"Anything Can Happen" is a Myth

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This week, we're debunking the myth that "anything can happen." 
In today's episode, we'll explore how adopting this mindset can increase uncertainty and cognitive load—especially in high-risk environments—and how it might set us up for failure. Instead of believing that "anything can happen," we'll focus on reducing uncertainty by deepening our understanding of human behavior and utilizing practical tools like game theory, probability, and Bayes’ theorem.

Join us as we dive into why strategic thinking, modeled through games, is essential for real-life decision-making. We'll discuss how you can leverage both the knowns and unknowns in human interactions to predict behavior more effectively, minimize ambiguity, and ultimately make better, more informed decisions. Whether you're involved in law enforcement training, making everyday choices, or viewing human behavior as a strategic game, this episode is packed with insights to help you think sharper and be better prepared for whatever comes your way.

Thank you so much for tuning in! We hope you enjoy the episode. Don’t forget to check out our Patreon channel for additional content and subscriber-only episodes. If you enjoy the podcast, please consider leaving us a review and, more importantly, sharing it with a friend.

Thank you for your time, and remember: Training Changes Behavior!

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Episode Transcript

Speaker 1 (00:00):
Hello everyone and welcome back to the Human
Behavior Podcast.
This week, we are debunking themyth that anything can happen.
In today's episode, we'llexplore how adopting this
mindset can increase uncertaintyand cognitive load, especially
in high-risk environments, andhow it might set us up for
failure.
Instead of believing thatanything can happen, we'll focus
on reducing uncertainty bydeepening our understanding of

(00:20):
human behavior and utilizingpractical tools like game theory
, probability and Bayes' theorem.
Join us as we dive into whystrategic thinking modeled
through games is essential forreal-life decision-making.
We'll discuss how you canleverage both the knowns and
unknowns in human interactionsto predict behavior more
effectively, minimize ambiguityand, ultimately, make better,
more informed decisions.

(00:40):
Whether you're involved in lawenforcement, training, making
everyday choices or viewinghuman behavior as a strategic
game, this episode is packedwith insights to help you think
sharper and be better preparedfor whatever comes your way.
Thank you so much for tuning in.
We hope you enjoyed the episode.
Don't forget to check out ourPatreon channel for additional
content and subscriber-onlyepisodes.
If you enjoyed the podcast,please consider leaving this

(01:01):
review and, more importantly,sharing it with.
Thank you for your time.
And remember training changesbehavior.
All right, greg, we'll getrestarted here now that I think
I have my audio issues.
Um, so, hello everyone.
We're having some problems, uh,getting started in the
recording this morning, but wehave a great episode for you,
and so, big picture topic thatwe're talking about is this

(01:25):
anything can happen and how it'sa myth as far as I'm concerned,
and what I mean by that is youknow, we talk about different
interactions from humans andpeople make observations that,
well, they could do anything oranything can occur.
You know, we don't know what'sgoing to happen and a lot of
times it just simply isn't true.
Now, if you're trying topredict you know some black swan

(01:47):
event, some major thing, youknow that's really hard to do,
right, and you see that in, likeyou know economics or you know
finance, different areas likethat where they you know you're
trying to someone capitalizes onsomething that that's a rare
occurrence and they were likethe only person that saw it
coming and it's so rare.
I mean, we're not talking aboutthis thing.

(02:08):
We're talking about, you know,basic human interactions, what
you can predict, what, what'slikely, what's unlikely, what's
known versus unknown kind ofthing.
And the idea is, when you havethis approach, my biggest
problem with people saying, well, anything can happen, um it, it
.
The problem with that is thatit increases the level of
uncertainty, especially inanything like a high risk

(02:28):
situation or some extremesituation, and therefore it
makes it harder to anticipatelikely outcomes.
Meaning if I go in and I'mgoing up to contact someone,
Greg and I'm going, oh man,anything can happen, I'm not
sure One it increases theuncertainty level, increases the
anxiety level, cognitive loadand my brain's all over the
place.

Speaker 2 (02:47):
Exactly.

Speaker 1 (02:48):
So it increases my cognitive load, and so this is
the big thing that we talk aboutwith our behavioral approaches,
with HBPRNA and how we leverageit to reduce the uncertainty
right.
So I don't want to increase it,I want to reduce what's
uncertain.
I want to reduce what'suncertain, I want to get rid of
unknowns, I want to focus on thethings that matter.

