Anything Can Happen is a Myth

Alright, Greg, we'll get restarted here now that I think I have my audio issues. So, hello, everyone. We're having some problems getting started in the recording this morning, but we have a great episode for you.

Our big-picture topic that we're talking about is this idea that "anything can happen" and how it's a myth, as far as I'm concerned. What I mean by that is, we talk about different interactions from humans, and people make observations that "well, they could do anything," or "anything could occur," or "we don't know what's going to happen." And a lot of times, it just simply isn't true.

Now, if you're trying to predict some Black Swan event, some major thing, that's really hard to do, right? You see that in economics or finance, different areas like that, where someone capitalizes on some rare occurrence, and they were like the only person that saw it coming. And it's so rare. We're not talking about things like that. We're talking about basic human interactions: what you can predict, what's likely, what's unlikely, what's known versus unknown, that kind of thing.

The idea is, when you have this approach, my biggest problem with people saying, "Well, anything could happen," the problem with that is that it increases the level of uncertainty, especially in anything like a high-risk situation or some extreme situation. And therefore, it makes it harder to anticipate likely outcomes. Meaning, if I go in and I'm going up to contact someone, Greg, and I'm going, "Oh, man, anything can happen," I'm not sure. One, it increases the uncertainty level, it increases the anxiety level, and all over the place. So, it increases my cognitive load.

This is the big thing that we talk about with our behavioral approaches with HBPR RNA (Human Behavior Pattern Recognition and Analysis) and how we leverage it to reduce the uncertainty. I don't want to increase it; I want to reduce what's uncertain. I want to get rid of unknowns. I want to focus on the things that matter. So that's what we do, and what we train people how to do. But there's a lot that goes into it, and I want to hit on some of the kind of big-picture topics that we don't typically explicitly get into, but that's what we can do here on The Human Behavior Podcast versus covering this stuff in a course or in a training course or something like that.

But we use things like Game Theory, and Probability Theory, and Bayes' Theorem. And we'll define what all that means for everyone, because we stick to the science and we use it in a manner for which it was intended, right? An actual use case in a sense that I'm not a mathematician, I'm not a high-level expert in those areas, but I know them well enough to use them in what we do, right? And I can point back to them and say, "This is where this comes from."

So there's a lot we're going to get into there. But I really want the big thing to be really sort of debunking this myth, this idea that, "Well, anything can happen," or "You don't know that." It's like, "Well, yeah, no, you do know that." And then get into things. Well, get into human acts, sort of as using games as an analogy. We'll talk about that, not just Game Theory, but games and how they reflect life. And then, how thinking in a certain manner, in what we're talking about today, can help your ability, can help your predictive analysis abilities.

When we're getting into everything I just brought up with "anything can happen," sometimes knowing what you might not know what's going to happen next, but knowing what isn't going to happen, or what's unlikely, is helpful. Meaning, going, "Well, I know none of this can happen over here, so I really only have to focus on these five things instead of these 50 things." So that's that reduction in cognitive load, that's a reduction in uncertainty that you're talking about, and now I can account for those. I'm talking about this big picture, but let's start with—I think we should start with games, Greg, if that's something you want to comment on.

Let's talk about what you just talked about first, because that's a great intro. And so this is my continued argument for improving law enforcement training and simulations, because what happens is we're focused on motor learning and motor control and speed—all of these elements that are not going to affect your decision-making when the time comes.

And I'll give you an example of that. You just talked about reducing uncertainty. So I saw a guy yesterday on a LinkedIn post that was drawing from a paddle holster that was appendix carry, fired all the rounds safely and quickly, and it was in the two-second range that he emptied his mag into a target. Do me a favor: when will that reduce uncertainty? That will reduce uncertainty at that one time in that person's life where the rotor hits the spark and the oxygen hits the fuel, and it's a thing where you have to draw, out-draw an opponent, and fire all the rounds in your weapon. I was a cop for 30 years; that never happened to me. Now, how many cops on the street has that happened to?

The idea is that you think you're doing great training, and I'm great with that type of training. But look at the Olympics. The Olympics is full of people that specialize in one thing for their entire life for the likelihood that that's going to come around. And that's not how things work. And that's why—I just want to throw that on the table and I want to say, "World according to Greg..."

That's actually a great way to, because we're using this Game Theory and games as analogy, but that's actually a really, really important distinction. And we'll get into how we define what games are. But that's a very... these analogies where it comes from sports performance to now we're going to talk and carry that over in military or law enforcement or high-risk situations, and it does not translate. It just can't translate because it's not the same.

What you've done is you've taken Monopoly and you've taken Checkers, and you're reading me the directions to play Monopoly and handing me a checkers board. It can't converge.

Because those are... because what you talk about, it's the scope. The scope is so limited in a 100-meter dash, like, that's it. There's no—you get to—there's a ton going on, don't get me wrong, it's not that it's not a complicated skill, but it's nowhere near as infinite as the potentials that could happen in something like human resources or police work, or being a teacher.

So my additional argument for improving law enforcement is that you have to understand the general limitations of a problem, limit the potential solutions. And what I mean by that is simply Probability Theory. Most people say, "Well, Probability Theory, mathematics, this and that the other." Well, I will tell you this, discussing the likelihood of everyday events, like the chance of rain, or the probability of winning a game, make these huge mathematical theories and principles more intuitive for the students to grasp. And that's why when we're doing like a briefing, you always see me use a slide about snow and lightning and fog. And the reason I do that is if you can predict them, you can predict likely outcomes, right? So what I'm trying to do is instead of the problem getting bigger, I'm trying to make the outcomes more clear so you can better understand what probability means.

So we talk about pattern recognition and analysis; that means we predict likelihood. And teaching tools, when you give a practical example like how this is going to work, makes more sense to a person. I'll give you an example: I go shopping every week and I go shopping at the same time, the same store. There's tens of thousands of choices that I could make in that store, but somehow I navigate every aisle, take the items that I want, and I come back. So, even though there seems to be an infinite amount of choices, there's literally a finite number of items that I select from, and my predisposition guides me that way.

Now you talked about games. So what's the difference in games? The elements of the game are that you have decision-makers, the players. Then you have their actions, so the choices that are available to them. Then you have the information or knowledge that they go in with, or that they learn while they're playing the game based on their opponent.

That's exactly what you're doing at the grocery store.

