121: Michael Mauboussin – Tactics for Better Decision Making, and Skill Versus Luck

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Hey crew, what's up? I hope you're doing fantastic. This week of episode 121, I have with me here Michael Moberson. Michael is a managing director and head of global financial strategies at Credit Suisse. He's also a professor of finance at Columbia Business School and the author of several books such as The Success Equation and More Than You Know.

Michael is widely recognized as a thought leader on the subject of decision making, as well as thinking about things in the way of process over outcome and skill versus luck. And it's these three things which are the overarching theme of this episode. So hopefully along the way you're gonna pick up a few good tips from Michael that will help to improve your ability of making better decisions and creating better processes as a trader.

For quick links to resources mentioned during this episode, you'll find everything at chatwithraders.com/slash one two one. Okay? Thanks very much for being tuned in. Now here is my interview with Michael Mobison. And I'll just mention one other thing, Aaron. My last name is pronounced Mobusin. Mobison. Yeah. And it takes took me twelve years to learn that, so uh hopefully you can do it in twelve seconds. Yeah. No, that's fun.

Excellent. Thanks for clarifying that too. No problem. I know a lot of people struggle with it. When I was doing a little bit of research for this interview, I came across a a video on YouTube. It was like um I think your students Head sort of put it together. Oh yeah, yeah, yeah. And uh one of the questions that they were asked is how to pronounce your last name. Yeah, yeah, exactly. I forgot about that, yeah. I think uh every one of them struggled with it.

I tell them the first day of class, but I don't know if they they remember, yeah. Anyway, that's cool. So

Michael, you're an authority on decision making and related subjects, but before we get into anything too serious, can you share a little bit about your background, like prior to working with Credit Suites? Yeah, so I was a liberal arts major in college, so I didn't study b business or finance at all formally. So when I first came to Wall Street I was I was quite f confused for quite a while.

Um but I was at a training program at Drexel Burnham Lombaire that was fantastic. We were allowed to not only get classroom training but also rotate through a number of different departments in the firm, which allowed anybody to figure out what they enjoyed the most, whether it was trading or research or investment banking.

And I personally really gravitated toward research, was able to uh take parlay that into a junior analyst job and eventually was hired origin at fur at the first Boston Corporation, the precursor to Credit Suisse. in the early nineteen nineties, was a research analyst for many years, morphed into a strategist, did that on the buy side, and then have rejoined Credit Suisse uh in this role in global financial strategy. So it's been uh it's been a fabulous journey

But uh started off as a liberal arts guy focused on first principles. And as you know, in your current role now you are head of global financial strategies at Credit Suisse. What does this entail? What sort of things are you doing there? Well the primary I mean we we we talked to different constituencies, including corporations and investors.

And the work we do tends to fall into one of four areas. Um we spend a lot of time thinking about markets and and capital markets theory and And for example recently a lot of discussion about indexing versus active management. We spent a lot of time on valuation work, uh so how to value businesses intelligently, a lot of work on competitive strategies, the third area. And then fourth and finally, decision making. So these are all things I think are ingredients.

in a thoughtful investment process. And that's probably uh the the most overarching title for what we do is is working on and improving uh any investment process.

And throughout this time you've also been teaching at Columbia Business School, is that right? I think you teach uh is it security analysts?

Security analysis, yeah, and and now we're in year we're on spring break. It's hard to tell from the snow outside here in uh the northeast of the United States, but um we're on spring break. So yeah, this is my twenty fifth consecutive year teaching security analysis, which is which is fabulous. And What I always like to say, teaching really uh compels one to clarify their thinking.

And uh and also allows me at least to keep one foot on sort of the academic platform as well as the practitioner platform. So trying to take the best of what academics do and the best of practitioners do and avoid the worst of both of those worlds. Sure. Yeah. Well, Michael, we've got a lot to talk about here. So let's get right into decision making. I'm not really sure what the best question to start this.

this uh this topic is, but let's start with how should we think about decision making and how can one judge the quality of their decisions? Yeah, so I think that the uh overarching concept here is that through work by folks like Danny Kahneman and Amos Tversky and and many other psychologists We've discovered that as as individuals we all often don't make uh the best decisions. Um we often rely on heuristics, rules of thumb which lead to certain biases.

And there should be ways to do all these things a little bit better. So for me the first step in all this is to become familiar with that work and to understand where our own cognitive limitations are likely to fall.

As for auditing the quality of your decisions, it's a really crucial issue. And I always like to say You know, if you're in a probabilistic field, and I know Aaron, you're very familiar with this, but if you're in a probabilistic field, The point of emphasis should really be on the the process of your decision making so that given the information you had at a time.

that you're making the proper choice rather than solely the outcome. And I think that as humans we tend to focus a lot on outcomes and we tend to associate good outcomes with good process and bad outcomes with bad process.

But if there's a heavy dose of luck or some sort of realm of probability, it really does have to be the the the focus has to be the process. So to me that's really the best way to try to audit the quality of your decisions is to document how you thought about it, what your process was, and then I do think if you do that well over time, certainly the outcomes are uh will be to your liking.

One of the things there is to be aware of your cognitive limitations. Uh would you mind expanding on that just a little bit? Sure. I think that uh you know, there are two big threads to the Kahneman and Traversky work. Um the first thread has to do with called heuristics and biases, which is what you asked about, and the second is on prospect theory.