(03:08):
So that's what we do and whatwe train people how to do.
But there's a lot that goesinto it and I want to hit on
some of the kind of big picturetopics that we don't typically
explicitly get into.
But that's kind of what we cando here on the podcast, versus
covering this stuff in course orlike in a training course or
something like that.
But you know, we we use thingslike game theory and probability

(03:30):
theory and and Bayes theorem,and we'll define what, what all
that means for everyone, andbecause we stick to the science
and we use it in a manner what Ithink for, for, for which it
was intended, right and and anactual use case, in a sense that
I'm not a mathematician, I'mnot a high level expert in those

(03:50):
areas, but I know them wellenough to use them in what we do
, right and I can point back tothem and saying this is where
this comes from.
So there's a, there's a, there'sa lot we're going to get into
in there.
But I really want to.
The big thing is really sort ofdebunking this myth, this idea
that, well, anything can happen,or you don't know that.
It's like, well, yeah, no, youdo know that, and then get into
things.
Um, what we get into humaninteractions is sort of as like

(04:11):
using games as an analogy, andwe'll talk about that not just
game theory, but but games andhow they reflect life, and then,
in a certain manner, you know,in what we're talking about
today, can help your ability tohelp your predictive analysis
abilities, right.
So, and when we're getting intoeverything I just brought up
with, anything can happensometimes, you know, knowing

(04:34):
what you might not know what'sgoing to happen next, but
knowing what isn't going tohappen or what's unlikely is
helpful, Meaning going like well, I know none of this can happen
over here, so I really onlyhave to focus on these five
things instead of these 50things.
So that's that reduction incognitive load, that's a
reduction in uncertainty thatyou're talking about, and now I
can account for this.
So I'm kind of talking aboutthis, this big picture, but

(04:55):
let's start with uh, I think weshould start with games, greg,
if that sounds good, so let'scomment on it.

Speaker 2 (05:00):
Yeah, let's talk about what you just talked about
first, because that's a greatintro, and so this is my
continued argument for improvinglaw enforcement training and
simulations, because whathappens is we're focused on
motor learning and motor controland speed all of these elements
that are not going to affectyour decision making when the

(05:20):
time comes, and I'll give you anexample of that.
You just talked about reducinguncertainty.
So I saw a guy yesterday on aLinkedIn post that was drawing
from a paddle holster that wasappendix carry, fired all the
rounds safely and quickly, andit was in the two second range
that he emptied his mag into atarget.
Okay, so do me a favor.
When will that reduceuncertainty?

(05:42):
That will reduce uncertainty atthat one time in that person's
life where the rotor hits thespark and that oxygen hits the
fuel, and it's a thing where youhave to draw, outdraw an
opponent and fire all the roundsin your, in your weapon.
You know, I was a cop for 30years.
That never happened to me.
Now, how many cops on thestreet has that happened to?
And the idea is that you thinkyou're doing great training and

(06:05):
I'm great with that type oftraining.
But look at the Olympics.
The Olympics is full of peoplethat specialize in one thing for
their entire life.
For the likelihood that that'sgoing to come around, and that's
not how things work, and that'swhy I just want to throw that
on the table and I want to sayworld according to Greg.

Speaker 1 (06:25):
That's actually a great way to, because we're
using this games theory andgames as an analogy.
But that's actually a really,really important distinction and
we'll get into how we definewhat games are Exactly.
That's a very these analogieswhere it comes from sports
performance to now we're goingto talk and carry that over in
like military or law enforcementor high risk situations.

(06:49):
It does not translate it, justdoes.

Speaker 2 (06:51):
It can't translate because it's not the same.
What you've done is you'vetaken Monopoly and you've taken
checkers and you're reading methe directions to play Monopoly
and handing me a checkers board.
It can't converge, you know.

Speaker 1 (07:06):
Because what you talk about is the scope.
The scope is so limited In a100-meter dash, that's it.
Like there's no—you get tofocus on one thing and there's a
ton going on.

