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For over a decade, David Tian, Ph.D., has helped hundreds of thousands of people from over 87 countries find happiness, success, and fulfillment in their social, professional, and love lives. His presentations – whether keynotes, seminars, or workshops – leave clients with insights into their behavior, psychology, and keys to their empowerment. His training methodologies are the result of over a decade of coaching and education of thousands of students around the world. Join him on the “DTPHD Podcast” as he explores deep questions of meaning, success, truth, love, and the good life. Subscribe now.
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HENRY CHONG is our special guest speaker on this episode. Henry is Director of Fusang Capital, a fund management company that manages the assets of multi-family offices. He is also a Director at the Portcullis Group, Asia’s biggest independent group of trust companies, providing comprehensive wealth administration to high-net-worth individuals, providing a one-stop shop for corporate, trustee, and fund administration services to individuals, family offices, philanthropies, private banks, and investment managers. Henry is a graduate of Oxford University with a B.A. (Hons) in Philosophy Politics & Economics and is a founder of the Oxford Economics Society. He also holds a M.Sc. in Behavioral Science from the London School of Economics and is a Fellow of the Royal Society of Arts (FRSC). He will be sharing with us from his deep insights in behavioral economics, finance, health, and psychology.
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Episode 19 Show Notes
2:27 What is a heuristic?
5:59 This is how to apply heuristics in life
11:15 How decisions are made in high pressure environments
15:37 Are we unconsciously using heuristics in our daily lives?
18:55 Where do we do better — algorithmic or heuristic thinking?
22:47 Understanding heuristics through the modern portfolio theory
27:58 Are all decisions emotionally driven?
30:00 How our brains operate in decision-making
33:35 This is how heuristics can be extremely useful
36:56 Relating the paradox of Buridan’s ass to our emotions
42:18 One of the most powerful heuristics
44:17 How artificial intelligence relates to heuristics
Dr. David Tian: Welcome to the DTPHD Podcast. I’m David Tian, PhD, your host, and I am joined by my guest, Henry Chong.
Henry Chong: Hello.
Dr. David Tian: Hey, Henry. There is a storm going on in Hong Kong.
Henry Chong: Yes. It is a super typhoon.
Dr. David Tian: It just started recently?
Henry Chong: Yeah, like late in the middle of last night, actually. I’m actually looking out the windows now. Just got upgraded to typhoon 10. It’s actually not that close to Hong Kong, but I think it went right over the Philippines and then it’s going to right over south in China, so we’ll see. You’ll probably hear a lot of background noise.
Dr. David Tian: Yeah. It’s a pretty big deal. I have some team members out in the Philippines and it took out their internet for a while.
Henry Chong: Yeah. So if I suddenly disappear, you’ll know why.
Dr. David Tian: Yes. And if you hear lots of wind in the background, that is what’s happening and it will just intensify over the next few hours. So, there’s no getting around it, but I appreciate your dedication, Henry, for being on. So, if this is the first time you’re listening to this, I have been for over the past 12 years helping hundreds of thousands of people in over 87 countries attain success, happiness, and fulfilment in life and love. Henry, how about you introduce yourself quickly?
Henry Chong: My name is Henry Chong and I am the CEO of the Fusang Group. We are a broadly diversified financial services group. We’ve got arms helping families do investment management, giving them peace of mind knowing that their assets are safe across the generations, but we’re also undergoing a number of fintech projects including in the blockchain space, which is some of the more exciting stuff I’m working on now. But here, I always enjoy coming and having conversations with you, David, about life, love, wealth, and happiness, and many other things besides. So I think today is heuristics that we’re going to go talk about.
Dr. David Tian: Yes. Great topic, it’s one that we’ve been thinking about discussing for a while. So, we’re finally getting around to it. And Henry, why don’t you start us off on just introducing the concept and why it’s important?
Henry Chong: Following on from our last podcast, I actually thought it would be very interesting to talk about heuristics, talking about things like resilience and antifragility. I think heuristics are really interesting in trying to actually apply some of that. Now, when I say heuristics, I mean as opposed to algorithmic decision making. So, an algorithm is a pre-programmed set of rules. We’re probably all familiar of the term because computers use this to make decisions or to run code or programs. There are some cases when having an algorithm as a human may work. Here’s a formula. Here’s a set of rules, follow it.
Perhaps, for example, talk more about this later but let’s say you’re trying to build a bridge. There are very specific rules and laws that you need to follow to make sure your bridge doesn’t fall down. You want to make very specific calculations. Right now, in a super typhoon like in Hong Kong, the way in which you construct certain buildings, you want to make sure that they are tolerant to winds and you can calculate the fault loads on these buildings. But a heuristic is what you call a rule of thumb, where it’s not a specific calculation; it is just some kind of broad based rule. I believe that especially for human decision making, these broad based rules actually can… They’re not only easier to execute, but in many cases actually result in better outcomes.