But there's not an unlimited amount of choices; there's a finite number, even though it's a big number, Brian. And so when we talk about that, why is that important? That's just touching on games and Game Theory very, very briefly, because we're going to discuss it. But what I mean by that is it seems overwhelming, yet I navigate it every week. Law enforcement, with all the choices and the possibilities that could come up when you bail out of that car from dispatch, from the RP (Reporting Party), from the scene and the location and the weather and all those other stuff, seems unmanageable. It seems infinite, it seems like anything can happen. But at the end of the day, anything can't happen.

As a matter of fact, a great thing about Probability Theory—and I don't want to get deep into math—but it's zero or it's one. It's going to happen or it's not going to happen. Now, you know how many variations there are between that zero and that one, but guess what? They're not infinite, they're finite. Look, physics limits the number of things that can happen.

Yeah, yeah, yeah. Unless you want to get into the mathematical argument about infinity and our grasp of it, there's a whole theoretical argument, right?

No, but how many theoretical arguments can be made for that on the scene with an opponent? He's going to have a weapon. Well, a weapon can vary. He's going to be aggressive or he's going to be passive-aggressive or he's not going to be aggressive. He's going to be communicative or he's not going to be. When you start branching that, it's just like Wing Chun, or just like martial arts when you're practicing a kata or doing sparring. The person doesn't come in and drop a smoke bomb and then stab you with a sai and go, "Hey, there you go," because that would be outside of the ken, outside of the realm of the possibilities that you're going to do on the mat at that time in the dojo. Right?

So training prepares us for as many of those contingencies as possible, and it's up to us to choose which ones we want to do. So instead of choosing the one-inch punch, you and I have chosen to spend our entire life on cognition, on thinking and outthinking a cunning opponent. And that's literally what you're talking about is the difference between coming in with the mindset—and I hate mindsets—coming in with the mindset and saying, "Anything can happen."

You're setting yourself up for failure. So you started, you started with games, and you gave some of the core elements of the games and kind of defined them about the players, there's rules, information, there's outcomes. So can you, because games are a great analogy, but far more significant than we think in a sense. So can you give like, the historical significance and what we mean by games?

Shelley and I, just before Shelley left this morning, our CEO, I said, "Hey, I'm going to be talking about games today." And she reminded me that 2007, Third Marine County, OEBAY, she said, "That's the first group of Marines you talked about with games." So bring that up. And I said, "Okay, I'll bring that up." Shout out back to the day, Brian.

But if you recall that class, when we walked in, we said, "Okay, every culture on the planet has games." And then people paused for a minute and they started thinking about it, because we had Marines are one of the most culturally diverse fighting organizations on the face of the planet, right? I learned the words I needed in Gag from a Marine. Games are also, probably, music is up there too, one of the oldest forms of human social interaction. Games are a way to teach and pass on knowledge and a way to store knowledge. That game on the shelf stores that knowledge, Brian, just like a book at the library.

And then when we look at it, ancient games, the oldest games that we know about, what did they teach? They taught farming and hunting and survival skills and social intercourse, how you meet other people, how you're supposed to act. And they help develop social and emotional and physical and cognitive skills. And so when we start talking about limiting stuff, no matter how diverse games are, they all have a winner, a loser, or a draw. They all have a set of rules that the people follow, and no game is infinite. So somebody right now that's listening is going to go, "Well, there's no rules in a knife fight." Yes, there are. You just, you know what? You went to the wrong dojo, because there's a whole bunch of rules in that. Gravity still applies, physics still applies, distance, time. So when you make comments like that, what you're doing is you're showing your naivety, and that's why we're still not happy with the state. Look, we partner with a whole bunch of companies, and when we see the state of training in some places, we object to certain things that are still missing. Why? Because our locus of control is to this thing that's right in front of us. And then we forget or absent thinking, because it's harder. It's harder.

It's more obvious to you. Your locus of control, it goes back to, actually, I was thinking of that with the mindset discussion we had this morning before we got on here. But what you're talking about is what humans think that they can control. So if anyone's never heard of the term "locus of control," it's a psychological term, but some people have very little, like they think everything in the environment and it's all external forces that play out their life, or everything is set up for failure. And people have a great internal locus of control really understand that, like, "No, I can change my life, I control the outcomes of my situation." So that's what you mean by that.

But you brought up some great points about games, because games are a... throughout history, every culture has played different types of games, right? Games of whatever: chance, skill, physical skill, mental skill, whatever. And with that, because they're used as, like you said, a teaching point, they're a model. They're a model for human interaction. So it's better if we just go do the, you know, three-day long Buzkashi turn, rather than warring with one another and fighting over everything.

It's better because there's always an end state, Brian. There's always an ultimate goal in games.

You're exactly right. It's when people go, "I don't understand." Like, people get so intense or, "How can you get so into football or soccer that you're going to be like..." This is an extension of the values of your life. And yes, some people take that too far, they let that sort of primitive reaction take over where they're all in on the game and they're going to, you know, you go to like South America where they—I remember it was like the goalie for one of the teams was murdered after he let up a goal where they—

It happens often, all the time.

And you're going, "How does it get to that? This is insane, you're such a fan." It's like, "No, no, no, like this is a very primitive extension of the model of human experience." And that person was so into that. Now, they went too far with it, obviously, but it's not a, it's not a large, you know, gap. It's not a big bridge to cross there. You wouldn't be surprised if you read that in an article. You wouldn't be surprised if you heard that.

And remember, look, shout out to Milo, because Milo was the first company to understand Hobman and embrace it. And people are now coming around to thinking what's going on with it, because we, meaning Arcadia Cognira, Brian and I, and our partners, we promote cognitive development in classrooms and in AI and in virtual by role-playing and problem-solving and logical thinking. We value creativity. We force you to play, just like in a game. And in class, we play those games that involve strategy and planning, and we encourage critical thinking and decision-making. We force them to recognize patterns and sequences so they understand the cues so they can solve for X before they see X. And that's the difference. We're not seeing that in games.

Well, that's what training is. Training is a game. Exactly. I mean, that's what I just said. It's a protracted game, yes. It's a, it's a, it's a, you're modeling and simulating a likely future event, and you're allowing yourself that mental rehearsal. So it, you're playing a game because you're going to get to the championship maybe one day, or you're going to get tested on it someday, or there's going to be an opponent that's going to challenge you in said game.