Which is how some of our behaviors um depart from what would be normative economic theory. So going back to heuristics and biases, the idea is that we operate with shorthands and those shorthands often lead to biases. that lead to decisions that might not be as good as they could be. Let me just give you maybe uh a three examples from that world that I think would be relevant for for investors or even people watching sports or or something else.

The first is called overconfidence, which uh has been very well do it's actually quite easy to document that people tend to be overconfident in their own abilities and k and views of things. And one of the major manifestations of that is that people tend to project outcomes. in the future that are vastly too narrow given the circumstances. So people fail to anticipate the range of outcomes that are that are plausible. So overconfidence can be very deleterious for that reason.

A second one's called confirmation bias. And this is I mean, if you're an investor you've done this. And confirmation s bias says once you've made a decision, even if you struggled to make that decision, Once you've made that decision, you tend to we all tend to seek information that confirms our point of view and we dismiss or discount or disavow information that doesn't confirm our view. So in effect we don't let new information in the door.

And what we know also in the decision-making literature is that uh really what you want to do is have a prior view of the world and update that as new information comes in. confirmation bias tends to block that process. So that's another really important one to to bear in mind. The third one's called recency bias, which means we tend to wait recent events.

much more than we do appropriate sample sizes for things. So, you know, if an athlete has had a particularly good stretch uh we tend to extrapolate his or her performance versus looking at the uh entire body of that athlete's performance and trying to understand exactly um

what their th what their likely match is like to be or their their next outcome is going to be. So those are three examples of of very specific things, all of which I think are relevant for investors, but really people in all decision domains, for sure. Let's pick up on that point of confirmation bias because I think that's really important. There are times when we have our minds made up before properly thinking things through, you know, and looking over all the facts.

What should someone do when they find themselves in this position? Like when they actually realize that they do have some sort of confirmation bias? What's the best thing to do in that situation? You know, Aaron, I think that the the simplest answer to that is to always have an open mind and uh to always keep all of your views or make your views somewhat uh tenuous. Uh there's a psychologist at the University of Pennsylvania, Phil Tedlock, who's got a great line. He says um Uh beliefs are

Theories, basically. Hypotheses to be tested, not treasures to be protected, right? Beliefs are hypotheses to be tested, not treasures to be protected. And I think that sort of encapsulates the thought um great really well. So the idea is to to keep an open mind and be actively open minded about new input. And then if if I want to be a little bit of a a nerd or be a little bit more formal, uh the the structured way to do this is through Bayes' theorem.

So Bayes Theorem is a mathematical way to take new information and to update your point of view. So clearly to begin you wanna go in the right direction, right? So make sure you're you're str strengthening or weakening your probabilities.

And then secondly, if you can go to the right magnitude, that's great. So so the first part is open-mindedness. And second, if you want to be slightly more formal, it's the application of ideas behind Bayes' theorem to improve the quality of your uh your new views. Can Bayes' theorem, I know of it, but um that's kind of about where it ends. So yeah, I'm I'm aware it's a it's a math thing. Very technical term there. Can that be applied to all decision making or only in some circumstances?

Yeah, I think it applies to to most everything and and I think that the while the math of it can be uh maybe daunt be daunting to some people, the basic principle is very straightforward, which is You know, you woke up this morning with a set of beliefs of the about the world.

and new information came across the transom. And the question is, uh, did you or is it appropriate for you to change your views given the new information that's come in? Right? So the subtleties are knowing again which direction to move in. and knowing the magnitude no in which to work. Now I get a I have to say to you that many many people are actually very uh skillful forecasters, for instance.

um are aware of Bayes' theorem, but they don't actually formally use it. So I I think that it's less about the formal application of the mathematical model and much more about the awareness that every view you have should be somewhat tenuous and that you should be ready when when the evidence dictates to change your view. So

You know, the the fact is that once we've made up our minds on things, we like to be right and uh we don't really like to work at it. So we'd rather just not have to deal with it and just pretend like we've got it got it correct.

As you know, I mean these are actually not not only in the world of investing, but also very, very important the world of business and and certainly we're seeing around the world uh very important for politics as well. So this is a I think it's an overarching theme of updating views. appropriately uh based on new information.

Yeah, and as we're talking about biases here, are there any other biases which you think we need to be aware of when it comes to decision making? And is there ever times when some bias is useful? Well, I mean there there are many I mean I think you document them, there could be dozens of them, right? So the the we go on and on about it. Another example is framing.

So for instance, Aaron, the way I present a problem to you, I could I could present two mathematically equivalent problems to you, but the way I presented them could skew the way you would decide, right? Or I could li I could get you to to choose one versus the other, even though they're mathematically the same. So that that that's the kind of thing framing is is another um

Good example of that. You know, I'm not sure that there are cases where biases work for you, right? By almost by definition a bias we're saying is um something that's a departure from what would be ideal. But that said, I I I think it's very important to acknowledge that Sometimes our rules of thumb work quite well, and in many instances they work quite well.

And in particular, your intuitions uh can be very robust in realms that are stable and realms that are linear, right? So if you practice in a stable or linear realm you can develop uh and cultivate uh an intuition that's actually quite useful. And you know the the classic example of that would be chess players.

You know, you show a chessboard to a master or grandmaster, he or she very quickly knows which players have the advantage. They can ide uh they can figure out uh good or even great moves very quickly. And even athletes too do a lot of this to to some degree. But that's the the real lit the real litmus test is where the these kinds of things, where your intuition works, is is relates to the stability and linearity of the environment.