Let me give you a very simple example. For a very long time, people have been trying to program robots to do motion tracking. So, a very simple thing is in baseball. Someone hits a baseball and you’re trying to get a robot to run and catch the ball. Now, to do so with an algorithm is very, very difficult. I need to calculate a huge number of variables, the flight trajectory of the ball, wind interference, and to try and calculate exactly where that ball is going to end up and have the robot go to that spot and catch it is a nontrivial task. Or I can just program the robot to be like a person. So, people, when they’re trying to catch a baseball, they don’t calculate the flight trajectory of the baseball and figure out where to run to. All a human does is follow a very simple rule: Keep your eye on the ball, start running. And if the ball stays in roughly the same fixed place relative to your vision, you’re on track, right? If you’re running further than the ball, the ball will drift backwards. If you’re running ahead, if the ball is ahead of you, the ball will drift forward.
And as long as you can keep a fixed gaze on the ball as you run, as you run all the way forward, you will naturally hit the spot at which the ball’s going to land anyway. Now, whether or not baseball catchers realize it, this is what they do. Likewise, airline pilots are trained that if you want to figure out when you’re flying you’re going to crash into something like another plane, all you need to do is look at that plane in your windshield and fix a point. And if the plane is moving left or right of that point, it means that you’re not going straight towards it. If you’re looking at a plane in your windshield and it doesn’t move left or right and it’s just getting bigger, that means you’re going to crash into it.
These very simple rules let you perform what are mathematically very complex calculations with these so-called dumb heuristics, and yet they work very well. Those are really basic examples of heuristics. Let me give you some examples of situations that are seemingly complex but where the application of basic heuristics actually has led to very good outcomes, I suppose. One famous example is in emergency rooms where doctors, for example, someone comes in and you’re trying to figure out whether or not they are having a heart attack. Again, they are algorithmic ways to try to figure this out. I can measure blood pressure. I can look at vital signs. I can do all kinds of calculations, or there’s a checklist that doctors go through. The famous ‘does your arm hurt’, et cetera.
And by following these checklists, with a very high probability rate, I can determine whether or not someone’s having a heart attack. Likewise, doctors do the same thing when a baby is born. They go through a checklist to figure out if that baby is healthy or not. Famously, one of those points is whether or not the baby is crying. If the baby is not crying, that is a very bad sign. Whether or not a baby cries when it is born is statistically a bigger sign of whether it’s healthy or not than trying to measure a whole bunch of vital signs. And so, even in these high… I would say especially in high pressure, high context environments, heuristics are very helpful because you do not have the time you need to go and do a bunch of calculations, and gather a bunch of data. Human beings under pressure are not always the best at thinking algorithmically.
Versus they found that when they give doctors just a checklist and you say, “Just follow the checklist”, the same for airline pilots, “Just follow the checklist”, you can resolve with very good outcomes. I don’t know if you’ve come across these kind of things, David.
Dr. David Tian: That’s perfect. The pilot example is really great because there’s actually a lot of research that shows, and reporting that’s shown that pilots are chronically overworked. So if they’re required to use their creative thinking on a regular basis, considering how overworked they are, we’re all in trouble. So in addition to autopilot backup systems which actually run on a kind of heuristic basis as well, we’d be in trouble. But it also works in business. So, as you were talking, I was thinking a lot of what I’ve been reading in terms of encouraging the individual to be creative and empowering the individual to find personal transformation, when it comes to a system, it’s actually detrimental to count on any of your team members having to exercise original thinking.
I mean, it’s great if they do obviously. And if you have team members who are that talented, by all means, if you want to give them the creative freedom. But a business that’s well-run is one that can be run by monkeys, because you have a system in place that has a lot of room for error, and in terms of these checklists and SOPs, that you can just follow the book, so to speak, and you’d be fine. So, you’d have all of these things in place so that when someone’s new and they join, just new, don’t know anything else about the company, or the system, or how things are run, but they have the book, they can run the company smoothly. In many ways, the military is like that.
So, these high pressure situations in which the margin of error is quite small, you want to make it dummy-proof. I was thinking in terms of personal transformation, how can we bring this down to the level of the individual and make it so that this is a transformative or an empowering thing for individuals? So, one of the things I was thinking was in terms of how we develop expertise. So, you know that somebody is a master at something when that person knows where the exceptions lie. So, rules of thumb, these heuristics are awesome for just going into autopilot and not making major mistakes. But then there are those times when you have a case at the extremes where the rules of thumb break down or they don’t apply. You have to know which situations those are. In order to know that, when these rules of thumb don’t apply in this exceptional situation, you’d have to have cultivated your intuitions.
It’s a Catch-22, which is that, to become an expert at something, you have to know where the rules break down, but you can’t know where the rules break down unless you have the experience. So, the way they get that experience is understanding the basic rules, the heuristics.
