into things that are more, as you mentioned, you know, maybe luck-laden or, you know,

biological, so they're going to be much messier. You do have to go back and default to understanding the process and with some sort of faith that a good process will ultimately lead to good outcomes. So the simpler case in that realm would be the Thorpe story, right? Where you know that you're making bets with a probability and there can be stretches where you just have bad luck even though you're making the right decisions in your process.

But when you talk about managing people, it becomes altogether that much more difficult, right? Because we don't really know exactly how the system is, and it's certainly not a physical system. So, I mean, part of that would be, to me, is to think about or revisit your process periodically. And, again, one of the important ingredients to improving process over time is trying to get feedback.

And the challenge is, I think, that the thing you're trying to manage, the thing you're trying to game your playing, let's say it that way, the rules can change. And as a consequence, you probably have to evolve. And a great example of that is in markets, where markets, as a market practitioner, you have to be very process-oriented.

But, you know, so that means that there are some elements of markets that are immutable, some principles that probably carry throughout any environment. But there are a lot of ideas that are mutable, that change and evolve. I mean, our economy evolves, the characters evolve, etc.

Relationships between nations evolve. And those are things you just have to take into consideration. So I don't know if there's an easy answer to that at all. But I do think you have to be very process oriented. And I do think you have to think about the domain, whether that's changing. You have to think about trying to gather and implement whatever feedback you get to make sure that you're aligned with what will lead to success in your domain.

I'm curious about your take on the concept of, uh, invariant strategies or invariant ideas, something that never steers you wrong, no matter the environmental changes, um, sort of a go-to always on by default. Do you have any of these in your head? Um, whether it's life or business? Yeah. I mean, I think that, um, uh,

When I think about, you know, I would just step back and just think about evolution and what is evolution trying to do? And really, it goes back to Dawkins, right? I mean, it's propagation of genes to some degree. So it's basically living to see another day and living basically, right? So strategies to propagate certainly would be a big one for that.

In money management, there probably would be a few things that are really important. One is, and I would say close to immutable if not immutable, one is that the value of usually a business, but really a financial asset is the present value of the cash that it generates. It's hard to get away from that ultimately being the case. That has always been true and I think it will always be true going forward.

Second is while it's extremely difficult to characterize, there should be a general relationship is the sure something is, the less you get paid to bet on it and the less sure it is, the more you get paid to bet on it. And, you know, the very simple metaphor is the horse race, the track, you know, so the favorite should get, you should get paid less if you bet on the favorite than the long shot. And that,

That's probably pretty close to immutable. So, so there are some of those principles like that. Um, you know, so again, evolution is a big one. Um, but again, the, the, the cast of characters tends to change. The circumstances tend to change. So how you, how you would evaluate those things might change quite a bit. And you think about from an evolutionary point of view, right? There are some ecosystems that are extremely stable that,

encourage a certain kind of ecosystem development and then there are other ecosystems that change really rapidly and what allows for success and propagation in that environment could be quite different. And this all ties back to things like how much

how much as an organization or even as a species you should trade off between exploitation, which is basically doing the same thing you've always done and taking advantage of it, and exploration, which is seeking new domains, new ideas, and so forth.

And those trade-offs, again, probably conditional on the rate of change in the environment, but that idea of how much you need to explore, how much you need to spend on R&D, that's also probably something that you can see almost everywhere you look. How do you determine the rate of change in your environment?

Yeah, I mean, I think that I'm sure there's an ecological answer to that that's much more rigorous than anything I could come up with. But, yeah, I mean, I think that, for example, in the world of business, you know, so in the world of business, I think there are some things that we can do to try to measure that. I'll mention some of the tests that we use.

that may be useful. One is straight up entry and exit data. So you're looking at a particular industry and the question you want to pose is how many companies come into it, how many companies leave, and all things being equal, higher entry and exit would be less stable, more changing. We look at a measure called market share change. So we look at all the participants over some period of time, say three years or five years,

And then we look at the absolute average market share change, right? So whether you've lost your gain becomes a positive number of that average and all things being equal. If that number is higher, that means there's a lot of market share moving around. If that number is low, that means it's relatively stable. So that's another rough proxy for it. The third thing we do is we look at something called profit pools, which is basically a breakdown of how much each company makes. And we see how that changes over time.

So those, at least in the world of business, those might be some high-level proxies for business and rate of change that can be pretty helpful heuristics. And again, you don't want to pay for a huge amount of competitive advantage in an industry where there's a ton of entry and exit, where market shares are very volatile. And by contrast, you might be much more willing to pay for profit.

for franchise value for an industry where market shares are very stable, where entrance and exits are very scarce, and so forth. So that would be, from a corporate point of view, might be one way to think about it.