You know, that's an interesting question as regards to as regards to business and investing because there's probably some components of it that are that are fairly predictable, but other components that are much less predictable. So yeah, you have to be more guarded about relying solely or exclusively on your intuition.

Okay. And and just so we're clear, when you mention a steady realm or a linear realm, what's an example of each of those? I know you mentioned the chessboard there, I'm not sure which one that fits into. Well I think chess is both, right? Stable and linear in the sense that the pieces move the same way. It's a open information game and so forth. And there there are no non linear effects, right? So

I mean that would be that would be that example. But even things like uh un non-stable uh nonlinear effects, by the way, even simple things like phase transition. So Uh the most basic phase transition is a physical example. So if you take water and uh it's at two degrees Celsius and you take it to zero degrees or minus one degree Celsius, it goes from liquid to solid, right? So that's a phase transition. It's a small change, incremental change in temperature.

leads to a very cha different change in the in the in the actual nature of the system. So those t those kinds of things tend to be very difficult for prediction and when they're we're out when they're on the ro not when it's just water freezing, but we're out in the real world. So and and you could think about this even you know like you're driving your automobile. So when you drove

When you drove into work today, um you mostly had stable and linear environment. But there could be instances, extreme instances for your driving, for instance, that you would probably be out of your uh out of your league in terms of comfort, right? How do you try to reduce the influence of emotion in decision making? I know this is somewhat related to bias, but

I think emotion is maybe slightly different. How do you try to reduce the emotion uh influence in decision making, particularly when the result of that decision is something that we greatly desire? Yeah, it's a great it's a great question. And um part of the answer I think relates to things like uh checklists. So if you're in an activity that lends itself to a checklist, would that allow you to be uh methodical?

um every time you make a decision, that tends to be a good thing. So for example, um the classic example is pilots, right? So you you would not think of getting on an airplane unless the pilot had gone through his or her preparedness checklist.

And uh that that allows for that consistency and whether that pilot's having sort of a good day or a bad day, if they've uh if they've uh dealt with the full checklist appropriately, um you're gonna fly safely. Now it's also interesting when you tie in the emotional component

They're really uh often talked about two different types of checklists. One is called do confirm and that means you basically do your job as you normally do, and then you pause periodically to confirm that you've covered all the things you're supposed to cover. They do do confirm checklists. The second kind of c checklist is called read do.

And this is uh when you're in an emergency. So now you're the pilot and you're flying uh you're seven seven seven and one of the engines goes out. Right, so it's not a good situation. Uh tends to lead to emotional arousal. And uh the redo checklist basically says left wing, left engine out on seven seven seven. Here are the following five things you should do in this order.

So it allows you to act again because people know too it allows you to act without worrying about your emotional arousal to to get to the most satisfactory solution as quickly as possible. And have to say in in much of our work You know, to to bring this to the world of investing, we wrote a piece called Managing the Man Overboard Moment about stuff when when a stock is down ten percent versus the market.

And again, very high emotional arousal. We provide a redo checklist that allows the investor to sit down, calmly go through. uh at least what the history has said about those types of situations to come up with hopefully a more balanced and uh hopefully rational judgment as to what to do from there. I know you've written a whole book on this, Michael, but would you mind describing

what you mean when you talk about counter intuition? Yeah. By the way, I'm not even sure counterintuition's a word. My my editor decided to use that subtitle, so so don't tell anybody, but I don't think it's in the dictionary. Um no the idea is this that that when we're faced with certain types of situations

our minds naturally want to deal with one way when there may be a better way to deal with it. So in a sense you have to counter your natural intuition, your natural way of doing something to come up with a better solution. So that's really where that you know, that book, Think Twice, is all and and the title is also very evocative, Think Twice. Um and so what I try to do in that book is is lay out eight eight of those types of situations and um

and uh and and provide some guidance as to how to sidestep them. So one example this actually ties into the man overboard, one example Also popularized by Danny Kahneman. is this idea called the inside outside view. So the idea here is that if I pose a problem to you, Aaron, it could be anything. It could be, you know, if you're a university student, when will you hand your paper in? It could be how long will it take you to remodel your kitchen and what will it cost? It could be anything.

The classic way to to solve that problem is to gather lots of information, to combine it with your own inputs and experience, and project into the future. And and left to our own devices, that's how we all do it. The outside view, by contrast, says, I'm going to think about my problem as an instance of a larger reference class. I'm going to ask the basic question, what happened when other people were in this situation before?

So rather me as the university student saying, I think I'll have my paper finished by Friday I ask how many p students in this situation, in this position, actually got done by Friday? Which is a very different question. And so it turns out thoughtful combinations of the inside and the outside view give you better predictions about what's going to happen.

So that's a classic example is if I leave your to your own device, you're going to use the inside view, but integrating thoughtfully the outside view, thinking twice. will allow you to be more accurate in what you're doing. So that's one example, but that's that's this idea of counterintuition is under certain conditions, your your natural the natural flow of how you're going to want to think about it may not be optimal.

And as you brought up more information there, gathering more information, how often is more information the key to better decisions? Or can this, you know, lead to indecisiveness? Analysis for house. Yeah. Yeah. Can you speak to that a little bit? Sure. And uh it's a fascinating question. And I think that look, in some domains more information is better. for sure. But there's a very, very famous series of ex psychological experiments that relate to this.