And so Game Theory, I'll give you kind of a quick definition of Game Theory, Greg, and we'll talk about it, because it's important to, and real quick, not getting into this to try to be like, "Oh, look how much I know," because that's not important. It's about, but it's about naming these things, because when we get into Probability Theory, Game Theory, and especially Bayes' Theorem, these are things you actually do unconsciously every single day. So if I can get some recognition and understanding of some of the elements of it, it can help me going forward. We'll get to that later, but I just want—I don't want to come across as like, "You guys are just talking about some stuff that doesn't matter." It's like, "These are things that you do."

So, Game Theory is sort of a branch of mathematics and economics that studies strategic interactions between decision-makers or the players in the game. What it is, it's a framework for anticipating the actions of others and making informed decisions based on potential choices available to all the parties involved. So, at its core, Game Theory helps us understand situations where the outcome for each participant depends not only on their decisions but the decisions of others. So the idea, it's a little bit more chaotic in a sense, or it allows for more contributing factors than just say, like, a one-on-one, like your checkers game, there's, it's nowhere near as complex as chess. So it's just a little bit different. So Game Theory really kind of can take that into account, and the same thing: you've got the players and their strategies and information, just like we talk about in games. But I just want to get out there, allow you to discuss the "so what" behind it really.

So the "so what" behind that, and it was a profound statement, and this isn't about our intellectual acumen, it's about yours, the one that's listening to us. And so if the outcomes depend on the player and the actions of each participant, then it's exactly like police work, because you're a player in a game, and you choose your action or your strategy, and you have to take into account the choices of others. Now, they may play their role first, and then you have to respond to it. So why is that any different? So Game Theory is a great way, and games are a great way to talk about that.

So people will say, "Well, yeah, but on the mat..." Yeah, okay. So every game that has strength or coordination or endurance, it does the same thing, and it's just as good for you cognitively. And it forces you to do these things and requires manual dexterity and assistance, your fine motor skills. So if you can find it in the world, you can find it in a game. What games do? Games evolve, Brian. And what does that mean? Games change as society changes. And that means that as players get better, the paint and the amount of time you can spend in it changes, right? And there's a smaller goal, and the goalie has a smaller area that he has to defend. So they make it more complex by adding these things to it, which is great. Which is evolution.

Yeah, NFL right now, they just changed, big changes this season for some of the, like, kickoffs and some, basically, they changed some of the rules on for safety issues because now people are bigger, stronger, and faster than they were a hundred years ago when those rules were came up with, and they're going, "You're going to, this is extremely dangerous now." So it had to evolve.

No, I just—the Olympics have changed. Things are added to the Olympics, things are taken away from the Olympics. And guess what? Just like in class, we're forcing them to challenge their memory, and then we have them perform when their concentration is challenged. And we force that in the class and in the training and in the practical application scenarios. And then we add different temporal elements to change the level of stress, just like in real life. And you go, "Oh, well, every game does it." Well, Jeopardy does it on TV, but you know what? The outcomes aren't that somebody dies. I've yet to see him take a two-pound sledge and the person that comes in third beat him to death on stage. The idea is that the consequences and outcomes are virtually interchangeable in life within a game. There's losers. The difference is that the loser doesn't die. Okay, we get that. But that's how you have to think.

And the problem with a mindset—don't get me started, but a mindset is powerful and it creates—I know, I always start it—mindsets create realities and shape your thoughts and behaviors in very important ways. But the problem is, they also create blind spots and they fuel biased thinking. And when left unchecked, they're harder to change because now it becomes a part of your behavior and your mindset starts to remain even though you're consciously aware of other factors. And that's when it becomes an inhibiting factor. So if you start thinking of just science: here's the number of reactions that might happen, here's the finite number of things that could happen. I'm going to play this in a game because I understand Game Theory and I understand that there's finite constraints. There may be tens of thousands of possibilities, but guess what? That's still finite, and it's a large set. But I can rehearse one of two ways: I can rehearse 10,000 different moves in karate, or I can understand anatomy. I can go 2,500 punches with my right hand, or I can understand physiology. And that's the difference. The difference is that I can take a look at our training, and training is training us for every eventuality by improving our cognitive acumen and improving our ability to assume what might happen next and create an ML (Most Likely) and an MDCO (Most Dangerous Course of Action).

And that's the idea of it is what, obviously, we want to get people better at predicting behavior. So you need some sort of tools to use this, and these are the, these, what we're talking about today are the foundational elements of the tools that we use, right? And we go, "Okay, knowing this, knowing, meaning, knowing these things about science and math, this is what's sort of known. So then how do we use them?" Now, you can use different tools, right? So that's the idea, and that's where you have to, you have to come in with something. I can't, I can't sit in there with a calculator in a situation, Greg, and figure out what, what, what's training is for.

So training helps me, exactly. But training helps me understand that a calculator and a slide rule are amazing tools that I can have at my disposal, but they're not going to make the decision for me. It's still up to me to make the decision and to choose what the ultimate eventualities are of a situation. And that, you know, that's why when we do training, Brian, it doesn't matter if it's raining or if it's snowing or if there's a tornado situation that's going on. I'm moving my location.

Yeah, exactly.

And it doesn't really matter about all those other factors because those factors are going to occur in real life too. And that's the amazing thing is that a simulator can do so much and we're not using it to its capability many times, right? Because practical examples, simulations, experiments, they're very effective because that allows a student to see that there's randomness that's surrounding them—chaos, right? But that certain patterns emerge no matter how chaotic a situation is, and that's the magic.

And I didn't, I, I, I didn't really want to get into the randomness yet on this episode because there there is another one. But it's an important thing to bring up as we're talking about all this and probability and what we'll get into next with Bayes' Theorem. But the idea, there, there is, there's a lot of randomness in the world, and because humans are primed for pattern recognition and we want to put things together and understand it and we don't want surprises, we don't want uncertainty. We'll often attribute value to things that really are insignificant and completely random because there is a ton of randomness in the world. And so we can, we can sort of get that can cloud our judgment, which is why I have to be able to account for that.

But, you know, the the big, again, so the "so what" on that Game Theory, what we're talking about is really just balancing those knowns and unknowns, right? And we'll define what that means. But this is predicting behavior. Understanding interactions as a game allows me to model that in a number of different ways. Actually have these and, because everyone does the "what if" scenarios, or we'll do, people call them TDGs sometimes, Tactical Decision Games, or "I'm going to give you a set of circumstances and a set of constraints and you tell me and we're just going to walk through on a whiteboard, 'Okay, then I'll allocate these resources here, then we can do this.' 'Okay, well what if then this occurs?'" Okay, so these are all all great, great things. Those are actually far more powerful than they're given credit for, because a lot of times they're just not set up correctly.