How have your thoughts on algorithmic kind of decision making progressed over time? Last time we talked, I think we talked a little bit about AI, and I think you called it the expert squeeze. Where are you on that these days? Do you think AI is going to take over ground more quickly or perhaps less quickly than most people think?

Yeah, well, I think it's incredible. It's incredibly wide open. I think that we can continue as societies to continue to apply more of these kinds of concepts.

So there's applicability. But, you know, all this also gives me a little bit of pause. And I guess the thing that concerns me the most, if you can imagine almost like a little three-by-three grid, and in the various columns you would put, you know, business, government, and academia. And then in the rows you might put something like, you know,

Access to resources, access to data, and then maybe incentives, right? Those three rows. And then just fill them out in a way that you think would make sense. And I think if you do that, what you probably would find is the access to resources and the access to data

is probably greatest among a certain handful of businesses, companies actually. And those are companies you could probably name right off the top of your head. And then the question is, what are the incentives? And whereas in academia, the incentive might be, you know, to write some really cool paper and to get citations and so forth.

In government, it might be to obviously try to govern effectively and do the best for the most people. For business, the incentives might be sell more ads, get more people to click on my page, right? Something like that. And those might not be, there's a question as to whether those are ultimately great societal goods. So that to me is something that gives me a little bit of pause in all this.

The second thing I'll just say in general, because I come at the world from the point of view of markets, and there are areas where applications of algorithms are incredibly effective. Again, it goes back to these sort of more stable environments or predictive environments. But you have to be very, very careful about systems that are changing, the fancy term for it is non-stationarity. So the statistical properties and underlying systems change over time.

That's another concern I might have. We see a little bit of this in finance. It's this concept of overfitting. You have these data. You can match history beautifully, but you fail to anticipate that the world itself is dynamic. The rules that worked in the past may not work in the future. Overall, I would say probably I'm a technology enthusiast. I tend to be an optimist, rightly or wrongly.

But there are some aspects of all this that give me pause. And, you know, there's sort of this idea of the incentives, who's got the resources, data, and what are their incentives, I guess is the thing that gives me the most concern overall. Do you think we ever end up in a situation where the data just becomes this insurmountable advantage?

Yeah, we may be there already. I don't know. You just think about the amount of data that some of these large technology companies have. The ones that come right to mind, of course, would be Google and Facebook and Amazon.com. It's staggering, and there doesn't seem to be a lot of slowdown in the momentum of a lot of those businesses. Now, broadly, those things generate huge amounts of value.

right, for consumers. For Google, it gives you a huge amount of value. I don't know that I could go through a day without deferring to my Google searches, which I use all the time. You know, Amazon, great value. Facebook, you know, societal value. So there are a lot of good things with this, but again, the amount of data they can capture is,

And because all those companies have substantial resources, they can put a lot of resources against not only capturing but analyzing this data. Yeah, it's an interesting flywheel, right? Do you think that that should be a public good? Like maybe hypothetically these companies should be forced to release data that's anything over six months old so we can encourage people to compete? I've never thought about that.

I don't know. Yeah, I mean, I probably would be inclined to think that's not a bad idea, but I just haven't thought about that. I don't know.

Let's go back to your work on the, you're on the Santa Fe Institute. You're the head of the board of trustees. How did you originally get involved with complexity research? So in the mid-1990s, I went to a Baltimore Orioles baseball game and my host was Bill Miller, who preceded me as chair of the board. And, you know, Bill is a very interesting guy, very eclectic reader. And he had read a bunch of articles and had been out to the Santa Fe Institute and said,

you know, this might be a place that would have an appeal for you. I ended up working with Bill a few years after that. And at the time I was reading a lot, you know, I was just reading a lot, but I was reading in particular a lot of science and biology and evolution. And I was very keen to understand the intersection between economics and biology and,

I was also doing a lot of work on competitive advantage. In particular, the theory that got me first roped in was work done by Brian Arthur, who ran the first economics program at SFI.

has this idea of something called increasing returns. So we're taught in economics, microeconomics, and by the way, it's still largely true, that returns on incremental capital tend to migrate toward the cost of capital. And typically the reason for that is competition, right? If you're generating, Shane, massive excess returns, I'm going to come in and compete with you and compete those, whittle those away eventually over time. Brian laid out a situation, a set of circumstances where

Returns actually may not progress toward the mean. They may actually be repulsing the mean, increasing returns.

And it was sort of a heretical thought in the world of economics, right? He was sort of shunned a little bit for this point of view. But it was based on a lot of this, what now seems to be more mainstream, but at the time very nascent work on networks and network effects and those kinds of concepts. So I went out there probably a little more than 20 years ago, and from the very first meeting there was enthrall the whole thing. And as you probably know, SFI was founded in the mid-1980s,

By a slew of very prominent scientists, many of which had Nobel Prizes, who felt that much of academia had become siloed. And so people were speaking to one another within disciplines, but that many of the most important investing issues that are scientific and really our world face were at the intersections of disciplines.