And um the original one was done by Paul Slovak back in the nineteen seventies, but this has been replicated quite a few times. But I'll I'll tell you what Slovak did back in the day. So what he did uh he went to people who were handicappers, so betting betting on horse races. And he had uh a menu of something like eighty eighty eight pieces of information. And he said, Here's what I want you to do. I want you to take uh uh b some bits of this, uh say five bits of information and place a bet.

Double that, place a bet, double it again, place a bet, and so forth. And so um then he could examine two things. One was how accurate their bets were. So so did the incremental information help them. And the second thing he measured, interestingly, was confidence. And what he found was the accuracy of the bets truly didn't improve much at all as the new information came in, and yet confidence tended to soar.

So there's this disconnect that as people have more information they tend to get more confident but no more accurate. So one of the things I like to talk a lot about And and by the way, there's a logical reason for this, right? Which is something like uh you know, you prioritize if you're a handicapper and I say, here's a menu of things you can pick from. you're gonna choose the things that are most relevant to predicting a horse race, right? So that's so so there's a

There's a substantial degradation of the value of the information as it's coming in, right? So that's this interesting contrast that often more information doesn't help us, yet it does make us more confident. And then Certainly if that confidence becomes misplaced, that that raises the risk of uh of of again making bad decisions or bad bets or what have you. So that that is um I think that's a really interesting dynamic.

The key on this analysis paralysis, I mean, i that's really has to do with the timeliness thing. So if you have you're in a situation where you have to make a decision, it's often the best to to wait as long as you can before you have to make a decision because new information may help you But if you're in a position where you should be making decisions, uh that that is a you know, again, you have to just move and and work with what you have. So that's where it becomes a problem.

Yeah, and one of the things I've heard you speak about is actually weighting information. How do you go about this and and how do you think about weighting information?

Yeah, I think it's a very I mean it's not it's not a super easy thing. But for example, uh you might take a simple example of how to think about a value of a corporation and it's usually It's usually the case that for a particular company um or a particular stock of a company, there are usually two or three variables that are that are hanging in the balance that you have to figure out one way or another.

And uh the sooner you can get to those um and uh come up with a a thoughtful differentiated point of view, the better off you're going to be. You mentioned uh at the beginning that I uh teach at Columbia Business School and and sort of the capstone of the course is the students present to real portfolio managers. So it's a it's a very live setting. They're pr they're pr they're uh saying buy or sell particular stocks and the portfolio managers are giving them

um feedback real time. Probably the n the number one thing the students hear from those professionals is you gave me too much information. And you need to figure out what matters for that particular company or that particular industry.

So this exactly this idea of prioritization um is really is really crucial. So um that's one example. Now in one of my books I wrote an another case of things that sound impressive that are not. So for instance Uh uh we had a technology analyst who was trying to figure out whether technology spending was gonna go up or down, which of course is a laudable objective. And so the analysts surveyed Fortune one thousand chief information officers. Well

If you look at the for and so they say I you know, we talked to seventy five of them. Well you look at the Fortune one thousand, right, it's very, very it's a power law, it's very skewed. So just a handful of companies spend almost all the money and the tail spends very little money. So if you're talking to

you know, company number two hundred to one thousand, they're really not moving the needle, whereas companies, you know, zero through fifty are really the ones that are making it making it happen. So that's an example of something that sounds superficially quite impressive, but unless you understand the actual contributions of the companies, uh you're you're gonna get a misread on the situation. Yeah, I think that's a really great example.

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How do you suggest that people deal with indecisiveness? Like when you don't know what's the best thing to do? I mean, I personally think this is maybe one of my weaknesses in some ways, is that I can sometimes struggle to make a decision and I can be very indecisive sometimes, uh, which is quite annoying. How do you suggest people deal with indecisiveness?

Yeah, it depa I mean a lot of that is domain, you know, depends on the domain you're dealing with and sometimes you're indecisive and this is not gonna It's getting more annoying than, you know, like if you're taking forever to order at a restaurant and off the menu or or sitting in Starbucks and and mulling over the different uh beverage choices.

But you know, the key question is i do you need to move or not? And uh you know, there are little simple techniques on things if you if you do need to move. Um, you know, one tet a very simple technique that can get you moving a bit is uh just a a plus minus column and just say, right, you know, here's a decision. If I do it, here are the things that are good about it. Uh on the minus column here are things that are m bad about it.

And as you enumer enumerate those things, you you try to cross off in equal proportion the things that are of equal of significance. And whatever you're left with basically says yay or nay, right? So you sense you're forcing yourself

to to qu quantify the the the the decision in that fashion. So that might be one one way to do that. So You know, I d I don't know if there is a great answer to that, but but those are that that's one simple technique that tends to get you moving, get you thinking and uh you know, get your mental juices flowing so you can so you can get off the fence and do something. Sure. And are there any other exercises that one can do just in general to improve their decision making ability?

Yeah, I mean there there if you if you especially if you're working in a group setting, um almost always the the main problem is that people don't surface different alternatives. So to the degree which you have techniques to surface alternatives, that's very effective, especially in group settings. Um one example of that is a concept called a pre mortem.

That was developed by a psychologist named Gary Klein. So we all know about post mortems, you know, so the patient has died or some adverse outcome and we And we try to learn from our mistakes. We give them the information, we think about what we could have or should have done differently. A premortem is actually a quite different exercise. It says, uh well let's pretend we've actually made a decision today, some investment for instance.