Henry Chong: And they don’t even have to be at a conscious level. There’s a famous example of, I think it was Daniel Kahneman or maybe it was Gerd Gigerenzer I think who was a famous researcher in the heuristics field. He was doing research on firefighters, I believe it was. So, he was very interested in professions that have to make decisions in high pressure, high context environments, not where you can step back and do some calculations. He was looking at firemen and there was this one case where a senior fireman was in a building, and suddenly he said he just didn’t feel right, and he told everyone to get out. And sure enough, it turns out that what they thought was a very safe fire actually had this huge fire, I think it was one floor below, and the whole building collapsed because the foundations were burning.
At that time, he didn’t know why. He felt that the whole building was unsafe. He just knew it was. And upon reflection later, he was like, “Oh yeah, I think it was because something about the floors were too hot.” There was something that was happening that was just not normal and not right. And even though he didn’t consciously realize it, he’d gone through so many of these situations that he said, “Wait a minute, this is not normal.” But again, I think it’s very important to first point out that before you worry about being a master and worrying about the edge cases, you first need to be able to be good at the normal stuff, and very good at it.
I remember my old basketball coach always used to say, “People who play in the NBA don’t get to the NBA because they’re really good at making really crazy, fancy shots. They’re there because they’re really good at making layups, really good at making free throws, and they do it better than anyone else in the planet. Now, once you’re in the NBA, what separates the very all-time greats from just the best are those edge cases, but you shouldn’t worry about those until you’re at that level. I think that’s one important point. I think another important point, that whole thing about checklists was really good, but I think people need to be very careful not to misunderstand what we’re saying.
And in many organizations, they end up coming with these SOPs, all these procedures that are very complex, and they say, “Follow these 36 steps and everything will be perfect.” And the truth is, in the real world, nothing’s ever perfect. And trying to expect people to follow complex procedures rarely works. And so, these checklists that we’re talking about in hospitals for example are actually not complex procedures. They are very basic ‘check these five things.’ It won’t be perfect but you’ll get there 80% of the time. And in a high pressure, high context environment, that 80% realistically is much better than you would’ve gotten with trying to make a decision any other way with the time and information that you have.
Taleb, talking last week about antifragility, he talked about this as well, about how when you over-optimize, and you walk too close to the edge, and you have no margin for error, that’s when you fall off the edge. And in many cases actually, taking a step back and trying to implement an 80% solution is actually usually better. Because many cases we have no idea where the edge is anyway exactly. And so, if you just pick a good enough solution, that’s what will get you there most of the time. And if anything, I would argue that masses of people who recognize that and can consistently produce the good enough 80% solution in all environments because the world is complex and the world changes.
Amateurs can do things perfectly once. Professionals are people who can execute again and again. I think that’s a big difference between them.
Dr. David Tian: In the examples we’ve been using so far, a lot of them are where we impose or where we want to impose heuristics onto an existing system. So, business, military, firefighting; these are all examples where there was chaos and then we want to get rid of this chaos by imposing order. And one of the profound things about heuristics is — Gigerenzer a good example of a theorist in… Actually also works in philosophy. And at the university where I was teaching as professor in philosophy, I led a moral psychology graduate seminar where we read some of Gigerenzer and the idea of heuristics as how human beings arrive at moral decisions.
We actually already use heuristics all the time, we’re just unthinking, uncritically, mindless about them, but the human brain probably, there are very good theories on this, that the human brain works in modules that are related by if-then rules that work like heuristics. So, we’ve often decided on the heuristics that we use for moral decision making or even just general decision making, like whether we’re going to buy this box of cereal or that brand of toothpaste based on things that happen to us in our childhood, like when we were young and we needed a way to make decisions quickly.
Often, we just uncritically adopt and carry forward the old heuristics. That’s how we make friends, for instance, and then that’s how we choose an avatar in a video game. These little decisions that were made when we were young, we carry them over and then in adulthood we start to run into problems. We are wondering, for instance, I get asked a lot, “How can I learn from…?” and this is already an advanced question, because at least he’s asking how he can learn from a mistake, but “how can I learn from a bad relationship beyond the fact that so and so was a failure, like I can blame him or her for these things. What can I learn to take forward into the next relationship, in the next dating situation so I don’t make this mistake again?”
Because we’re so wedded to the unconscious, uncritical heuristics that we use before that are no longer serving us, we’ve lost touch, we’ve lost our ability to devise heuristics critically. This is part of the problem in moral psychology. We make bad ethical decisions because we learn how to make decisions using heuristics when we were six years old, and we made another set of heuristics when we were eight because we got hit by a baseball walking across the field when there was a game going on. We learn these things. Like, okay, if there’s a game going on, let’s not walk across the field. That would be just a rule of thumb. You don’t stop to think about what all the exceptions could be, what’s the logical reasoning behind this. “I learned it through a mistake, I’m going to adopt this and move forward.”