Speaker 2 (07:14):
Don't get me wrong.
It's not that it's not acomplicated skill, but it's
nowhere near as infinite as thepotentials that could happen in
something like human resourcesor police work or being a
teacher.
So my additional argument forimproving law enforcement is
that you have to understand thegeneral limitations of a problem

(07:34):
, limit the potential solutions,and what I mean by that is
simply the probability theory.
And most people say, well,probability theory, mathematics,
this and that and the other.
Well, I'll tell you thisDiscussing the likelihood of
everyday events, like the chanceof rain or the probability of
winning a game, make these hugemathematical theories and
principles more intuitive forthe students to grasp.

(07:55):
And that's why, when we'redoing like a briefing, you
always see me use a slide aboutsnow and lightning and fog, and
the reason I do that is if youcan predict them, you can
predict likely outcomes right.
So what I'm trying to do is,instead of making the problem
bigger, I'm trying to clear,make the outcomes more clear so
you can better understand whatprobability means.

(08:16):
So we talk about patternrecognition and analysis.
That means we predictlikelihood and teaching tools
when you give a practicalexample like, we predict
likelihood and teaching tools.
When you give a practicalexample like uh, uh, you know
how this is going to work makesmore sense to a person.
I'll give you an example.
So you know, I go shoppingevery week and I go shopping at
the same time, the same store.
There's tens of thousands ofchoices that I could make in

(08:39):
that store, but somehow Inavigate every aisle, take the
items that I want and want and Icome back.
So, even though there seems tobe an infinite amount of choices
, there's literally a finitenumber of items that I select
from, and my predispositionguides me that way.
Now you talked about games.
So what's the difference ingames?
So the elements of a game arethat you have decision makers,

(09:03):
the players.
Then you have their actions, sothe choices that are available
to them, and then you have theinformation or knowledge that
they go in with or that theylearn while they're playing the
game based on their opponent.
That's exactly what you'redoing at the grocery store, but
there's not an unlimited amountof choices.
There's a finite number, eventhough it's a big number, brian.

(09:24):
And so when we talk about that,why is that important?
And that's just touching ongames and game theory, very,
very briefly, because we'regoing to discuss it.
But what I mean by that is itseems overwhelming.
Yet I navigate it every week.
Law enforcement, with all thechoices and the possibilities
that could come up when you bailout of that car, from dispatch,
from the RP, from the scene andthe location and the weather

(09:46):
and all those other stuff, seemsunmanageable, it seems infinite
.
It seems like anything canhappen, but at the end of the
day anything can't happen.
As a matter of fact.
A great thing about probabilitytheory, and I don't want to get
deep into math.
But it's zero or it's one, it'sgoing to happen or it's not
going to happen.
Now you know how manyvariations there are between
that zero and that one.

(10:06):
But guess what?
They're not infinite, they'refinite.

Speaker 1 (10:10):
Look, physics limits the number of things that can
happen, yeah, unless you want toget into the mathematical
argument about infinity and ourgrasp of it.

Speaker 2 (10:20):
But how many?
theoretical arguments can bemade for that on the scene with
an opponent, he's going to havea weapon.
Well, a weapon can vary he'sgoing to be aggressive or he's
going to be passive.
Aggressive or he's not going tobe aggressive.
He's going to be communicativeor he's not going to be.
When you start branching that,it's just like Wing Chun or just

(10:41):
like martial arts.
When you're practicing Akata ordoing sparring, the person
doesn't come in and drop a smokebomb and then stab you with a
sai and go.
Hey, there you go, because thatwould be outside of the ken,
outside of the realm of thepossibilities that you're going
to do on the mat at that time inthe dojo.
So training prepares us for asmany of those contingencies as

(11:04):
possible and it's up to us tochoose which ones we want to do.
So, instead of choosing theone-inch punch, you and I have
chosen to spend our entire lifeon cognition, on thinking and
out-thinking a cunning opponent.
And that's literally whatyou're talking about is the
difference between coming inwith the mindset and you know I
hate mindsets coming in with themindset and saying anything can
happen right.

Speaker 1 (11:26):
Yeah, you're setting yourself up for failure.
So you started with games andyou gave some of the core
elements of the games and kindof defined them.
About the players, there'srules there's information,
there's outcomes.
So can you, because games are agreat analogy but far more
significant than we think.

Speaker 2 (11:43):
In a sense, so can you give us like?

Speaker 1 (11:45):
the historical significance and what we mean by
games.