And then we launch ourselves into the future, say, so now it's March of 2018, and we pretend this decision turned out very poorly, very badly. And then each of us individually writes down why that decision didn't work out. And what's interesting is the fact that you put yourself in the future looking back to the present and the fact that you've not really made the investment, but you open up your mind in a certain way that allows you to consider

and weigh more possibilities than simply projecting out ranges of outcomes. So it's a it's a very interesting little technique that that allows people to to to help their decision making mostly by Making sure their mind stays open.

you probably guess I would say this is that I'm just a big fan of people uh spending as much time as they can reading and thinking and exposing themselves to different points of view in a in a thoughtful, rigorous and tolerant way because I just think that that exposure and that reading and that thinking allow you to be much more judicious as you as you do have to make specific types of decisions.

As we've been talking about decision making here and as this is something you've clearly been studying for quite a while now, have you come across any interesting like neuroscience studies that kind of back this sort of thing up which almost get at the reasons why human minds are in many ways flawed for investing. Yeah, actually we wrote a little report about that and um yeah, I thought it was fascinating. We had a summer intern a couple summers ago.

who was terrific uh and had the audacity to write on his uh resume that he was interested in cognitive neuroscience. So we put him to work on that. And And what we did is we picked a number of the uh sort of these biases or these psychological issues and we asked them to do the find the research for the neural correlates. So I'll give you one example on this, which is a fascinating one. The f the fancy term for it is hyperbolic discounting.

But you really the the concept is a very easy one, right? Which is something like this. If I ask you, uh, for example, if I say, Hey Aaron, uh, you know, w w would it benefit you to lose a couple of pounds you go, Yeah, you know, I could be a little bit trimmer maybe. We all think that a little bit.

And I go, Okay, that's great. So um would you like to have dessert today? And he goes, Sure, I'll start my diet tomorrow, right? So in other words Our future selves are really good, but our present selves are not so good, right? And so if I say, uh, you know, what will your snack be a week from now versus your snack at this moment, people tend to pick the unhealthy snack now and the healthy snack a week from now, right?

So there's a fa there's a formal way we can model all that stuff, but that's the basic idea is that we want immediate gratification, but we want to be good in the future. So it turns out, this is fascinating, that uh they put people in FMRI machines as they are making these kinds of decisions. And it turns out that different parts of your brain mediate those decisions. Uh the the more me the more um mo in the now moment decision is mediated by a more basic part of your brain.

And uh the decisions about the future are mediated more by your free front free front frontal cortex, so more of your executive functioning. So c so it's quite literally those questions uh get sent to different parts of your brain that deal with them in different ways. So I think that's a really cool example of how

how that that process could basically work. And we you know we give other examples uh as well, but that's that's one really nice uh case that was supported by work in the FMRI machines. Right. So I mean in that example, like how does one overcome that? Like if your brain is working against you, wh what does it take just to There's not much. That one you have to figure out some other techniques, right? So that one you have to and you know, th and there are there's a there's some um

Uh so Katie Milkman's a professor at the University of Pennsylvania. She's come up with some pretty clever things. Um And and basically what you do is you link things you like with things that you may not like. So you might say something like, you know, I I love Aaron's podcast. It's my favorite part of my day listening to his podcast or you know, here's a series on television I love to watch or or what have you.

And then you s you link it to your uh workout on the treadmill, right? So I'll I'll only listen to Aaron's podcast when I'm running on the treadmill. So So I can't do the fun thing until I'm doing the thing that I need to be doing. So so by uh linking things together like that, that that can be a motivating uh a motivating factor for people. Okay. That's kind of clever. Cool. Yeah. In a talk you gave

You you spoke about the brain creating narratives also. Can you speak about this a little more? Yeah, I f I find this to be I find this to be absolutely fascinating. Um the work that I uh rely on a lot for this is uh by a neuroscientist mate named Michael Gazanaga.

And he's most famous for his work on split brain patients. So these are people that have had debilitating epilepsy, who failed all their treatments and and really as a last ditch effort, uh neurosurgeons go in and section the corpus callosum, so the bundle of nerves between the two hemispheres of the brain and

And, you know, uh by the way, I should tell you that people feel better after it, so it helps them. But more importantly, for our discussion, it leads to uh an amazing exper experimental condition where a uh scientists can figure out modularity. So what's happening in your left hemisphere and what's happening in your right hemisphere. And to to make a well, not a long story shorter a long girl story shorter, um what they found is in the left hemisphere of your brain.

is a module they call the interpreter. And the job of the interpreter is to link cause and effect. So Aaron, if I throw some outcome at you, some sort of effect, um your mind is going to demand some sort of explanation for it, some sort of a cause. It's like an itch that demands to be scratched. And once you come up with that cause, even if it's not particularly good, it closes that loop and the m interpreter moves on. And in fact Kazanaga wrote a book last year and you know, is is a very

maybe two years ago. It was a very poignant part of the book where he says basically, um, this is the this is the feature that distinguishes humans from other species above all else, which is really fascinating. So the point is we love stories, we love narratives. And I mentioned a few moments ago this idea of the inside outside view and reliance on base rates and sort of historical performance to guide your future. The problem is if I give you a statistical answer to something.