When we get to adulthood, we start to realize that those actually limit us. That’s one of the profound things for me as a coach for people trying to transform themselves personally, especially in relationships and in lifestyle, to think more deeply about the heuristics that you’re already using to choose a dating partner, or how you’re spotting these red flags in a relationship, or the green lights to go ahead, and to think deeper about what are the best rules of thumb for you to use because you can’t expend all of this brain power on lots of different decisions going on in your life.
Henry Chong: Kahneman and Tversky talk about what they call system one, system two thinking and humans are really bad at algorithmic thinking. We’re actually really good at heuristical thinking. If anything, the combination of heuristical thinking plus experience is probably our one edge against the computers. That’s what we do quite well. And what you should do is you should do your slow brain, so to speak, the analytic part of your brain to stop and think about what heuristics you want to put in place when you have time. That’s when you can think about these things.
And then you can say, “Okay, in the moment, in the high pressure environment, I have these heuristics and I just execute according to them.” That’s what a lot of professionals do. They already have these heuristics whether they realize it or not, built up, combined with experience, which is basically pattern recognition. And then when you combine the two of them, you can end up with hugely better outcomes than if you’re trying to use algorithmic thinking in the moment. But I think that also this links back into… We’ve talked in the past about principles. If you believe that there are three key principles in some area that result in 80% of the outcome. Actually, just trying to come up with heuristics that will get you that 80% result will get most of the way there. And again, to combine three different things, heuristics, principles, and what we’re talking about last week of resilience. It’s very hard for anyone to predict the future.
Nevermind whether or not you’re good at algorithmic thinking or anything like that, but the future is uncertain and as mathematicians call it chaotic. It’s very difficult to predict what tomorrow will look like based on today in some areas like typhoons. The path is very unpredictable. And so, given that the future is uncertain, trying to calculate the edge is impossible anyway so you should just come up with heuristics that will get you 80% of the way there. As I speak, my whole building is actually swaying ever so slightly, which it’s designed to do in a typhoon. You need that resilience.
Henry Chong: What floor are you on? You had a pretty baller view when you showed it to me.
Dr. David Tian: And you’re up in the mid-level, so it’s on slant?
Henry Chong: Yeah, it’s pretty high up. But you need that, right? You need some degree of fault tolerance. If I tried to build a building so it’s just right, that’s a really, really bad idea. I want to build a building so that it’s got enough tolerance built in that is not treading the edge, and I want to build some degree of resilience into that. Hey, if it need be, just sway, it’s no big deal. Because buildings that don’t sway are buildings that fall over. And I think if you think of the world in that way and you say, “I kind of walked too close to the edge. How do I build in heuristics that will get me 80% of the way there that will help me build in that resilience to my system almost always?” Especially as a human being, you will come up with better outcomes than if you sat down with a pen and a piece of paper and tried to work out the math, so to speak.
Dr. David Tian: Going back to one of the first things you said about the difference between heuristics and algorithms… Trying to get more clarity on that distinction for you is, are heuristics… Would heuristics, at a more advanced level, would they become algorithmic?
Henry Chong: My definition isn’t great because I guess an algorithm is just a programmed set of rules and you can argue that consciously or not that’s how our brains operate at some kind of instinctual level. What I mean is instead of trying to sit down and work something out, so for example to say… Let me give you another example. One of the foundational theories in finance is this thing called modern portfolio theory that a guy called Harry Markowitz came up with and actually won a Nobel Prize for. He outlines a way in which given certain information, information about the future expected returns and expected variances on various asset classes, you can come up with the optimal portfolio allocation. Optimal means best. Given this data, I can come up with the best portfolio allocation.
And like I said, he won a Nobel Prize for it, and I think either shortly after he won his Nobel Prize, someone asked him, “Is this actually how you construct your portfolios? You do these calculations and you allocate?” And you said, “Oh no, I just use an 1/n strategy, meaning if I have five asset classes, I allocate evenly across the five of them, 20% each.” And so, even him who came up with this idea of how to fully optimize things, he himself did not allocate this way because he said it’s just too hard to calculate.
And Gigerenzer for example has done some really interesting research to show that for you to apply modern portfolio theory, you need about 200 years’ worth of historical data. And for most asset classes, we don’t have anything close to that. Most stock markets are not actually that old. So, we just don’t have enough data, even, to calculate how these things should look. Versus actually if you just follow a so-called naive strategy of allocating evenly, it won’t be perfect but it will also avoid risk of ruin. Chances are you won’t go bankrupt that way. I mean, again, given the way asset classes work, even if one completely blows up, chances are you’ll be fine especially over some period of time versus trying to calculate, and hit the edge, and probably miss it. And I think that these sort of naive strategies so to speak, can be shown to work really well in many cases.