Speaker 2 (11:49):
So, shelly and I just before Shelly left this morning
, our CEO.
I said, hey, I'm going to betalking about games today, and
she reminded me that, 2007,.
Uh, third Marine, connie OweeBay.
She said that's the first groupof Marines you talked about, uh
, with games, so bring that up.
And I said, okay, I'll bringthat up.
Shout out back to the day,brian.
But if you recall that class,when we walked in, we said that,

(12:10):
okay, every culture on theplanet has games.
And then people paused for aminute and they started thinking
about it because we had a.
Marines are one of the mostculturally diverse fighting
organizations on the face of theplanet.
Right, okay, I learned thewords I needed in Tagalog from a
Marine, right, yeah, so gamesare also probably music is up

(12:32):
there too one of the oldestforms of human social
interaction.
Okay, games are a way to teachand pass on knowledge and a way
to store knowledge.
That game on the shelf storesthat knowledge, brian, just like
a book at the library.
And then, when we look at itancient games, the oldest games
that we know about.
What did they teach?
They taught farming and huntingand survival skills and social

(12:55):
intercourse, how you meet otherpeople, how you're supposed to
act, and they helped developthis social and emotional and
physical and cognitive skills,and so when we start talking
about limiting stuff, okay, nomatter how diverse games are,
they all have a winner, a loseror a draw.
They all have a set of rulesthat the people follow and no

(13:18):
game is infinite.
So somebody right now that'slistening is going to go well,
there's no rules in a knifefight.
Fuck, yes, there are.
You're just you know what.
You went to the wrong dojobecause there's a whole bunch of
rules in that.
You know, gravity still applies, physics still applies Distance
time.
So when you make comments likethat, what you're doing is
you're showing your naivety, andthat's why we're still not

(13:41):
happy with the state.
Look, we partner with a wholebunch of companies and when we
see the state of training insome places, we object to
certain things that are stillmissing.
Why?
Because our locus of control isto 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 obtuse,you know your, your locus of

(14:05):
control.

Speaker 1 (14:06):
It goes back to actually, I was thinking of that
with the mindset discussion wehad this morning before we got
on here.
But, um, you know, what you'retalking about is what humans
think that they can control.
So so if anyone's never heardterm locus of control, it's just
it's all external forces thatthey that plan out their life or

(14:30):
or everything.
I'm set up for failure and this.
And people have a great, youknow, internal locus of control.
Really understand that like, no, I can change my life, I can I
control the outcomes of mysituation.
So that that's what you mean bythat.
But you, you brought up somegreat points about games,
because games are a.
Throughout history, everyculture has played different
types of games.
Right game, whatever chanceskill, uh, physical skill,

(14:50):
mental skill, whatever exactlyand and with that, because
they're, they're used as, likeyou said, a teaching point.
They're, they're a model.
They're a model for humaninteraction.
So, you know, it's better if wejust go do the, you know three
day long booskashi tournament,rather than worrying about one
another and fighting overeverything, uh it's better

(15:13):
because there's always an endstate.

Speaker 2 (15:15):
Brian, there's always an ultimate goal in games.

Speaker 1 (15:17):
You're exactly right, spot on because it's when, when
people go.
I don't understand like peopleget so intense.
Or how can you get so intofootball or soccer that you're
going to be like?
This is an extension of thevalues of your life, and and and
yes, some people take that toofar they let it.
They let that sort of primitivereaction take over where, where
they're all in on the game.

(15:38):
And they're going to, you knowyou go to, like South America,
where they I remember that, Ican remember it was like the
goalie uh, for one of the teams.
It was murdered, you know,after he let up a goal where
they.

Speaker 2 (15:48):
you know that happens often, yeah.

Speaker 1 (15:50):
And you're going like how does it get to that?
This is insane.
You're such a fan.
It's like no, no, like this isa very primitive extension of
the model of human experienceand that person was so into that
.
Now they went too far with it,obviously, but it's not a large
gap, it's not a big bridge tocross there.