Because I have to understand what's the likelihood of these different situations occurring. And, because I remember even a few years back, even running, we're still running some, some tactical training, and it was a big ending exercise, and there, this, this team, the couple of teams that were going through it were doing an absolutely phenomenal job, like beyond what we thought, like surprised us during this final exercise. And we were like, "Holy crap, they're really, they are killing it." And they had great comms, they had everything set up, they were task organized, and they were doing so well. And they had this great plan, right? So then what we have to do, we're just like, "Alright, well, let's just, let's just see how far this goes." We obviously start coming up with ridiculous stuff and giving all these problems to the point where they're like yelling and getting frustrated. And then at the end, when we went to do the debrief, they thought that they had failed. And I had to start off with, like, "Alright, guys, that was the best team we've ever seen come through." Like, confused, like, "What do you mean?" Like, "Guys, like, we were making stuff up at the end, like, we just, we had time enough to do it, so we just said, like, 'Jesus, how far can these guys go?'" Because they, they did it so well. But the idea is like that, that we, we, we, we get that wrong sometimes when we're coming up with a "what if" game. So this is trying to help that, that sort of "what if" game to keep it within the realms of the possible. And then you're going to get a lot more value out of it.

And so what you did there too—no, no, just to add to that—what you did there, everything is evidence-based, just like, absolutely overused term, right? But the idea is we do, and I can see it and feel it and taste it. But the idea is that what you did is you were conducting an experiment, yes. So what you did is the experiment yielded results, and you can lift and shift, fire based on those results. So it's not how much faster that you can get through the scenario, or how quickly you can win. It's what you learned from that win or loss. What you learned from that game, what you learned from playing against this opponent rather than another opponent. And if you can pay that knowledge forward, that's the key.

What does Bayes' Theorem tell us? And I know you're going to get into Bayes', but let's do a real quick a street definition of Bayes' is simply this: you have to update your probabilities based on new and incoming information. And if you're not constantly doing that, because we have certain assumptions, Brian, then all of a sudden we're in a situation. Dispatch told us this. We get out of the car. Yeah, there is in fact an argument that's going on, and you know what? The guy that we're talking to that we think is the RP, he's the guy that's killing everybody in the scenario, or whatever real life situation we're facing. So we have to update that, and we have to update that quickly, because as that prediction or as that information changes, your prediction of likely outcomes change. And if you can't do that, and people go, "Well, isn't that like Jon Zullo?" That's like every critical thinker that's ever, ever lived. Even when he came out of the cave with a bō staff, he had the same realization: if I don't update, then what's going to happen is I'm going to continue to stay on the gas coming into the turn, I'm going to overrun my headlights, and guess what? I'm going to crash.

So Bayes' and Bayesian thinking has always been at the root of Greg's experience. I hate to talk about myself in third person, but look, I'll tell you how profound it is. You and I met when you were coming in and out of combat zones, and you were part of a training evolution that I was doing. And you go, "This guy's full of crap," until you started trying some of these and then you found out, "Wait a minute. Makes me faster, smarter, harder to kill." So it's not about me. I just happen to be the guy that introduced you to the right book in the library. That's what this is about, Brian. What I think our primary job is, is to create a legacy of opening eyes on people that didn't see it that way before, handing out those flashlights so they can search the box instead of going outside of...

In all fairness, my reaction was actually, "Oh my God, this guy's hilarious and he's completely full of crap." But a two-pronged standard that you had, like it was like, "Okay, like, he's a, he's a bullshitter. I got plenty of buddies like this. I've met people like this before." And then I was like, "Oh, wait a minute. This is, hang on, there's something going on here."

And to further define, I guess, or talk of the, because we brought up Bayes' Theorem. And this is another great example of something you unconsciously use every single day of your life, right? You're, you're, you're always doing that, in most areas. And actually, if you're going to survive, you are.

Yeah, yeah.

Well, yeah. I came across a good one where, because basically we're talking about, "Hey, you have this prior belief, then you get some new evidence, then you have an updated belief." And there's a million different studies like this, and I've used ones before in class where they'll give someone some information, or they'll say, "Find out what everyone's beliefs are." And usually in something that's a very, like, politics are a great one because a lot of people are very set in their ways and how they look at certain things. They'll take a political issue and then find out which side of it you're on, and then they'll force you, like, "Hey, do like a 500-word essay on the opposing viewpoint." And then all of a sudden, when they have to do all that work, they kind of go, their initial viewpoint was like, "Oh, man, like, I guess I wasn't taking into account all of these other things."

And even to the point where the one from today, I'll put it in there, it was an interesting one because they use a great example about our prior beliefs and the different kind of how sometimes we are overconfident when we're wrong, or sometimes not confident in the things that we do know. And that's the two sides of the coin that we're talking about right here, meaning exactly. We're going, "This is a lot more complex, there's a lot behind this." And someone's listening going, "Man, you're talking about all these mathematical stuff. I don't understand this or what are you getting at?" It's like, "No, no, no, you actually do know this. You actually use this. So you can get better at how you use it in new novel circumstances."

And so this one was, they use a great example: "You walk up to someone and go, 'Hey, do you know, do you know how a toilet works?' And they're like, 'Yeah.' And then they go, 'Well, can you explain it to me?' And they're like, 'Well, I just kind of push the handle and it flushes.' Actually, I don't know how a toilet works." Right? So we have this—

Yeah, I think I understand the cell phone is a great one now. Your iPhone, how many people have I met can actually explain how an iPhone or cell phone works? I mean, how it actually works. Almost no one can. And we're past the point of even getting to the base because it's evolved so quickly past what original cell technology was versus a landline. Like, it's highly complex, but we know how to use it, right? And that's all that matters to us. And that's actually what HBPR RNA is, really. It's simply, I don't care if you can tell me all of this stuff. I want you to be able to use it. But understanding some of the big picture concepts are really important.

Yeah, I absolutely agree.