For example, Gigerenzer did some other interesting research where he showed that… I think he walked around some town in Germany and he just plucked people off the streets, gave them a list of stocks and said, “Which names are you familiar with?” And if you invested in the names that people, just by pure name recognition go, “Oh yeah, I’ve heard of that company”, you have outperformed your market. I mean, if you think about it, it makes sense, especially if it’s a consumer or retail-facing company. If people in the street recognize their company name, that’s a good sign, that’s better than average. And so, just really, really, sort of so-called dumb heuristics like that can actually end up giving pretty good outcomes.
I guess the reason why I think that is, is because humans always make decisions emotionally. What I mean by this is that, again, people like Kahneman talk about system one, system two, almost like they’re two separate brains, but that’s not the way it works. There’s the famous idea that Descartes came up with of Cartesian duality. “I think, therefore I am.” Many people mean that to talk about existence, but what he really means is that there is a thinking me separate from the experiencing me, my body. The recent behavioral science has shown that that’s just not true. You don’t have your body and your mind. They are integrated. Your mind cannot make decisions without the body.
So there’s the famous example of Phineas Gage. He’s a guy who had a, I think it was a railway spike, through his brain. He was perfectly normal and healthy except for the fact that he could not — he had no sort of emotions left. What one researcher found is that they did this experiment where they had two decks of cards and one deck was rigged. The idea was he was supposed to keep drawing from the two decks until he could make a decision as to which deck had more high cards than the other. And again, one deck was clearly rigged.
And he was starting to draw. The interesting thing is most people who do this, even before they consciously pick one deck and say, “That is the good deck. That is the bad deck”, you can measure a galvanic skin response. So, they will have a primal stress response to the bad deck. Their heart rate will go up, their pupils will dilate. So, their body will know that that’s the bad deck before their brain does. But Phineas Gage couldn’t do that. He was drawing from these decks and halfway through it’s clear that one deck was a rigged deck. And if you asked him, “Is that deck the rigged deck?” He’ll go, “Yes.” And then you’ll say, “Which deck should you choose from?” And he’ll say, “I don’t know.”
And so, even though intellectually he was perfectly able to make these calculations, he was completely unable to make decisions. And again, a later research has shown that all decisions are emotionally-mediated. What I mean by that is when you have a thought in your brain, that thought has to be mediated through the brainstem, the amygdala and also the brainstem. That means that how you feel, your state, as people like Tony Robbins talks about, really does influence almost every decision that we make. But it doesn’t actually have to be the other way around.
Your body, in a sense, can have reactions to things without your brain involved. So, a lot of very primal-like gustatory responses or just the — we call them instinctual responses to them, we have these without even conscious thought. Spiders are one. A lot of people, you see a spider. Before you can even consciously go, “Oh, that’s a spider” and “Oh, I’m scared” your body will flinch and will react to it. And so, if anything, it’s the other way around. Our body makes decisions and sometimes our mind is involved, but not always.
Dr. David Tian: Yeah. The latest neuroscience, it’s not even that recent it seems because pop culture or pop consciousness is always slow in catching up, but the literature is at least 15 to 20 years. Antonio Damasio is a great figure on thinking about the brain. The view is that the brain isn’t just located in that part of your skull, through the spine, and then through your autonomic nervous system. Neuroscientists think of the brain as your entire body. And there are parts of your brain that don’t enter the conscious cortexes or the frontal cortexes and the processes happen, let’s say, the brain in your gut. So, that’s a way of talking about the mind-gut connection.
Henry Chong: Yeah. There’s more serotonin in your gut than in your brain. So when people talk about making gut decisions, there really is almost a whole separate brain down there. And again, how you feel, what you eat, that really does affect your decision making.
Dr. David Tian: Right. There’s actually quite a lot of studies on people who are blind but who can see, so blind sight, where the visual stimuli is being picked up through the eyes. But because the connection between the eye and the frontal cortexes have severed, or the main cortexes have severed, but the eye is still picking up this information, there’s still an emotional reaction to a very angry face or a very sad face. The people who are blind can’t explain why they have this feeling, this feeling of fear or whatever, but they have the bioconductance measures for it.
There’s a lot of research. There’s a lot of evidence for the fact that the brain, as we normally think of it, is a whole body thing. It’s connected through the spinal cord to the rest of the body, and we’re mostly confining our view of what’s happening in the brain, in the mind, so to speak, in a very small part of the brain and it’s mostly the prefrontal cortex.
Henry Chong: That’s why heuristics can work because you’re using, in a sense, all of these information sources as opposed to just one of your information sources which is your prefrontal cortex.