Speaker 2 (16:08):
You wouldn't be surprised if you read that in an
article.
You wouldn't be surprised ifyou heard that and remember,
look, shout out to Milo, becauseMilo was the first company to
understand Hoberman and embraceit.
And people are now comingaround and thinking what's going
on with it?
Because we meaning ArcadiaCognorati, Brian and I and our

(16:28):
partners we promote cognitivedevelopment in classrooms and in
AI and in virtual, byrole-playing and problem-solving
and logical thinking.
We value creativity.
We force you to play it justlike in a game, and in class we
play those games.
We involve strategy andplanning and we encourage
critical thinking anddecision-making.

(16:49):
We force them to recognizepatterns and sequences so they
understand the cues, so they cansolve for X before they see X.
And that's the difference.
We're not seeing that in gamesnow.
No, no, Well, you know that'swhat training is.
Training is a game.

Speaker 1 (17:02):
Exactly, I mean, that's what I just said.
That's what training is.
Training is a game.
Exactly, I mean, that's what Ijust said, that's what it's a
protracted game.
Yes, it's a.
It's a.
It's a.
It's a.
You're modeling, you know,you're in simulating um, a
likely future event, and you'reallowing yourself that mental
rehearsal.
So you're, you're playing agame because you're, you, you,

(17:23):
you, you're going to get to thechampionship maybe one day, or
you're going to get tested on itsomeday, or there's going to be
an opponent that's going tochallenge you in said game.
And so, game theory, I'll giveit kind of like a quick
definition of game theory, greg,and we'll talk about it because
it's important to and realquick.
You know, not getting into thisto try to be like, oh look, how

(17:44):
much shit I know, becausethat's not important.

Speaker 2 (17:46):
It's not what this is about.

Speaker 1 (17:48):
But it's about naming these things, because when we
get into probability theory,game theory and especially
Bayes' theorem, these are thingsyou actually do unconsciously.

Speaker 2 (17:57):
These are already things that you do every single
day.

Speaker 1 (18:01):
So if I can get some recognition and understanding of
some of the elements of it, itcan help me going forward.
We'll get to that later, but Ijust want to.
I don't want to come across aslike no, no, no no.
You guys just talking about someshit that doesn't matter, it's
like these are things that youdo.
So, so, game theory well, gametheory is, you know, sort of a
branch of mathematics andeconomics studies strategic
interactions between decisionmakers or the players in the

(18:23):
game.
So what it is?
It's a framework foranticipating the actions of
others and making informeddecisions based on potential
choices available to all theparties involved.
So, at its core, game theoryhelps us understand situations
where the outcome for eachparticipant depends not only on
their decisions, but thedecisions of others.

(18:44):
Participant depends not only ontheir decisions, but the
decisions of others.
So the idea it's a little bitmore chaotic.
Uh, in a sense, or or it allowsfor more contributing factors
than just say, like a one-on-one, like your, your checkers game.
Right, there's the is isnowhere near as complex as chess
.
Um, you know what I'm saying.
So it's it's just a little bitdifferent.
So game theory really kind ofcan take that into account and

(19:04):
the the same thing.
You got the players and theirstrategies and information and
it just like we talked about ingames.
But I just want to get that outthere.
No, no, no, no, so so allow youto discuss the.
So what behind it?
Really?

Speaker 2 (19:14):
Right.
So so the the, so whatlistening to us?
And so if the outcomes dependon the player and the actions of
each participant, then it'sexactly like police work,
because you're a player in agame and you choose your action

(19:35):
or your strategy and you have totake into account the choices
of others.
Now they may play their rolefirst, 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 strengthor coordination or endurance, it

(19:58):
does the same thing and it'sjust as good for you cognitively
and it forces you to do thesethings and requires manual
dexterity and assist in yourfine motor skills.
So if you can find it in theworld, you can find it in a game
.
You know what games do?
Games evolve, brian.
What does that mean?
Games change as society changesand that means that as players

(20:20):
get better, the paint and theamount of time you can spend in
it changes, right, and there's asmaller goal and the goalie has
a smaller area that he has todefend.
So they make it more complex byadding these things to it,
which is great.
Which defines evolution?
Yeah.

Speaker 1 (20:39):
The NFL right now.
They just made big changes thisseason.
For some of the kickoffs theyjust changed big changes this
season for some of the kickoffs.
Basically, they changed some ofthe rules on this for safety
issues, because now people arebigger, stronger and faster than
they were 100 years ago, whenthose rules came up with and
they're going like.
This is extremely dangerous now.
So it had to evolve.