So let's go back to Bayes' and Bayesian thinking and let's talk briefly about how it impacts your life every day. So I'll give you a fail in Bayesian thinking: every single year that I've been alive, I've done an article or a reporter talked about confined space entry death where methane gas has built up and the farmer goes down to clear out the vent, he dies, then his son, then his wife, and the whole family is dead. Every year that happens. Every year during graduation, I do a story about a car full of kids that go out and they're not harming anybody, but the car hits a tree and rolls over and does it.

So Bayesian thinking is a form of high-speed hypothesis testing that says when certain pre-event indications coalesce, I have to update what I thought was going on. "The kids are just going to a party, they're just going to go out for a drive. Maybe it's methane gas, maybe I don't see it, it's colorless and odorless, and therefore that changes the likely interaction with the environment," right? And so the adjustment of expectations is huge. So what do I mean by that? Everybody that's a cop that's listening to this, everybody that's HR or if you're a teacher or administrator at a school, you saw a behavior change and it was profound and it was fundamental. And all of a sudden you said, "Well, you know, maybe it's just because it's Valentine's Day or whatever else," and you didn't account for it. And then you saw another thing with the same person or situation or environment or financial—it doesn't matter what the baseline is—and you didn't account for it. And then all of a sudden, here was this failure. Something went catastrophically wrong, that dysfunction showed itself. And then we looked back and we said, "You know what? Every one of those things was present. I just didn't account for them."

So that predictive analysis to update your probabilities based on the new and incoming evidence is Bayes'. That's the root of Bayesian thinking. But it's also Probability Theory, and the beauty is it's also Game Theory. So each one of these is intrinsically connected to how you process information. And if you fail to see how the breadcrumbs coalesce, if you failed to see that gosh darn cottage in the wood with a lady with a cauldron going, "Come in, have some candy," then you're going to die. And I know we make that oversimplified, Brian, but we do it for a reason so you go, "Hey, I know what you're talking about."

And with, there, there's a thing to add in here with all of this is that there, there's, there's a few things. One, statistics, Probability Theory, even Bayes' Theorem, like this is the most, I, I guess the most newly defined parts of math, meaning math's been around forever, it's just existed and then humans kind of understand it as time progresses, right? But when you get, the reason why it's important to understand some of this stuff at least at a theoretical level, and big picture level, is it's one thing, I always say in class, like, humans, we don't intuitively understand probability. We, we, we really don't. Like we intuitively understand physics. If you never got taught anything about physics, you still understand what gravity is, you still understand what force equals mass times acceleration means, right? You implicitly understand that because it governs the way you, you, you go through the world, right? Locomotion, balance, everything. Yes. But with this stuff, it's, it's, it's, it's not, this is not an intuitive way of looking at things because the intuitive way of looking at things is simple, it's survival-based, and it, it's what I know in front of me.

And sort of the reason why Bayes' Theorem is so powerful too, because it's a little bit different in a sense of Probability Theory that Probability Theory is very, there's a lot of rigor, it's very mathematical base, and it's a lot of axioms, right? Whereas Bayes' Theorem kind of allows for more subjective observations to be put in, right? Meaning you have different experiences than I do. So you can use this kind of model, and you might get a more robust or different answer than me, and we're, we can both be right in a sense of what we think it is, but maybe yours is more right than mine. You get what I'm saying?

But I can also use that same tool, those three tools, to transfer my knowledge and experience to you, and I do that by playing a game. So what is a practical application scenario? It's a game. There are players on both sides, there's an outcome that we think is likely, and people have to use strategies to negotiate the rules that we put into place. Don't look now. Now, watch this, this is a day in the life, those type of elements that we put into it. And let's go back to how long this has been important. Miyamoto Musashi said, "You win or lose before ever drawing your sword." Musashi beat people with a bō staff. Musashi beat the best samurai with the sheath from his gosh darn katana, right? If you've read Sun Tzu, then that's Bayes' Theorem, exactly everything he says.

So let's talk about that. So why is there such an impetus in now that the curiosity and the training and everybody that we listen to that's putting out great stuff, there's nothing that somebody's putting out that's wrong, but it's focused on motor control. Why? Because we understand motor control, we understand sympathetic and parasympathetic. Exactly. But the problem with when we're talking about cognition is we don't completely fully understand the brain. We don't understand all the cortical, we don't understand even what sections of the brain are responsible with. So it's mysterious, but it's not because if you look at science, if you look at nature, how does nature warn us about winter? It gives us fall. How does winter, or nature, warn us about the time to plant? It brings us spring. Do you see what I'm trying to say? So it forces us to learn by outside experimentation. And those, those, the more times that we go through those, Brian, the hypothesis testing, the better we learn. And so Bayesian thinking is hypothesis testing.

So I'm in this pursuit, but this guy's taken way too many chances. So what are my possibilities? He's drunk. Okay, but he's holding it together pretty good. Maybe it's drugs on board. Okay, but maybe he's got a hostage, and maybe it's this. Now what happens is those few decisions become crystal clear on what might be going on.

And you're talking about it right there in the moment. But I also have, in that specific example, historical precedent. What happens the longer that pursuit goes on? Does it better, do the, do the potentially good outcomes increase or decrease over time? Because what, what, what historical precedent says is that it gets worse the longer that goes on. The more likely this is not going to go well, right? And so that informs the decision-making. And that's a, that's a perfect example of how right there in the moment I'm constantly updating my hypothesis.

So what we're talking about is trying to be more aware or you say mindful of this process so that I can actually get better at it. And that's sort of the way these things coalesce of games, Game Theory, and Bayesian thinking. And I, and, you know, that I call it Bayesian only based on the fact that's why I was trained and taught. And either, I think, Amanda Miller, right? I mean, I, I respond to the way that I'm trained.

And Brian, I think your point is so important. I want to make sure that you clearly make it.

Yeah, yeah, no. And when, when these things, the reason why we're talking about all these together, I, I think really, is what you're constantly trying to do, especially in complex situations—not just complex situations, now adding that there's potential danger involved in that complexity—because like, a market system or figuring out what you want to do in life can be a complex situation, meaning there's a number of inter and intradependent factors that affect the outcome, and you don't control them all, right? That's the whole thing, "Hey, the bad, the bad guy gets a vote too." I mean, they, they get, they get a say in what's going on in the situation too. You don't, you, you, you don't get to, how much influence you have over that is finite.