So it was set up as an institute that was meant to break down disciplinary borders and have people of different disciplines speak with one another about problems that are common. And the unifying theme, if there is one, is the study of complex systems. And complex systems, I think we can articulate fairly simply, which is independent agents, which could be neurons in your brain or people in the city of New York or

Ants in an ant colony, allowing them to interact with one another and observing and understanding the systems that emerge from that interaction.

And so absolutely fascinating stuff. And I've learned so many important lessons, both that apply both to science and also the world of business. But perhaps most importantly, for me at least, it's an extraordinary community in the sense of massive self-selection for people who are intellectually voracious, willing to go across intellectual boundaries,

Just wide, minds wide open, which is really fantastic. So we actually have our board meeting coming up this weekend. It's the beginning of November and I'm really always excited to go out there and we're going to be talking actually about intelligence and the application of intelligence as one of our themes. So yeah, very, very influential, continues to be very influential. And in my 20 years of involvement, it's been a really important contributor to my intellectual development.

You do so much. So you're on the board, you have a full-time career in finance, you're a prolific kind of author and researcher. How do you, how do you balance all of these different demands? It doesn't feel like I do very much actually. Um, no, I, I, there's no, there's nothing magical about it. I think that, um,

As the nature of the work that I've been able to do over the years, I've been blessed with the opportunity to allocate a fair bit of time doing research and being able to write and

And, you know, it's really fun for me. I like to think about it as input and output. And that's, by the way, really the essence, I think, of teaching or quality teaching. So I've been able to have this balance between learning about things and inputting and, you know, especially being able to follow a little bit of what I find to be interesting. And then output, which is communicating it, you know, certainly within our organization first, but also more broadly.

And, you know, I'm a big believer that the notion of synthesis and being able to write and speak reasonably intelligently on something is actually an indication of understanding or first step toward understanding, let's say it that way. And I just had this sort of lucky, lucky ability to life that I've been able to pursue some of these things. Yeah.

I also, I think, Shane, we may have talked about this before, but I think it's also important to always recognize, and I think this is true for you as well, so I can't speak on your behalf, but I think I'll speak on your behalf, which is, you know, a lot of it's time allocation. Whereas I...

I really enjoy reading books. I get a lot from it. I spend a lot of time doing it. That's, um, I'm not watching TV much, right? I don't, if you talk to me about popular TV show series or something, I don't know what's going on. And that's not great. I mean, you know, they're Game of Thrones. I've never seen this episode of Game of Thrones. So, uh, not that I'm proud of that, but that's a trade-off that I've decided to make. So,

So when you focus on sort of the output results, it's important to acknowledge that there are some things that I, there are many deficiencies that should be underscored.

I've never seen an episode of Game of Thrones either. I think we're in the same boat there. Do you think in all the opportunities that you get, do you think about them from an opportunity cost lens? How do you think about that? Yeah, I do a little bit. I mean, I think that, as you know, time is really our most scarce resource. And so trying to be thoughtful about allocation of time is always useful.

But to some degree, and again, it's part of it. It's just I've been very lucky in my career and who I've been able to work with. In some degree, there's been a remarkable overlap between the kinds of things that I'm interested in and the kinds of things that...

I can actually do from a professional point of view, legitimately, right? So that's been, to me, that's really been a lifesaver. So there's not, it's not like, and by the way, even all the stuff that I read outside of work, I can't ever view that as separate from my work, right? So basically everything that goes into this mix is part of the overall process.

thinking in a partly overall output so yeah i mean um uh so yeah and we say opportunity costs right so like not not watching game of thrones is probably an opportunity cost that's a trade-off i'd be willing to make but um i also think it's not these are you know it's not for everybody and as the way i live my life is not for everybody should just always want to be clear about that there's no uh

claim of superiority or anything like that. I think we might be the only two people on the planet who've never watched Game of Thrones and we're talking to each other right now. You're a prolific reader. I have a lot of reading questions for you. I mean, I have to ask, of course, um,

what are a few of the best books you've read since we've chatted last? Are there any that have changed? You've changed your mind on significantly. Yeah. I mean, I think that, uh, even this year has been, I've really found a few books to be really terrific. Um,

Over the summer, and by the way, I think it's, and I always forewarn people on this, I think it's a very big commitment, but over the summer I spent a lot of time reading Robert Sapolsky's new book, Behave. Did you read that, by the way? I haven't read it yet. I did a skim of it already. Yeah. So I think Behave is, I don't want to be too hyperbolic about it, but I think it's probably the best book I've ever read on human behavior.

Sapolsky himself is obviously a really terrific scholar, but he's a brilliant communicator as well.