(21:00):
The Olympics have changed.

Speaker 2 (21:02):
Things are added to the Olympics, Things are taken
away from the Olympics and guesswhat?
Just like in class, we'reforcing them to challenge their
memory.
And then we have them performwhen their concentration is
challenged.
And we force that in the classand in the training and in the
practical application scenarios,and then we add different

(21:23):
temporal elements to change thelevel of stress, just like in
real life.
Okay, and and you go.
Oh well, every game does it.
Well, jeopardy does it on TV.
But you know what?
The outcomes aren't thatsomebody dies.
I've yet to see him take a twopound sledge and the, the person
that comes in third, beat himto death on stage.
The idea is that theconsequences and outcomes are

(21:44):
virtually interchangeable inlife.
Within a game, there's losers.
The difference is that theloser doesn't die.
Okay, we get that.
But that's how you have tothink.
And the problem with a mindsetdon't get me started, but a
mindset's powerful and itcreates.
I know I always start itMindsets create realities and
shape your thoughts andbehaviors in very important ways

(22:06):
.
But the problem is they alsocreate blind spots and they fuel
biased thinking.
And when left unchecked,they're harder to change because
now it becomes a part of yourbehavior and your mindset starts
to remain, even though you'reconsciously aware of other
factors, and that's when itbecomes an inhibiting factor.

(22:28):
So if you start thinking ofjust the science, here's the
number of reactions that mighthappen.
Here's the finite number ofthings that could happen.
I'm going to play this in a gamebecause I understand game
theory and I understand thatthere's finite constraints.
There may be tens of thousandsof possibilities, but guess what
?
That's still finite and it's alarge set.
But I can rehearse one of twoways I can rehearse 10,000

(22:52):
different moves in karate, or Ican understand anatomy.
I can go 2,500 punches with myright hand, or I can understand
physiology, and that's thedifference.
The difference is that I cantake a look at our training and
our training is training us forevery eventuality by improving
our cognitive acumen andimproving our ability to assume

(23:16):
what might happen next, andcreate an ML and an MD co.

Speaker 1 (23:20):
And that's the idea of it is what obviously we want
to get people better at, youknow, predicting behavior.
So you need some sort of toolsto use this, and this is these
are the, these.
What we're talking about todayare the, the foundational
elements of the tools that weuse.
Right, and we go OK, knowingthis, knowing meaning, knowing

(23:40):
these things about science andmath, right, yeah, this is
what's sort of known, so.
So then, how do we use them?
Now you can use those differenttools, right?
So that's the idea, and that'swhere you have to come in with
something.
I can't stand there with acalculator you know what I mean
in a situation, greg and figureout what's going on.
So training is for.
So training helps me.

(24:00):
That's what you're doingExactly.

Speaker 2 (24:01):
So training helps me Exactly, but training helps me
understand that a calculator anda 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 thedecision and to choose what the
ultimate eventualities are of asituation, and you know that's
why, when we do training, brian,it doesn't matter if it's
raining or if it's snowing or ifthere's a tornadic situation

(24:24):
that's going on.
I'm moving my location.
Yeah exactly, and it doesn'treally matter about all those
other factors, because thosefactors are going to occur in
real life too.
And that's the amazing thing isthat a simulator can do so much
and we're not using it to itscapability many times.
Right, because practicalexamples, simulations,

(24:47):
experiments.
They're very effective becausethat allows a student to see
that there's randomness, that'ssurrounding chaos, right, but
that certain patterns emerge, nomatter how chaotic a situation
is, and that's the magic.

Speaker 1 (25:04):
And I didn't really want to get into the randomness
yet on this episode becausethere is another one, but it's
an important thing to bring upas we're talking about all this
and probability and what we'llget into next with Bayes'
theorem.
But the idea there is, there'sa lot of randomness in the world
and because humans are primedfor pattern recognition and we

(25:27):
want to put things together andunderstand it and we don't want
surprises, we don't wantuncertainty, you know we'll
often attribute value to thingsthat really have are
insignificant and completelyrandom, because there is a ton
of randomness in the world andso we can, we can sort of get
that can cloud our judgment,which is why I have to be able