But what we're, what we're talking about is balancing the knowns and unknowns in every interaction, in every human interaction, in every way, every situation you go into. So we've been talking about from real situations, from training situations, and just cognitively how we think as humans. And, you know, you kept saying hypothesis testing, and that's the best way to do it. And that's what games do as well, right? So at that the football game, with college, NFL, whatever, when they're going up against their opponent, they've studied their opponent, they've studied what they do, what their strengths and weaknesses are. And then I have to compare that against my team's strengths and weaknesses and how I'm going to play against that. And, and, and really play to my strengths and try to, try to eliminate them from exploiting my weaknesses.

And then there's a little bit of randomness in there. If that, that temperature all of a sudden drops 30 degrees that day, and maybe my team is more used to playing in warmer climate. The, the, the rain on the field. There's an injury right before the game where someone who is a top player gets pulled out and they have to sit that game out. That's where that stuff really starts to affect it. But it, it comes, still comes down to, you know, if at, at a basic level, is if I get good at identifying knowns and unknowns. And so, so the known, you know, that's just understanding and recognizing different patterns, established behaviors, environmental cues, everything that we talk about. And I want to increase as much as what I know about a situation to reduce the uncertainty. So I don't want to throw in there anything. And, and then I can compare that to what, what don't I know, right? And I have the comparative baseline.

The question that you have to ask yourself, and this is Bayes' as well, the update is, "What am I missing here?" Okay, this guy's driving like I've never seen anybody drive before. This person's fighting harder than I've ever seen anybody. This person, no matter what I'm telling them, they want to jump off that bridge, or hide that evidence, or do whatever else. What am I missing? There's something here, there's an environmental, there's a piece of information, there's a fact that I'm overlooking. And so how can I reassess that in real time while this is going on? And you know, the fact of the matter is, because of your occupation, sometimes you can't. Like there's this video that's circulating now with this huge female having a mental breakdown behind the door. The guy's trying to negotiate, and all of a sudden she comes out and she's, you know, six-foot-five and coming at him and slashing and doing a knife.

Okay, look, that's the Jack in the Box. We try to reduce the Jack in the Box every time that we do the training, but guess what? Because there's a spectrum of potential possibilities, that's always a possibility. But if you only train for that, how is that affecting your de-escalation technique, or the use of cover, or all the other things that are infinitely important as well? So if you just look at that and go, "Yeah, but that can happen," then we're right back to the theorem that you posited at the very beginning of this, "Well, anything could happen." No, that's one of the things that could happen. So yes, I need to be ready for that, but you know what? Every single day there's encounters exactly like that that don't end in a fatal shooting. What we've done is we've upended the apple cart and we've only taken a look at those things because they're fun and they get clicks and people want to know them, the fatal stuff. You see what I'm trying to say? And the more that you see that, that limits your options too, because that creates a, what, Brian? A mindset, you know?

Yeah, and it, and it's, we don't implicitly recognize these things when they're laid out in front of us. It's exactly, "Dude, this is not a big deal. Look, yeah, you rolled through a stop sign, but like, that's not, it's not the end of the world. It's just there's a school here, and I want to maybe talk to you about it." But you're going this way. It's the same thing with the insurgent, right? When I'm like, "Hey, did you get that stuff done for your math assignment or whatever?" And then all of a sudden she goes off the top, "I told you I did!" I'm like, "Well, this is, this, this is not a typical response. There's clearly something else going on, and there's some other thing that she has on her mind versus the question that I asked. And that that dissonance, that disconnect there means, okay, there's something else here that I need to investigate or get some time and distance and come back to that and find out what else is going on."

But, but, but that's the thing. It's not just the, "Well, that's weird," or, "Hey, don't talk to me like that," or, "What are you doing?" It's, I, there has to be that instant recognition of, "I'm missing a piece of information that's critical right now." So I may need to get some time and distance right now and figure out what that is before I make my next decision. And that's the point where we don't.

But we don't do that in those, we don't keep the non-standard observations. In general, as humans, those are most likely the marginal information, the space between the words and the paragraph that you're reading are important as the words that you're reading. And we don't do that. We don't do that holistic approach. Again, back to Hobman and why that Hobman sphere is so important, because we got to make that problem a 360. We got to take a look at that. And when we talk about predictive analysis, we're talking about being able to recognize patterns that would tend to show a reasonable person that a thing's going to turn into a crap sandwich, or things are going well. And Bayes' tells us to constantly update that. And guess what? If you're in high-risk encounter after high-risk encounter, and it's unknowns. Traffic stops are an unknown, domestics are an unknown, suicidal subject an unknown, medical, mental. All of these are unknowns. You've got to be able to make a fast, intelligent, informed decision that's on an unpredictable set of circumstances, and it has to be legal, moral, and ethical. So where are you going to do that on the street? Are you going to do that on the street? No, you do that in a game. And the more that you do that in a game, and the more that you start at the academy when you're a kid, and it matures during your FTO (Field Training Officer), and when you're old now, you're the gray beard street vet, and you reinforce those, Brian, now that becomes a way of life. And habits are hard to break. Habit over mindset any day. I mean that.

Yeah, and so let's, let's, so for for the purposes of the discussion then, let's, let's talk about knowns, because my argument is, especially like you're giving these law enforcement examples, and I tell this every course we go to or every time I've trained or worked with, whatever. I mean, that was even the first time when I, I mean, like, because obviously I had a military, tactical, government contracting background and then started working with law enforcement as well, right? And I'm like, my thing was, "Holy crap, these folks know more than they realize." Like, your knowns are so, you, you gather so much tacit knowledge and experience, and that's anyone with any like subject matter expertise in anything, right? I don't care if you're a pilot, I don't care if you've been doing HR your whole life, I don't care.

A layperson, you're exactly right.

You know so much more. And, and, and those get, when, when you get into the science of like, intuitive decision-making and subject matter expertise, right? That that's all there, that's, that's those, um, those intuitive decisions that people make are based on their selected priors, based on those really good cognition biases that they've developed through experience.