It's a slog though. It's 700 pages and it's a lot of work and it requires some commitment. But it starts really with neuroscience and goes everywhere from neuroscience up to culture and basically everything in between. And I think when you read a book like that, when you put that down and really contemplate it, it makes you really circumspect about a lot of things, about people's behaviors, about

your own behaviors, how we fit into societies and so forth. So Sapolsky's book, Behave, I thought was epic. And again, it's not something you can just dip in and dip out of. What I would recommend if someone goes away on holiday or whatever is to allocate a couple hours a day for a week or so and really get into it.

Earlier this year, one of my Santa Fe Institute colleagues, Jeffrey West, published a book called Scale. I find this work to be among the most wondrous research I've ever seen. The principle of scale is there's a relationship that's been well understood in biology for a long time, which is if you plot the mass and metabolic rates, basically mass and energy usage of, let's just use something simple, say mammals,

And you do it on a logarithmic scale. So that's the key is not one, two, three, four, five, but rather logarithmic means that each tick mark is the same percentage difference of one, 10, 100, 1000, and so forth. So you plot it on a logarithmic scale. And then you plot where each mammal is. It follows a perfect line on a logarithmic scale with a three quarters exponent. Amazing, right? So you tell me the mass of a mammal, I can tell you that mammal's metabolic rate.

I can tell you a bunch of other stuff as well. So that had been known empirically for a long time, but it turns out that Jeffrey West and some of the other colleagues at the Santa Fe Institute, Brian Enquist and

Brown came along with a theory to explain that, Jim Brown. So a theory to explain that. And it's actually really cool. And basically the simple version, I think, is energy dissipation in a network. They really figured out the math and so forth. Jeffrey then extends that work from biological systems to social systems.

We see very similar, there are different mechanisms, similar patterns for social systems. For example, cities follow these scaling laws. Corporations follow these scaling laws. Not exactly the same mathematical relationship, but also scaling laws. There's some really, really fascinating research on this and some conjectures as to what those things mean.

scale by Jeffrey West. By the way, here's my little cocktail party statistic on, uh, on that, which I find, I find this really, um, both fascinating and also frightening. Um, if you plot humans on that mass metabolic rate scale, um, humans should use about a hundred Watts a day. So, you know, what it takes to sustain your basic metabolic rate, what takes to sustain news about, uh, what it takes to power a light bulb, right? Um, but it turns out that, uh,

In the United States, I think it's probably true for North America, in the United States, our average energy usage is 11,000 watts per day. And around the world, it's 3,000 watts per day. So why is it so much higher? Because we've harnessed technology to allow us to deploy much more energy than each of our bodies require. So saying that differently is our energetic footprint is 30x our mass footprint.

And so that would be saying instead of having 7 billion people, we have 210 billion people in the world. Right. Right. So the question that's really interesting is, does how long will Mother Nature put up? Right. And will Mother Nature put a stop on that? That's really interesting.

And then a third book, by the way, I mean, I liked I really liked the book. I like the first part better than the second part. I really enjoyed Andrew Lowe's book, Adaptive Markets. But maybe the next book I would mention that I thought was interesting was Peter Goffrey Smith's book called Other Minds.

And the book is actually, and he's a philosopher by training, it's really about consciousness. But he does that through the lens of octopus, the octopus intelligence. So it's a really, really fun read. You learn a lot about octopuses, but also about the notion of consciousness and sentience and so on and so forth. So that I thought was a really fun book. But I think I don't read as much as you do.

but I think I'm around at around 25 or 30 books for 2017. Those are some of the ones that I found.

I mean, there are lots of other ones that I really enjoy, but those are the ones that come bubble to the surface. Talk to me a little bit about the Jeffrey West book in terms of taking linear and nonlinear thinking. How can we apply that to day-to-day life? Yeah. So one of the things that Jeffrey and his colleagues found that is really interesting is that for, for example, cities, that when they broke down some of these relationships, they found sub and super linear scaling.

So what does that mean in plain language? So sublinear scaling basically is an indication of economies of scale. So for example, if you look at cities of different sizes, as they get larger, they tend to use the physical infrastructure more efficiently. So you need fewer miles per road per person or distance of pipes and electric wires and so forth. So cities...

like larger beings tend to be more efficient as they get larger. So that's the sub-linear scaling, economies of scale. What they also found was super-linear scaling, which is some things grow faster than linear scaling. And those are things that are good, for example, patents. So larger cities tend to be more productive from an intellectual capital point of view than smaller cities. But it also applies to things that are negative, for example,

and things like that. So I think that these guys are starting to make some headway in understanding these various properties of, and I'm in this case, cities. Corporations, by the way, you know, we're early on in that, but both, by the way, for animals, for me,

Biological systems well social systems this idea of economies of scale seems to pop up in both both particular instances So so the you know the cell of an elephant or a whale is working substantially less hard than the cell of a mouse Which is really they're the same a million cell of course, but they're there they work very differently so

Those are interesting ideas. And then Jeffrey, you know, I think that all this work ties back ultimately to things like innovation. And how have we been able to harness so much energy as humans, right? Mostly via technology. But in a sense, we have to get better and better at innovation in order to sustain the growth that we've been able to achieve over the last few hundred years. And, you know, I think there's...