It comes across as pallid. It's not lively. If I tell you a story, it's vivid. cause and effect are built into it. It's much, much more salient and much more likely to get you to move. So in our world, even if we have all sorts of statistics and algorithms and numbers, it's still at the end of the day it's the it's the stories that move us. And that's really important to recognize um both as a communicator

but also as a ingester of information. That the stories themselves will tend to be more uh important to you and and tend to to to uh encourage you to behave more than just a statistical argument. That's really interesting. That's really interesting. So I mean if someone wanted to find out more about

that where's w you know, what sort of things could they they look up or is there any resources you might recommend on that? Sure, yeah. I mean I think that um I mean I would recommend certainly any work by Michael Gazanaga. He wrote a book called Tales from Both Sides of the Brain. Um and so I would uh if you're interested in that broader topic, I would I would check that out.

And uh he's written a number of popular articles, so if you don't want to go uh deeply into it, I think if he I think he wrote a very um well documented uh Scientific American argument uh article. So if you Google Gazanaga Scientific American. Um you could find that. And that's a a nice little four or five page primer to get get a sense of what we're talking about. Before we move off the topic of decision making here, let's just do one more question. So

How can we learn from poor decisions instead of repeating them over and over again? So, you know, I first had the great honor of meeting Danny Kahneman in the mid two thousands and about a dozen years ago, and that was basically the first question I got to ask him. And his response was the best way to do this is to keep a journal of your decisions. And it doesn't have to be fancy. So go get a very simple notebook.

Very simple pen. And when you write down uh when you make a decision that's of consequence, write down what you decided, why you decided, what you expect to happen. And you know, d put down the date and time and he said listen, if you if you're inclined, write down how you feel physically and emotionally about this decision and just ki and and just have that as a repository

to go back and audit the quality of your decisions. Because the truth of the matter is if you don't do that, we all fall for what's called called hindsight bias, which is we start to think we knew what was going to happen with a greater probability than we actually did, right?

And something called creeping determinism, which was you start to believe the outcomes we see are the only things that could have happened. So you really want to fend off hindsight bias and creeping determinism and by keeping a log of the quality of your decisions I think that's the best way to do that. So it requires discipl it's not hard, it's not expensive, it just requires discipline to do it.

Now the next subject which I think flows on quite well from decision making, um, which I want to speak with you about, is process versus outcome. And y I know you briefly mentioned this um when we first started talking. When you talk about process being more important than the outcome, why is this the case? Help us understand. Yeah, so Aaron, I think that the important thing is that that's not true for all domains. Some domains that are pure

pure s uh skill domains, uh outcomes are perfectly indicative of what's going on. So uh you know, we'd mentioned chess before about a running race, you know, something tennis match, for the most part Those are skill dominant games and the best player wins and we don't need to worry about so much what was behind that. However, when you get into realms that are probabilistic where there's a dose of luck in the outcomes

Um it becomes very important to focus primarily on process and not on outcome. And you know, I'll give you one example and I opened one of my books with a story. A good friend of mine is uh you know, a sports executive and he's a f fan of playing blackjack and uh he's he's sitting at a table and uh dealers just playing and and did deal dealing cards. And a guy at his table uh is Delta seventeen.

And uh so the dealer looks at the guy and assumes he's gonna sit on it and the guy says, No, no, no, give me uh you know, give me a hit and the dealer rolls over a four and the guy gets twenty one and wins the round and you know, high fives and drinks all round. And you know, this executive was saying to himself, you know that was a good outcome but a horrible process because if you do that a hundred times and certainly a thousand times

You're gonna come out at the sure end. We know that, right? So he's celebrating a good outcome, but the process itself was really bad. So the key is in these realms where there are probabilities is to focus really on the quality of the decision and the process. And again, the ultimate objective is outcomes, right? We want to have good outcomes at the end, but uh the the the belief is that an adherence to a good process gives you the highest chance of a good outcome over time.

Okay, so with that being said, how does one build trust in their process? That's really difficult. So you you need to have and it depends again on the domain, but you know, the best thing is to have uh you I mean ultimately you want your process to be um economically sound. So uh you know, and certainly if it's mathematically d devised that can be really helpful. And it needs to be repeatable. All right, so it's gotta be efficient, sound, and repeatable.

So those are sort of the questions to ask is is my process seem to have those components. So domains like uh blackjack and card counting, we can really specify all the rules to to behave in a way that's that's pretty good for a good process. Other realms are much more dynamic. You know, you think about it whether you're an investment manager or e executive of a company.

You know, y you could still have components of process, but the the d the world you're dealing with is more dynamic. So those become trickier. But but that's the basic idea, is make sure it's transparent, repeatable, and uh economically uh or mathematically sound. Yeah, and I I think that word repeatable i is really key there. So, you know, if we If we talk about investing in trading here, you know, where ideally your process is repeatable

How much should your process be influenced by outcomes? Like I know you're kinda talking about how the process is is more important than the outcome, especially when you're playing like a probabilistic game. Is there ever a circumstance when your process does need to be somewhat influenced by the outcome? Like if your process is just constantly. dishing out losing trade after losing trade, obviously your process needs some refining.

Yeah, and this Aaron I think is a really interesting question and I think one that you you are probably more familiar with than I am. I'm I have to say that um I was I don't know how solid all this stuff was is, but I've always loved reading about Um the turtle traders, right? Richard Dennis and the and the original trend followers. And uh there was a very interesting uh section of a book by Curtis Faith. where he talks about a certain strategy, a certain process.

that um that was very profitable if it if applied uh um you know with fidelity. but that had a streak of bad trades, losing trades, something like fifteen bad trades in a row. All right. And it's common and and so if you looked at the full trade and you just stuck it out, you would you make you worked out brilliantly, but Fifteen trades in a row, you're not losing much money, but you're losing, right? And and he said basically, psych this is just too psychologically difficult for most people.