Dr. David Tian: Yeah, and if we had to depend on the prefrontal cortex to make decisions, we’d be royally screwed. Like you were pointing out earlier, one advantage against AI, and this is a very scant advantage, is that because we’re very slow at computing but we are experienced over millions of years in creating heuristics mostly unconsciously as I was explaining. I’ll give an example that everyone hopefully can understand: driving. When you first drive and if you do it when you’re young, like I did it as soon as I turned 16, took the driver’s course in Canada and it was pretty freaky. Like, driving around the neighborhood is pretty easy, but you get on the highway, and everything’s going real fast. If you didn’t have those heuristics from the paper class, like the classroom, you would have to start calculating everything.
So now we have driverless cars and the computers can calculate everything, hopefully. For instance, one of the heuristics I was given in drivers ed, and hopefully this still applies, is that on the highway, you should stay three to five seconds behind the car in front of you. So, I’ll just watch the car in front of me pass one of those divider paint things, and then I’ll just count one steamboat, two steamboat, three steamboat. If I’m passing then, I’m at a decent distance depending on my speed. And if I’m going like one steamboat and I’m right behind him, okay, I’m too close. That’s it. That’s all I have to think about.
But if I didn’t have that heuristic, I would have to calculate my speed. I would have to do physics. I would have to estimate his speed, my speed, how quick my reaction time is, how good the breaks are, all that other stuff that hopefully your driverless car, computer, is doing super quick. There are tons of other heuristics you’re given in drivers ed. Because if you’re required while you’re driving to make calculations to decide what distance to stay behind the car in front of you, we’re going to have a lot of accidents. Another heuristic that I see many people not applying is to leave a car space in front, or two car spaces in front, at a red light and to be watching mostly your rear-view mirror when you’re at a red light.
But these are just good rules of thumb. You can break from them if you understand why we have these rules of thumb, these heuristics. But your point about heuristics have to be simple. They have to be simple for our human brains to comprehend and enact quickly, otherwise they’re going to be useless because then we might as well — then, we’re into computing land. And there’s a heuristic that — there’s a movie that I saw recently again called Karate Kid. I just love Karate Kid. There’s just so many ways to use this movie. Many of you listening might have watched the Jaden Smith and Jackie Chan version. I rewatched the original with Miyagi and Daniel-san for nostalgia. I think that’s actually better for making these points about the Zen approach to life or actually the best approaches to life.
And there was a scene in it where Daniel-san is freaking out because he’s learned basically eight moves by doing chores like painting the fence and so on. It’s the night before the big tournament and he’s freaking out because he’s like, “I’m thinking about everything I don’t know. There’s so much I don’t know about karate.” And then Miyagi-san’s like, “Yes, that is right but we have confidence not in…” I can’t remember the exact line, but he said something like, “not in quantity of knowledge but in quality.” And then he wins the tournament. In many ways, you’ve heard this probably before like Bruce Lee’s thing about fearing the man who has practiced the same kick a thousand times versus the man who has done a thousand moves once each. The idea there is you have these fundamentals, and they’re simple, and you’ve trained them over and over, and they’re good heuristics.
So in fighting, there’s a lot of heuristics. These are just examples. Now, when it comes to emotional decisions, so again bringing it back to a subject that I know a lot of, our audience cares about, which is relationships. There are these heuristics that a lot of people didn’t discover when they were younger and they just sort of… No one taught them explicitly about social skills, or about dating skills, or about how to assess which emotions are accurate for the situation. Does she really like you? What heuristics do you have to determine that? I’ll give an example of where there’s a good heuristic that people can use.
So, a lot of the audience are men. Here’s a heuristic. If you meet a girl in a club, a nightclub, and she’s a regular, the chances are good, the heuristic is, don’t trust her, don’t believe anything she says. Take it all with a grain of salt. However, you need to know when this doesn’t apply. There are exceptions. And this is where that… You get into the edges of the use cases, or where the use cases break down, where the heuristics break down, where it doesn’t apply to the exceptions but you have to know when it applies or when it doesn’t apply. I come back to that original paradox.
And then it links up to you were saying about emotions. So, the fact that our decisions are all emotionally laden… So, here’s an example from philosophy. I think this is from medieval philosophy. Buridan’s ass. It’s a great concept. Buridan’s ass; Buridan’s donkey. So, the idea is there’s this donkey that doesn’t feel anything, has no emotions, has no preferences. And so, our preferences are driven by our emotions: What do we want? And it’s stuck at this fork in the road and it’s equidistant, the same distance between two equally-copious amount of food, exact same distance, exact same amount of food, and it just can’t make a decision because they are equally desirable, or according to the heuristic it has, got to the place that has more food in the quickest amount of time.
So, it’s just looking at the food, left, right, left, right, and then it dies. That’s Buridan’s ass, that’s philosophy. The point is that without our emotions, we wouldn’t even get started. We would literally be like the AI that we have now, where there are no intrinsic desires. I don’t know about now. They might be pretty freaking enhanced, but I mean like before, where it’s just literally a computer. A lot of people don’t realize how important, especially men, well, people, raised in a Confucian setting or the typical Asian culture that doesn’t value emotions, in fact wants you to get rid of emotions and just make decisions logically, not realizing that without the emotions attached to the — well, without the preferences which are emotionally laden, which those actually are emotions, we won’t get off the ground with our decisions.