Like I said, you know, will Mother Nature put up with this? We'll see. But there might be a limit to how much we can continue to grow through innovation. And, you know, that would be, you know, I don't think it'll be within our lifetimes we have to worry about anything. But over the next couple hundred years, there might be some challenges. And one of the ways that may manifest, for example, might be something like just a population reset. So in other words, there may be some tragic event, whether it's war or war.

disease propagation or something that resets the human population to a more manageable level. It's a cheery thought. My next question is, what book do you give away the most to other people? Usually the books that I give away are, it's usually a little trio. The Steve Pinker's book, How the Mind Works,

E.L. Wilson's book, Consilience. And the third book is The Metaphysical Club.

So those tend to be the three books that if you said to me, and I usually give those to younger people, they're a 20 year old kid. How do you go about reading? Like when you pick up a book, do you read cover to cover? Is there a process? I know you, there's probably a process well thought out behind this. Like how do you do this? I say, go to the Farnham street blog and read the section about how to read a book. No, I think that, you know, the Adler, the Adler stuff is really how you should do it. And I do some of that.

I don't do it as systematically as I should. I usually do read things from cover to cover. I will periodically read part of a book and put it down. But for the most part, I try to pick stuff that I like and I usually will try to get through it from cover to cover. And I don't know if you find this as well. I do find that there tend to be ebbs and flow in my reading pace. So there'll be episodes, weeks where I'll really read a lot.

Part of that might be the content that I'm going through. Part of it might just be how my schedule works and so forth. In other weeks where it tends to be much slower, I try to be consistent about doing something all the time, but it does speed up and slow down.

Um, and I, I do, there are certain books where I will just do a sort of a heavy skim to make sure that I have some sense of the content and hopefully can remember if there's something in there that I should refer back to. So I won't read the whole thing, but for the most part, I do read things cover to cover. And, um,

Yeah, sometimes I regret that. But for the most part, I don't mind doing that. Do you keep all the books after you read them? I do. It's becoming a sore spot because I don't have any place to put them anymore. So I'm going to probably have to at some point get rid of some of them. And I have books primarily in two places. One is my home office and one is an office that's –

outside of my home or work office. And they both probably have now, I don't know, 1,500 or 2,000 books. So I don't, it's not like a crazy book collection, but probably 3,000 or 4,000 altogether. And I always feel very, very comforted about sitting in the middle of that pile, those piles, those shelves, right? Because I always feel like I have access to just a lot of, it's almost like they're friends, but a lot of knowledge and resources.

And I always feel great being in the middle of all that. So, and there will be days where if I'm between meetings or I have a few minutes, I just might walk over to a shelf and pull a book off I haven't seen in a while and just flip through it and say, is there anything, you know, re-quit myself with an old friend and say, is there anything in here that I should probably be paying attention to? Because as you know, especially very good books, when you go back to them,

with a new point of view, new knowledge. There are gifts that continue to give. You can always take something new from them, which is awesome. How much time do you spend reading new books versus rereading older classics? Mostly new, probably 90-10, 90%, 10%. One caveat to that is that

When I am researching, so when I'm writing, almost always that will encompass going back to things that I – and that actually may not just be books. It might be academic articles. But that does require circling back around. So maybe it's higher if you did it that way, but pure, like, books off the shelf. What am I – you know.

what am I actually reading? What's on my nightstand? It's going to be 90. Is there a spot where you read or time of day that you're most comfortable in? No, the, no, I mean, usually evenings, uh, at home, uh, we'll do that anywhere basically in the house. Um, and then a lot on the weekends and usually weekends are a fairly simple routine. Part of that, um, you know, my wife and I have five kids. Um,

Almost all of them are out of the house. The one that's at home is now in high school. So that creates a lot more flexibility than I know with people with younger kids and so forth. But on weekends, that's a very typical thing for me is to get up on the earlier side of things, come downstairs. It's quiet.

make a, make some coffee and just go at it for a few hours in the morning. And that's great. So, you know, it's quiet, it's comfortable. It's the morning. I tend to be more of a morning person than an evening person personally. So that's, that's where I can get a lot of the best reading done. So weekends, big, big on the weekends, a lot of weekends. Are you the type of person who goes to bed early or just the type of person who rises early? You know, and I think we may have talked about this before, um,

one of the things that I have become quite religious about is sleeping. And I find it helps me on so many different levels. So I,

I probably go to bed at a normal time and get up at a normal time, but I try to be very religious about eight hours. For me, eight hours of sleep at night is really, really good. There are some nights where I'll sneak by with seven, seven and a half hours, but I really try to be really religious about sleeping. That just washes over so many other aspects of my life. It affects my exercise. It affects my diet. It affects my productivity. I just find if I sleep, I can do those things much better than if I don't.