So that becomes this in this important, difficult question, which is how do I know that I've got just a bad process in which I in which case I should abandon it? And how do I know that it's just uh my process has not yielded the results I want for some period of time, in which case I should I should hew to it. and uh with the faith that better times are ahead. So I I I I don't know how to answer that question but um but that is that is an essential ongoing question and um

It's a really hard one. It's a really tricky one. Now some other other realms it's much easier. I mean again that investing is tricky. And trading is tricky. In other parts of the world, I mean, again in athletics and business We we do know certain processes, certain best practices that tend to yield m the better results than others.

And so the you know a and again there's a lot of opportunity for people to to pay attention to those practices and those processes and to employ them themselves. So there's still a ton of upside in the world. I want to be clear to that about that. But But right, in some some some areas it's tricky. Yeah. No, it is a tough one. It is a tough one.

Um let's talk about skill versus luck. This is something you've also written about extensively and you talk about frequently. When did you begin thinking in terms of skill and luck? I'm just curious, like was there an event or was there something that triggered you to think this way? Because many people don't think this way. For sure. And you know, I grew up as and I still am a fairly avid athlete. So always in the world of sports. And uh, you know, I have to say if there was a triggering event

for my thinking more deeply about luck and skill, it was Michael Lewis's book Moneyball back in two thousand and three. And I was I'm no big baseball fan, but I thought the book was a it was certainly a fascinating story. And um, you know, the characters were interesting, but that also highlighted the importance of understanding um longer statistical patterns and uh ignoring these these short term bursts and and sticking to the process, all the themes we've been talking about.

are readily evident in that book. So that was probably the main thing and I just realized as I thought about that is that that applies to basically everything I'm interested in, right? It applies to investing, it applies to business, it applies to athletics. And so that sort of got me um more excited about it. The other thing that happened simultaneously was that Nasim Taleb wrote his book, Fooled by Randomness, in two thousand and one.

Um and I found you know it's a great book. It's very interesting. What I found, however, wanting was there was no quantification of where the randomness was and where I was likely to be fooled. And I knew that in some domains

there was no randomness or randomness didn't play much of a role at all. In other realms it was all there was, right? So so trying to get a little bit more serious about quantifying the contributions, relative contribution skill and lack two became became an important important mission in the whole thought process.

Yeah, I read that book, Fooled by Randomness, and I mean I I highly recommend it. It's it's just changed the way I think about everything. Like it's it's really had a big big impact on the way I think. Do you mind explaining to us why luck is more prominent in the short term? Yeah, so I think what happens is um that you might imagine um let's see if I can you know, you might imagine drawing from two different

uh jars. Right. The first jar is gonna have a bunch of numbers in them and that will be your skill number, right?

And uh it's gonna have some distribution but some skill number. And you're gonna draw and hold on to that. And then you're gonna draw from a second jar which has a bunch of luck numbers and they they average out to zero, then they average to zero, but you're gonna have good luck or bad number. And then you're gonna put those two numbers together and that will be your particular outcome.

If it's the case where the the luck distribution is much wider than the skill distribution Um it could be the case that you're gonna draw these different luck cards and by definition, you know, uh it it takes a very long time for luck to net out to zero and for your skill to shine through. So in the short run, you're gonna get outcomes that are very different from your underlying skill. But over long periods of time your luck is gonna net out to zero and skill will shine through.

So in the short run, luck tak luck can very often dominate, but over the long haul, luck nets to zero in many cases and then skill shines through. So that's w that's probably how I would try to

um describe it. Maybe another another approach would be something like this, which is you know that a coin is uh you know, a fair coin is fifty percent heads, fifty percent tails. But if I flipped a coin ten times, for example You certainly wouldn't be shocked, nor would your listeners be shocked to see seven heads and three tails.

But you know that if I flipped it a hundred, a thousand, ten thousand, and a million times that we would converge toward that fifty-fifty ratio. So again, much more luck. Much more randomness in a short sample small sample than there is in a much larger sample. So essentially what happens is the errors cancel out as the sample e expands and uh it distills down to the skill that's there. Mm-hmm. Yeah, otherwise known as the law of large numbers.

Um maybe I should have asked this question beforehand, but when thinking about skill versus luck, how are you able to differentiate the two? I I presume this is probably another tricky question, but is there a way to You know, quantify.

Yeah, so we we go through the in the book we have a chapter dedicated to specifically this question. But I'll I'll tell you I'll give you one technique that's actually a nice a really elegant one that we used and and that was sort of the cornerstone of that chapter. So there's a very interesting statist statistical property, right, which which basically says um

The standard deviation of distribution A s times the standard deviation is it squared. Actually standard deviation squared of distribution A plus the standard deviation squared of distribution B equals the standard deviation squared of A plus B. So they call this the Pythagorean theorem of of statistics, basically. So saying this differently, it's variance of independent distribution A plus variance of independent distribution B equals variance of A plus B.

Okay, so that's that's the key thing from a statistical point of view. Now you say, Let me um figure out uh professional basketball. How much is luck, how much is skill? Well, We know what the actual distribution of one loss records are for the for a for a team or th for the league pardon me for a season. So we know that. That's the A plus B part. So we have the solution.

And we can estimate what the uh win losses would look like if the game were totally random, right? If it was a binomial distribution. So if we have two out of the three pieces of an equation, we can imp we can infer what the third uh part of the equation is. And from that we can uh determine Or estimate how much is skill and how much is luck. So that's one little technique that's really cool, that's very elegant, that gets you in the right uh neighborhood. When we talk about skill and luck.