But I, rather than, than, because this goes into the argument of people like, "Well, what do I need to look for and what do I need to do and what..." It's like, "No, no, no, focus, focus on on vanilla." You even said it, that the margins in the paragraph, the piece of paper it's written on, the title of the book, the name of this chapter, the dust on the spine of the book, and where it's at on the shelf and which room. You're exactly right, those all are interesting, and you can, you can pull those out, right? I can contextualize my knowns of my past experiences to carry me forward, right? So rather than learning the lesson of, "Okay, if I ever run into that situation again, I'm not going to do that, or I'm going to go to this right away," or "Next time I go to a call and I see that same, you know, woman in a mental health episode doing something, I know I'm going to have to kill her." It's like, "No, no, no, no, no, hang on, like, that's, wait a minute, that's, that's not what, what we mean." And, and, you know, I, I'm, I'm oversimplifying it in a sense. People go, "Well, yeah, that's not really what happens." And I'm saying, "Yes, it is what happens." That's actually what happens is we don't learn those lessons because I don't unpack my knowns.

And so, so the better I get at articulating all of my past experiences and what occurred and the decisions that went into it, so meaning, I, I'm, I'm getting this from taking everything we're talking about and rather than trying to say, "Alright, I'm going to go out today and I'm going to use Bayes' Theorem, I'm going to use probability or Game Theory, so I should look at everything as a game." It's like, "Well, hang on, before we get to that, go back, go back to your own personal experiences and your life." Now I can start to pull apart who the players were, what were the conditions that were set, what conditions did I set unintentionally? What, what did I unintentionally put myself in a position of disadvantage? And go, "Holy crap, what were the mistakes I made? What were those indicators that I should have recognized earlier and been able to draw a reasonable conclusion because I went through it and I went, 'Damn it, I, I knew that was going to happen!'" So that, that, that allows me that faster and better intervention strategy because I'm seeing things sooner, the recognition happens faster.

So spot on, and that's such an important detail. Again, folks, mark this part and go back and listen to it again. What Brian just told you is that when we talk about knowns and unknowns, we have a known where there's your definition, a high likelihood that it's going to occur based on artifacts and evidence that I'm witnessing. And you have an unknown where there's a low likelihood. Okay, which means that if it's a low likelihood and it comes to surface, it's going to come out like a Jack in the Box, and you're going to have to respond to it rather than being prepared for it. So the game is to give yourself enough time and distance to balance high and low likelihood. And then the likelihood goes into most likely course of action or most deadly course of action, dangerous course of action. If you can do that for your entire life, then you're going to be fine.

You can't lose weight with Ozempic and pills and not manage your diet and your workout. That's unsustainable. So what you have to do is you have to say, "I can't just get through my police career learning how to use my gun and my baton and my less lethal." A lot of what you do, and this is Brian, you, you'll remember this argument, I think it was 2006 in the fall when we were all at the back of the tomato cannery that became the I, "Well, yeah, but you know what?" It was one of you, you know what I'm trying to say, it was one of you. And we were having the argument, and General Amos was there, and the argument came up about, "Greg, I've watched three back-to-back scenarios and they've all been non-kin, non-kinetic." And it's like, "Yes, sir." And he's like, "Well, I didn't pay all this effing money to watch a non-kin scenario." And it's like, "Yeah, but sir, it's a game. That's how your brain learns. Your brain learns just as much watching the normal daily baseline activity that's going on because then an anomaly becomes immediately apparent." And you know what? There is a gap for so long and they go, "Holy crap!" And then look at all the material started coming out about non-kin, non-kin village and all that other stuff.

Now, I'd love to say that I'm the start of everything, you know, the sun coming up and this and that, because it's my massive ego. But I'll tell you what, those arguments were going around out there and people didn't listen. And now take a look at at the time that you have a simulator. Somebody's going to go, "Well, if I'm going to spend time in a simulator, it better be a gosh darn shootout! It better be the, and I better feel recoil, and I better get shot once in a while so I can put on thing." Trust me, those things are going to happen in your own life, you'll do fine. But if you handle the mental portion, and that means overcoming uncertainty by anticipating likely outcomes, that's the game. The whole game is which move is this person going to make next? And I anticipate three or four that I know. Then there's a couple that I don't know yet. But if I see the pattern form, then I can think that that pattern is suggestive of a likely outcome. Oh my God, I mean, that right there, that should have been a book. Not because it came from me. All I'm doing is I'm like a gosh darn sprinkle, I can only spout out what I learned. And so I just learned this stuff, you get what I'm saying? And it's what I know better than anything else. And that's why we have to reassess what we do in training, because training on the mat, of course, that's important. Training on the range, of course, that's important. But if you don't have an equal or greater amount of cognitive decision-making, of sense-making, and of in extremis critical decision-making, Brian, then when the time comes and it's you and the spotlight shines on you, you might not sing, you might not dance, you might freeze up. And that could be a detrimental outcome. That could be a shitty day for everybody involved.

I, because, because in your line of work, the failure to act may be just as bad as you acting in the wrong manner, right? And, um, you know, you're, you're, you're bringing up some, some very relevant examples and showing it from the idea of how, how do I sort of account for this, plan for this, get better at it, um, because that's, that's the whole point with all this. And to your point of, you know, "I only spout out what I learned." That's, that's, you know, I have the same thing. Look, I could always tell people, "Look, I've never had an original thought or idea my entire life." But you, you know, I'm one of the few that realizes that, that I haven't had it, you know, where most people, you know, that's, that's a little bit different, they're convinced.

Yeah, well.

And that's the, the another point of talking about these subjects is these are problems and tales as old as time. These are nothing new. But we as humans, what do we do? "Well, there's got to be some technology," you know, and, and, you know, yes, when, when whoever invented the wheel, that was revolutionary. Okay, the, the printing press, industrialization, the, the internal combustion engine. You can rattle off some things that, that, that are revolutionary, but most aren't. Like, most things don't do that. You don't need some revolutionary solution or technological application. You need to go back to focusing on the knowns. What do we know that's consistently worked in every situation across time? Timeless. Come on. So, you know, that that's, that, you know, it's part of the "so what" is, is why we like discussing this stuff because it's like, take, take a, get some time and distance. Like we're coming up with solutions to problems that don't exist, or we're coming up with solutions to problems that, that the, to the wrong solution because we haven't clearly defined what the problem is.

Or are so sporadic that they're likely never to occur again. Exactly.