So, and I think that's something people under, I still think, I know there's been a ton written about this, but I still think people don't understand how important it is to sleep. And it's often the case that, you know, one, one less hour doing something else and one more hour of sleep, you're going to be much more productive in your, in your other activities. What have you learned about what contributes to your sleep quality? It's usually, um, probably very common ingredients, right? Which is, um,

I also am a big believer in exercise and moving. So exercise helps a lot. So I will always sleep better, almost always sleep better on nights where I've exercised and the nights where I don't. So the more days I exercise, the better I'm going to be.

to be. Diet's very important too, so try not to eat too much, try to eat quality stuff. I don't not drink alcohol, but I drink very little alcohol. That also probably helps. Very rarely will I have more than one drink in a day, something like that. Those are probably the things that contribute. I've never had a problem sleeping, but those probably are contributing factors. I would say you've got a firm grasp of what derails most people.

Why don't you describe a time that you failed? And I'm interested in the situation in which you failed through a personal mistake. And then not only how you recovered and got out of that failure, but how did you go about learning from that failure? Yeah. Well, probably my, you know, the episode that I think of as the greatest failure was right when I got out of college and

I was in a training program for an investment bank, which was terrific, by the way, and I learned a lot. And it was a year and a half long. And then that put us into our first jobs. And these were what are now called financial advisors, but basically stockbrokers. And so I was given a job as a stockbroker. And this, by the way, was early 1988. The firm I was with, Drexel Burnham, had gone through a bunch of legal issues. It was obviously in the heels of the stock market crash.

So the environment may not have been ideal, but I was so ill-suited for that work. And so I gave it a try, but I was miserable and I was certainly a big failure in doing that job. And essentially, I mean, I don't think I formally got fired, but basically got fired. Basically, there was a parting of the ways between the firm and me. And that made sense, by the way, on many levels.

And so, you know, maybe the silver lining in all that, maybe there are two things that came out of that for me that were really important. The first was that having gone through the training program, and this is why I'm forever grateful for that training program, we were exposed in our program to lots of different parts of the bank.

And that allowed anyone who was there to understand sort of where they felt most comfortable, where they felt they could add the most value. So we were on the trading desks and we were in investment banking and we were in research and in operation. So, you know, it was a great opportunity to see where you fit, see where your skills and interests would align with what the organization was trying to do.

And by the way, from that I realized the kinds of things that I thought I could be more effective at. And then the second lesson was learning precisely what I'm bad at. And part of it was I'm a natural introvert. That job, I think, was much better suited for an extrovert. I was in a sense selling products that I had nothing to do with creating, so I had no real confidence in the underlying products and so forth.

So I just learned a lot about what made me uncomfortable, what made me ineffective. And those were lessons that I, by the way, there were some positive things because they taught me a lot about sales and how to sell things. And some of those lessons I've been able to carry through my life.

But yeah, so that was a big one. And so then from then I just resolved to find a job or a set of jobs that I thought would suit the kinds of things that I tended to be better at and work toward my strengths. And so usually the advice, you know, I often get called by young people, college students and so forth. They're saying, you know, what should I do? And so, you know, my first bit of advice is,

It's a degree to which you can do this even when you're young is to take an inventory of what you think you're good at, what you think you're not as good at, what kinds of environments you're comfortable in, where you think you can be productive and what you're not good at, and try to find a career path that sort of gets you on the right trajectory to help you build off your strengths versus build off your weaknesses. So to me, that was my big failure. And, you know, last thing I'll say on that, as I used to take the train,

to go to work. And I remember telling my then girlfriend, now wife, you know, I'd rather ride on this train back and forth from the stations all day than go to work. And I was like, okay, that's probably a good sign that I should not be doing this any longer. So it was a painful period, but a great money experience.

Yeah. On the notion of advice and kind of following your skills, one of the things that I tell people is to identify what they're really good at that other people are typically really bad at. Yeah. I mean,

I love that. I love that. That's great. That's a little bit like Peter Thiel's "How to Build a Great Company" kind of thing. It's the same. Yeah, right. What problems can you solve that other people can't solve and you're going to be good at? It's a great way to think about things. Now, when you're young, you often don't have proven skills you can bring to the world, but you can start to think about

You know, do I want to deal with people? Do I want to deal by myself? Am I more project oriented or am I want to continue this flow? I mean, there's just there are certain. Am I a nine to five or am I someone who needs to be out and about? I mean, there are certain sort of dimensions you can probably think about and at least place yourself somewhere in those dimensions. Hey, on the notion of Peter Thiel, to steal one of his questions, I mean, like what is something that you believe that other people don't?