Are there certain outcomes where it's not as black and white as that, where, you know, the outcome is partially due to luck, partially due to skill. Is there ever situations where those sort of things occur? Yeah, almost every situation's like that, candidly. It's just a question of what is the relative contribution. So just going back to my my basketball example, right? So you say over over a season

the ratio comes out to be roughly ninety percent skill, ten percent luck. Uh you know, and that's again over a an eighty two game season. So there's an example where there's a contribution from both. And you know, in the shorter run, as I mentioned for small sample sizes, you're gonna get you're gonna get more of a contribution of luck. It tends to to even out over time. So absolutely you're gonna have uh it's it's very difficult to disentangle.

And it for all but the most extreme situations, you know, again, some things are almost all skill without much luck at all, like chess, like running races. Some things are clearly all uh luck like you know, roulette wheels or lotteries or close to that side of it. But almost everything we deal with in life, almost all those activities are between those two polar extremes.

When you look at great traders and great investors, in your opinion, what are the ultimate skills that they possess? They they tend to do a few things. One is they do think about the world probabilistically, so all the great investors I've ever seen think probabilistically. Second is they they are very disciplined in the sense that they always have the odds in their favor.

And this is from very small bets to very big bets. They always have positive expected value. And uh again, many people slip from that discipline. And then the third thing is they do understand the role of time. You know, this has come up Aaron a bunch of times in our discussion, you know, the you know, the small sample size versus large sample size, the role of time. And the great investors, the great traders understand uh the role of time.

And by the way, one of the rules of trading, for instance, is always live to see another day, right? In a sense you want to preserve optionality to make sure that you can leg into a a a more attractive environment should it arise.

So that's part of that as well. So so those are those are certainly qualities. Um I always like to talk when I talk about this I always like to cite uh the great uh cigar chomping gambler uh named Puggy Pearson and uh he's a legendary guy in his day but He basically said there's only three things you need to know, uh this the sixty forty end of a proposition, money management, and knowing yourself.

And that pretty much sums it up, right? Sixty forty out of a proposition means do you have edge, right? Are you on the proper side of that? Probability. Money management means knowing how much to bet when you're in a you're in advantage, right? And being making sure that you live to see another day. And knowing yourself is uh recognizing your own shortcomings, psychological shortcomings.

And making sure that uh you manage your own uh foibles. Uh that there's not much you can say that's better than Puggy Pearson's advice on that one. Is he still around today? He passed away a few years ago, um, unfortunately, but again, very very colorful guy and uh in many ways very admirable. Um by the way, the there the he's legendary and and there was legend that

He was the great the greatest money putter of all time. So he would He would lure professional golf players out on the on the golf course and uh would putt them for ten thousand dollars or twenty and apparently he had uh He had ice running through his veins and and could and could sink those putts every time and and took uh took some of these golf pros for a lot of money, which is which is a great story. That's very cool. I do take quite a bit of inspiration from these like really great

uh gamblers and people who are just have mastered probability pretty much. I think um I think that's a great example you gave.

Michael, on that note, let's uh let's wrap this up. So if someone wants to find out more about you, where is the best place they can go? I know you've got a website, you're on Twitter. Yeah, I think Aaron, you nailed it. Uh uh Michael Mobison dot com uh is one good site and that's got mostly ri links back to the books.

And then uh my Twitter handle is MJ Mobison, uh at MJ Mobison. So uh you know I don't I'm not super active on Twitter, but I do try to tweet out some of the stuff we're working on, things that are interesting. So those are two uh those are two good ways to to keep up with what I'm working on. Okay. And just to help everyone listening out, is how do you spell your last name? Yeah, so Michael Mubison would be M I C H A E L and then the last name is M A U. Be as in boy O U S S. I N

Mobusin. Thanks for that, Aaron. I appreciate it because I know it's not an easy one. Yeah. I'm sure you've been dealing with it your whole life. Um Now you've also read in several books. I think you've read in is it maybe four or five books now. If someone hasn't read any of your books, is there one in particular which you suggest would be a good place to start?

It's like tell asking me if my for my favorite child. No, let me uh I'll give you the the the ten second. So so this the success equation's about luck and skill. So if that topic piques your interest, that's the one to go to. Think twice, we discover discussed a bit. That's on decision making and again where your mind wants to go one way and there should there's a better way to think about it.

I wrote a book called More Than You Know and for people that have um a lot of intellectual curiosity and like short chapters of books, it's a wide ranging book, goes all over the place. It's not uh there's no flow to it, you can pick it up anywhere you want. but lots of interesting essays about science and complexity and psychology and investing and so forth. And the original book I I co authored with my mentor, Al Rappaport, is called Expectations Investing, and that's much more

You know, here's a process for investing by reverse engineering expectations. So that's a that's a little bit more kind of down the the mainstream finance. But th those are that's a little quick summary for each depends on your what what you're what you're in the mood for. Yeah, no, that's that's really good. And I'll put a link to all those in the show notes and they're also available on Amazon as well. So

Yeah, Michael, thank you very much for coming on the podcast. It's been a lot of fun. I've enjoyed it. Thank you. My pleasure, Aaron. Have a great day. You've reached the end of this episode of Chat with Traders, but rest assured, there are more episodes. soon. I'd love it if you'd leave a-