It's, "That's happened once, and let's put all our money there." And that goes back to, we gotta, we gotta have on, whenever, whenever his book comes out, I know you've seen on LinkedIn, my cousin Pat, he, he deals with, um, oh, what a high thinker by the way. And that's, that's his, that's his, yeah, he, he does kind of like a lot of this type of gaming and strategic level gaming and planning. And he has background in, you know, he's a Harvard guy, but then also, you know, some intelligence community and government work and stuff like that. He is a consulting firm where he does this, but that was a funny point was, you know, some the stories from immediately following 9/11, you know, where he's in with this group of people, and, you know, the, the generals in there going, "Okay," I mean, I'm talking like immediately following 9/11, like, like there's still smoke and they're going like, "Okay, we got to start thinking outside the box. We got to look at everything. What other buildings do we think they're going to hit?" And it's like, "Oh my God!" It's like, "No, no, no, no, no, sir. That, that ship is, that ship is sailed. It's the next problem." But, um, so it just reminded me of that example.

But yeah, um, we, we, we went over a lot, so I kind of want to recap some of this stuff. And what I'll do too, for, for our Patreon subscribers, I'll kind of give you some outlines and some stuff from this that, that we, we discussed and with some of the information so, so you can always go on there. But it goes back to the beginning thing. I want to debunk this myth of "anything can happen." I, I think that really has a bigger effect than we realize when people start doing that. And it, there's always, sure, a meteor strike, something random, a Black Swan event, but you know, you can go back after those things and look and go, "Well, here were all of the pre-event indicators," but because it was so rare or unique, that's a major contributing factor to why no one saw it coming. But most things, 99% of the things you see in life are never going to be like that. So human behavior, while complex, it follows patterns, it can be understood, and it can be anticipated, especially, especially, Greg, and this is why, you know, I bring this in in comparison to like different economic models or when you get into this with, with, with, with market systems and Game Theory, like, when the clear, the more clearly you define the context of the situation, the less potential outcomes there are.

And the less ambiguous it is, exactly.

If you're looking at, at a market in a long, are good at a lot of it is luck and some unique intuitive or inside knowledge. And I don't even mean like at an illegal like insider trading; I mean they, they have a unique, you know, insight that they don't even realize that they have. And that's why they're getting good at making those decisions. But most things, especially the clearer you define the context, the less complex it becomes. And a great way to do this is what we talked about with games, and games reflect life. You know, there's fundamental aspects of games that help us comprehend and understand strategic interactions in real life. It's all about looking at it. Games, Game Theory, and decision-making. Applying the Game Theory allows us to anticipate other actions, plan strategies accordingly. The Bayesian thinking, that is what allows us to enhance our predictions.

And what we talk about all the time in The Human Behavior Podcast, what we do for a living, what we train, is that HBPR RNA is the goal is reducing the uncertainty, reduce the ambiguity, reduce the complexity so I can, I can have a, make a more informed decision sooner and and be more confident in my decisions, right? Because I have a leg to stand on. I can, I can show my work, I can, I can, I can show the teacher my work to get to the, to, to get to the answer. And, and maybe even if the answer was maybe not the best answer, it's better than rolling the dice, right? I mean, that's the other thing is, is what that's what informed means. You know, that you're going to be wrong sometime, but guess what? You're much more informed than the person that's constantly just guessing. You're going to be right more than you're wrong. And when you are wrong, you're going to know, you're going to be able to know where it went wrong. And we didn't even really get into, obviously, like, what, what, what does a correct decision or success look like? Because that's defined a number of things because you're, you're not looking for the right answer, you're looking for like the best answer given the available information that I have, and at least know what "right" looks like so I know that I was on the right path, you know? So you're exactly right.

I would, I would encourage everyone to kind of think about this stuff and and use it in your everyday life in the most simple interactions with, you know, family, friends, whatever. And and try to become more aware or mindful, as Greg would say, of the different factors and characters that are at play and the different roles and the different rules that govern your everyday social interactions. There's, there's things and ways that are more appropriate to to, you know, to say or do at a loud bar at midnight on a Friday versus, you know, the church on Sunday morning. So, so think about those. So, Greg, I'll throw to you for sort of—

Real quick, real quick, one, Brian. So we talked about Bayesian Theory and Probability Theory and Game Theory. And so the hardest opponents to anything are always our law enforcement subject matter experts, because you learn one way, and then the walls start going and now all of a sudden you got a silo. So I tell you this: all of a sudden you got that younger cop and you just called off a pursuit. And they show up at roll call and they're like, "Hey, this is crap! I drive every day. I know what's going on. I'm a street vet. I make a lot of felonies." And you look at them, you go, "Did you check the oil on that car today? Did you check the tire pressure on that car? Is that the car that you've driven through all of the training? When was the last time you were training?" The idea is that we make decisions and we think that it's in the best interest. If we don't go back and understand Bayes' and Probability and Game Theory, then what's happening is we're riding for a fall because we're allowing all that hubris to get up ahead of us and we're not crystal clear in our thinking. So making a better decision, and sometimes "no" is the right decision, is much better than getting into that "roll of the dice" mentality and saying, "Look, I can beat the house. I know I can beat the house this time," because that's dangerous thinking. So all HBPR RNA is, is reducing uncertainty in the most austere and complex environments in a speed that you should feel comfortable with. Meaning, with training, you'll get faster.

Yeah, and that's the, the, the house, the, The House Always Wins, right? Physics, math, Mother Nature is always going to, going to, going to win out in the, in the. Yeah, that, that, that's what's going to win, right? It's people who go, "Oh, we got to, we got to save the environment." No, we, we, the environment's going to be fine whether we're here or not.

It has a way of fixing things. No, it's, it's good. Like, we're, we're the ones we need to fix. So do governments, Brian. So do governments, so do diseases, so does everything else. So we need to look at that. And all of those follow scientific theories. So we're more interested in building your mental acumen than your, you know, speed of returning your weapon to the holster or doing a tactical reload. So if you're looking for that stuff, we're probably not your game.

Yeah, yeah, that's true. Um, all right. Well, that, that was a lot. Uh, so we, we appreciate everyone listening, uh, especially if you made it all the way to the end. Folks, you know, please check out our, our Patreon page and always reach out to us, you know, thehumanbehaviorpodcast@gmail.com. More than happy to answer questions and get into stuff on here. We do that all the time for our Patreon subscribers who we do love and appreciate, and we, we get some good ones. We get them to come on sometime and bring their, their expertise as well. Shout out to, to Scott Kuester from Kuester Solutions. He's great. I, and I love the name of his company because there's a lot of meaning behind it where I believe that too. Yeah. So we thank, you know, everyone for, for tuning in. We appreciate it. Share, share the episode with a friend if you enjoyed it, and, um, don't forget that training changes behavior.