I always find those to be tricky questions, but the one thing that I think that has really changed, one view of mine that's really changed and was very much inspired by this work by Judith Rich Harris is the work on parenting.

And as a parent of five kids, this obviously is not a non-trivial thing for me. And, you know, I think the argument, I think as parents, we all tend to think that we're really, really pivotal in the lives of our kids. And on some levels, of course, we can be very influential. But I think the argument that Harris makes that's quite compelling is that parents aren't quite as important as they think they are.

And there are a lot of studies that I think would contribute to that. You know, some of these fascinating studies, by the way, of twins, especially identical twins, would suggest that they turn out a lot more similar than different, notwithstanding their very different environments. And that's quite compelling stuff.

But I also think the other point that she makes that I've really tried to take to heart and think a lot about in the context of my own kids was the importance of your peer group, especially when you're sort of early teen through kind of high school, it's called that way. So it's a middle school and high school. And that your peer groups are really important influence on your life in terms of everything, academic achievement, so on and so forth. So that's, you know, I think I would have placed much greater emphasis on the role of parents and

20 years ago, 15 years ago, and after reading that, that was something that really did, I really did change my mind on that a lot from that. What other sort of, I mean, possibly counterintuitive strategies do you use as a parent with your kids? Do you either encourage them to think or? So one of the things that I always try to do, especially when the kids get to a certain age, I mean, look, some things for young kids, you're telling them what to do, just, you know, basic things like, you know,

go to bed, you know, bathe yourself or whatever it is. And so that's, you're not going to get around that. But when the kids get a little bit older, for me, I try to be very mindful to give them ideas and ways of thinking. And I call them recommendations. So I say, here's a way you might, here's a recommendation for you. Here's a way you might think about this.

rather than saying, here's what I think, or here's what you do. And it's been rare that the kids have come, have done something different than what that recommendation is, because obviously I'm trying to illuminate it in a certain way that would make it sensible. But in a sense, it allows them to take ownership of these ideas, right? So I think that's one thing I say, you know, here's a recommendation for you. Here's something, here's what way I might think about it.

problem, see how that fits you and that suits you and if that's helpful in your decision-making process. So I think there's a lot of, you know, there's a book that was also very influential for me when my kids were much younger called Parent Effectiveness Training, PET. And their basic argument was as adults, you probably see the answer or a solution faster than your kid does.

or you can solve the problem more effectively than your kid does. And especially in our society today, the temptation then is to solve the problem for your kid. And that, of course, leaves the kids without the tools of problem solving on their own. So in that book, they like to distinguish your problems that are not the kid's problem, in which case you need the kids to help you solve the problem. And then problems that are the kid's problems, not your problems,

In which case, you should have them solve them and you need a facilitator, right? Helpful, right? And I thought that was a very useful distinction between whose problem am I facing

And as a parent, sort of holding back on this idea that I can solve this problem faster than you let me deal with it and you don't have to deal with it. Right. So essentially teaching the kids how to solve problems, you as an advisor or a guider or a facilitator, rather than doing it on their behalf. So that to me was another thing that I thought was really helpful as a mental model.

I like that a lot. One of the members of our learning community wrote in with a question for you asking whether you have a recent example of updating your views, maybe cryptocurrency, what to eat, something along those lines.

Oh, updating my views? Yeah, yeah, yeah. Well, you know, I've got to say that I don't know if this counts as updating my views, but there's a book, and I don't know if we talked about this, but there's a book I read last year that I thought was really awesome. And it was Ed Young's book called I Contain Multitudes. Do you know that book? Yeah, I think we talked about that at the retreat last year. You did? Awesome. So, right, so the work on the microbiome.

And so I think that's another area that, you know, I don't know that much about it. I think there's a lot to learn about that. But I have a suspicion that that's probably much more important than most of us probably believe.

So I have changed a couple habits as a result of reading that and thinking about that a little bit. The main thing, which I should have done a long, long time ago, but the main thing is I've sworn off any type of soda in any way, shape, or form. No soft drinks, no Diet Coke, no anything like that.

And I don't know that this is causal. I should be really clear about that. But from the moment I did that, I found it much easier to maintain a lower weight. I found, you know, I just feel better. So, again, that might be all psychosomatic, but it may not be as well. So that may have had an effect on me that I wasn't fully appreciating.