When it comes to these red flag issues, or heuristics about meeting people in nightclubs… There are so many dudes who grew up with this heuristic that fun is bad, or alcohol is evil, or more commonly drugs are evil. This is coming off, I don’t know, a week or so off of Elon Musk and Joe Rogan’s podcast which seems like a lot of people talking about, in my circles anyway, and I love it. And I found out later, we’re actually talking with Stefan Ravalli about meditation, that there are people hating on Elon for taking a drag at the end of the interview. I haven’t even gotten to the end of the interview yet, it’s so long, but I did see the meme and I was like, “Wow, that’s so common, people have a heuristic that they haven’t thought about, that’s uncritical, that’s just this mindless thing.” They heard like in the Reagan era, “Say no to drugs”, or something like that.
In the 1950s, it was say no to dancing. So, Elvis Presley was the devil and there are still a lot of colleges that ban dancing and alcohol back in the 50s. That was a heuristic that was uncritically adopted. Like I was saying, most people adopt these heuristics uncritically from outside sources and/or from when they were young. And when it comes to meeting people in nightclubs, generally, a good heuristic is don’t trust people that you meet at nightclubs because of the type of personality that kind of environment would attract. But there are lots of exceptions.
I used to go to nightclubs a lot. I liked to think that I was an exception, and I know plenty of other exceptions, but they’re still the minority. It’s important to know why that heuristic is in place so that you know when to break from it and which times are exceptions. And once you start to develop that expertise, then you start to develop this sort of mastery. So in martial arts, you know… Like, Brazilian jiu jitsu’s a great example. There’s a guy that everyone should follow who is interested in strategy, John Danaher, who is this BJJ coach. He’s every day posting these longform Instagram essays, and a lot of them are about where the heuristics break down because at that more advanced level, that’s what you’re dealing with.
Because everyone, by the time you get to intermediate or whatever the belt level would be, knows the general heuristics. The guy’s going to go into a kimura, their counterattack is this. So, it’s like chess, you’re already thinking really far ahead. And then you start to get into semi-algorithmic thinking where the heuristics get more and more complex but they’re still heuristics for the expert practitioner because they’re still simple for the expert practitioner. But they got there by practicing the basic heuristics. And then we have to develop a heuristic to figure out what heuristics to adopt at the beginning.
This is where we get the cognitive bias towards authority, and this is a useful one. This is one that’s ingrained in us probably in our DNA because it was evolutionary adaptive to look towards authority so you don’t have to reinvent the wheel every time. But one thing you can do is to look for people who have done the thing that you’re trying to do and what heuristics do they adopt. So, we’re talking about heuristics on this podcast.
Henry Chong: I think learning how to learn, the art of learning, those are some of the most powerful heuristics because that’s the root one which helps you learn others. And I think it’s the really interesting thing, is that that’s exactly what AI researchers are doing these days, trying to teach the AI how to learn. Talking earlier about AI trying to be algorithmic, actually, a lot of the cutting-edge ways in which people are designing things like self-driving cars are to try and mimic human decision making more and more. There’s a big difference between the way that Tesla and Google have been developing their self-driving cars.
So, Google’s idea, in a typical Google fashion, was put tons and tons of sensors on the car and we will try and write better and better algorithms to teach the car how to compute data, and make decisions, and drive itself. They’ve been semi-successful. They still haven’t launched any kind of working product. Tesla, on the other hand, for better or for worse, has launched some kind of working product, and their approach is very, very different. For about two years before they even told anybody that they were rolling out this kind of functionality, they already put a bunch of sensors on cars, nowhere near as advanced, no cameras, they were just sort of lasers, pretty cheap. By collecting reams and reams of data for two years, all of these Tesla drivers all around the world were driving and they were gathering data on how these cars are driving, and basically teaching their machines as to how to actually operate.
So, rather than try and program, “What should we do in this situation at this intersection?” They just said, “What has the last thousand drivers done at this intersection?” And if 99.9% out of them all do the same thing, that’s probably a good standing point. And a lot of machine learning is really built this way, either by observation, like, how do other people do it… And you can feed in, for example, when you’re playing, trying to teach someone to play a video game, teach an AI to play a video game, you upload all of the games of all of the best players in the world. It studies that and models how these players do it, or it just does trial and error, and in a sense, it models itself. It keeps trying and trying thousands of times and then it figures out what are the optimal strategies to take, rather than trying to calculate, in some way, again, algorithmically, it just copies and models.