So that's a big one. By the way, the stuff on cryptocurrencies, I don't know. I mean, I've studied a little bit about this, and I suspect many people have, but I'm only watching this mostly out of the corner of my eye. But it's really fascinating. And certainly something like the blockchain, I would imagine, will be a technology that will continue to develop. But whether things like these basic cryptocurrencies, what those mean and so forth,

Yeah, I don't have a strong view one way or another. It is amazing to watch what's happened right the last four or five years. Oh, yeah. But there's no strong views on that one. One of the questions that I wanted to ask you that I've never had a chance to ask you, which is, what is happiness to you? What does that mean? What does that word mean? Can you unpack it for me? Yeah, a lot for me is, you know,

I thought a little bit about this and really for me, it's a sense of, I don't know, like independence actually is probably the word that comes to my mind, which is that I feel like I can do a little bit of what I want, you know,

and don't have to worry about too many, to be able to do that. What brings me happiness though as I get older, certainly come to realize it is about people and I think everybody emphasizes this but it's really true. So, you know, I can say that I look forward to nothing greater than having, being with all my family because now we have, you know, with all these kids and many, a couple of them out of college and so forth, it's difficult to bring everybody together and

I look forward to nothing and I'm no happier than when we have everybody together as a group. And so family, I think, is ultimately the top thing, being with those people. But the sense of independence in society that I don't have to worry about, I guess, goes ties back to this work on scarcity. I don't have to worry about, you know, where my where I think it's going to come from and having a little bit of financial stability and so forth would be related to that as well.

And then I think a lot about, you know, I think this is this idea. It's funny. You know, one of the things I do is play in a sort of beer league hockey group. Right. So,

Um, you know, so skating, you know, try to skate, get out there and skate once or twice a week and, and skating with the guys obviously itself is fun and that's exercise. But, you know, I've always also come to realize that part of what makes like hockey is beer league hockey. So interesting is that you're spending essentially, you know, 20 or 30 minutes before the game and 20 or 30 minutes after the game, hanging out with these guys, right? Yeah. People from all different walks of life and very different day experiences, very different experiences in general, different ages, um,

And there's just a lot to that. By the way, a lot of people saying a lot of zany things, but a lot, there's a lot to that, right? In terms of the social processes and

and so forth that I really value. So, yeah, to me, it's being able to do what you want, having that independence, and ultimately it's being around people that, yeah, that you want to be around. And the other thing is I'll say that from an intellectual point of view too, the affiliation with SFI has been incredibly valuable in that regard. So I'm usually very happy when I'm there hanging out with little scientists and talking to people, again, learning, growing. It's a lot of fun.

I can imagine. I mean, I would love geeking out like that too. What's next for you? Are you working on a new book these days?

No, so nothing formal. There are a couple of things that are percolating, and so we'll see if something comes to pass. One of the things that has come up over and over is about a little over a year ago, I wrote a piece, I think it was called 30 Years. But basically, I started on Wall Street just a little, now it's 31 years, but a little over 30 years ago. And I try to write down my reflections on what made for a great investor, so the top 10 attributes of a great investor.

And that was at that end of being, you know, very popular. I think, I think for someone like you, Shane, it would be, you know, mom and apple pie because a lot of the ideas were about decision-making and mental models and reading and so forth and, you know, sort of intellectual growth. But that, that is something that I've been approached numerous times, whether that could be something that would be reasonable to develop into a little book. And it wouldn't be, you know, it wouldn't be a warm piece. It could be a relatively short book.

And there's some other things. I mean, the other thing I've always been drawn to is this idea of, and it's really at the outset of your question about the Santa Fe Institute, is really, might we be able to, and this would be something probably more like an edited volume, might we be able to draw on a handful of really great thinkers to talk about

the role of complex systems and how, how, uh, it permeates into the world of markets and into the world of business. And, um, I might be in a decent position to be able to tap those people, um, to write a little introduction, something like that would be a project too, that I would be, um, I would be inclined to, to entertain. So, so nothing, nothing in the immediate offing, but a couple of ideas that are percolating, hopefully something at some point in the next, uh, couple of years we'll, we'll, uh, we'll

Oh, that's awesome. Eve, I'm already waiting with anticipation. This has been great. Where can people find out more about you? Well, a couple areas. One is if you don't follow me on Twitter, I'm certainly on Twitter at MJ Mobison is my handle. And MichaelMobison.com is another website. We don't update it a ton, but there's references to all the books. There are free chapters. There are a couple of fun things in there as well.

But if you really, you know, and I'm not a hugely active person on Twitter, but I do post stuff from time to time, a few times a week probably. That's probably the best way to keep up a little bit on what I'm working on and what I'm thinking about. Thank you so much, Michael. This has been phenomenal. My pleasure, Shane. Thank you. Hey guys, this is Shane again. Just a few more things before we wrap up. You can find show notes from today's show at fs.blog slash podcast. You can also find out information on how to get a transcript there.

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