Some of the more advanced AI you see these days like AlphaGo for example, the famous Google DeepMind algorithm that played and beat the world Go champion, was based on a combination of algorithms, trying to figure out the rules and make calculations, and heuristic analysis, trying to copy how humans think, how humans make these kinds of decisions and how to sort of figure out the 80% solution when one line of thinking is no longer optimal and chances are I should be applying all my computing power along this line. So, I guess the scary thing is that AI is very rapidly trying to copy some of this heuristical-based thinking and that is why recent AI has gotten so much better so quickly, by applying computing power plus the heuristic analysis.
Dr. David Tian: Wow, that’s fascinating. I didn’t know that about the Google-Tesla issue. That’s so cool. So, we’ll see how that plays out. In individual or personal psychology, you’ve heard of this distinction between the maximizer and the satisficer. There are a lot of guys who have trouble with women and attraction, because they’re too uptight, because their temperament veers towards the maximizer. So, he wants to make the best decision and he will take a ton of time to do so because it matters that much. It’s actually a fear-based type of temperament and decision making style.
So, I give an example. If I have an event, the maximizers will usually sign up way in advance. And no matter when I announce it, they’ll sign up then. If I announce it two weeks ahead, they’ll sign up two weeks ahead and be pissed. If I announce it four months ahead, they’ll sign up at the four-month mark, and they’ll feel better about it because now they can get the best flight for the best price, and get the best hotel room, the best value, and they’re booking way ahead. There’s a lot of anxiety there if they’re not able to do that. And then I get emails because often, you know to — Actually, an attractive trait is being easy-going, and taking it easy, and realizing most things in life aren’t going to make that much of a difference if you make a decision early on or not, and to be happy with good enough. That’s the satisficer position, which is that 80%, 90%… Obviously, there’s a continuum there.
There had been studies that show that maximizers make marginally better decisions when you lengthen that amount of time they have to make the best decision. But their satisfaction with that marginally better decision is equal or is often lower than the satisficer who make a slightly less optimal decision but they do it quickly, and they move on with their lives, and they’re more satisfied with the decision because they have a heuristic. The heuristic is: good enough is good enough, and then they move on. So, speed.
So, you can see how when it comes to making decisions like in driving or playing chess, that sometimes if you can just get to the simple heuristics, you get the benefit of speed and then you learn quicker along the way. Everything just speeds up so much faster in terms of your learning and your learning curve. Along the way, you have these people who lose. They might win in the short-term, because maybe when you’re in the fourth grade, you’re not penalized for taking a lot of time to do something. But over time, especially when you’re an adult, waiting for just the perfect time to go talk to the girl at the bar, that means you’ll never talk to that girl at the bar or you’ll never talk to that girl at the Starbucks walking by. There’s just never the maximum or the optimal perfect time to go. And rarely in life is there ever the perfect anything.
A good heuristic is good enough and move, good enough and go. It’s no wonder that that’s actually an adaptive trait that females would’ve selected males out for that. There’s also this risk-taking element there versus the maximizer, but there’s also just a lot more room for enjoyment of life.
Henry Chong: Yep. I was in San Diego at Coronado in the U.S. Navy SEAL training base a while ago, and I was listening to one of the Navy SEAL commanders talk. He was saying that if you wait until you have the 100% perfect solution, you’re too late. Once you get the 80% solution, you go. And you’re saying actually, in today’s world, once you get the 60% solution, you should already go.
Dr. David Tian: Yeah, absolutely. I think my video is frozen.
Henry Chong: It might be my internet slowly dying.
Dr. David Tian: It might be the storm.
Henry Chong: It is getting stronger and stronger.
Dr. David Tian: It’s probably a good time to wrap up. I think we’ve covered a lot of ground here, way overtime, and hopefully you can batten down the hatches there.
Henry Chong: Maybe before we end, you’re talking about kung fu and training earlier. It reminds me of this old quote, and I’ll read it out quickly before we end. And he’s got this old master and he says… So, there’s this young disciple who is talking to the master and he says, “Why am I not advancing in my technique, master?” And the guy says, “Have you seen the sunset when the seagulls fly flaming across the plane?” And he says, “Yes, master.” And the guy says, “And the water from the waterfall hitting the rock without achieving anything?” And he says, “Yes, master.” And he says, “And the moon reflecting upon calm water?” And he says, “Yes, master.” The guy says, “That’s your problem. You keep watching stupid shit instead of practicing.”
Dr. David Tian: Oh, that’s perfect. That’s a perfect ending. Thank you for the quote, Henry, and let’s wrap this up. Thank you so much for listening. Henry, how can they get a hold of you?
Henry Chong: You can find me on my website at HenryChong.com. I share links to everything else there.
Dr. David Tian: Awesome, and you can find me at DavidTianPHD.com. We’ll put everything in the show notes for the links out, and thanks so much again for listening. We will be back again shortly, and hopefully Henry will be all in one piece as will Hong Kong. Alright, thanks so much for joining us.