(25:48):
to account for that.
But you know the big, you knowagain.
So the, so what.
On that game theory, what we'retalking about is really just
balancing those knowns andunknowns.
Right, we'll define what thatmeans.
But you know that this is thepredicting behavior.
Understanding interactions as agame allows me to model that in
a number of different ways.
It allows me to to actuallyhave these conversations and you

(26:11):
know, because everyone does the, the what-if scenarios or we'll
do.
You know people call them tdg,sometimes a tactical decision
games, or I'm going to give youa set of circumstances and a set
of constraints and you tell meand we're just going to walk
through on a whiteboard, okay,then I'll allocate these
resources, resources here, thenwe can do this.
Okay, well, what if then thisoccurs?
Okay, so these are all allgreat, great things.
Those, those are actually farmore powerful than they're given

(26:34):
credit for, because a lot oftimes they're just not set up
correctly.
So because, because I have tounderstand you know what are the
what's the likelihood of thesedifferent situations occurring,
and because I remember, even afew years back, even running or
still running, some, sometactical training, and it was a
big ending exercise, and therethis, this team, the couple of

(26:55):
teams that were going through it, were doing an absolutely
phenomenal job and, like beyondwhat we thought, like surprised
us during this final exercise,and we were like, holy crap,
they're really, they are killingit and they had great comms.
They had everything set up,they're task organized, they're
doing so well and it's greatplan, right, so then what do we
have to do?
We're just like, all right,well, let's just, let's just see
how far this goes.

(27:15):
And so we obviously startedcoming up with ridiculous stuff
and even all these problems, tothe point where they're like
yelling and getting frustrated.
And then at the end, when wewent to do the debrief, they
thought that they had failed.
And you know, I had to startoff with like all right guys,
that was the best team we'veever seen come through.

Speaker 2 (27:31):
Wow through like confusing.

Speaker 1 (27:32):
Like what do you mean ?
Like, like guys, like we weremaking stuff up at the end, like
we, just we had enough time andenough to do it.
So we just said, like jesus,how far can these guys go
because they, they did it sowell?
But but the idea is like thatthat we, we, we get that wrong.
Sometimes we're coming up witha what-if game.
So this is trying to help thatthat sort of what-if game to, to
keep it within the realms ofthe possible, and then you know,

(27:54):
you know you're going to get alot more value out of it.
So what?

Speaker 2 (27:59):
you did there too.
No, no, just to add to thatwhat you did there.
Everything is evidence-based,just like you know.
Absolutely overused term, right, but the?
idea is we do, and I can see itand feel it and taste it.
Okay, but the idea is that whatyou did is you were conducting
an experiment.
Yes, so what you did is theexperiment yielded results and

(28:23):
you can lift and shift firebased on those results.
So it's not how much fasterthat you can get through the
scenario or how quickly you canwin.
It's what you learned from thatwin or loss, what you learned
from that tie game, what youlearned from that win or loss,
what you learned from that tiegame, what you learned from
playing against this opponentrather than another opponent.

(28:43):
And if you can pay thatknowledge forward, that's the
key.
Look, what's Bayes' theoremtell us and I know you're going
to get into Bayes, but let's doit real quick.
A street definition of Bayes issimply this you have to update
your probabilities based on newand incoming information.
And if you're not constantlydoing that because we have
certain assumptions, brian thenall of a sudden we're in a

(29:05):
situation.
Dispatch told us this we getout of the car.
Yeah, there is in fact anargument 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 killingeverybody in this scenario or
whatever real-life situationwe're facing.
So we have to update that, andwe have to update that quickly
because as that prediction, oras that information changes,

(29:25):
your prediction of likelyoutcomes change.
And if you can't do that andpeople go, well, isn't that like
John Boyd Zootlew?
That's like every criticalthinker that's ever lived?
Oogluck, when he came out ofthe cave with a wood sap, he had
the same realization.
If I don't update, then what'sgoing to happen is I'm going to
continue to stay on the gascoming into the turn, I'm going

(29:46):
to overrun my headlights andguess what?
I'm going to crash.
So Bayes and Bayesian thinkinghas always been a root of Greg's
experience.
I hate to talk about myself inthird person, but look, I'll
tell you how profound it is.
You and I met when you werecoming in and out of combat
zones and you were part of atraining evolution that I was

(30:08):
doing.
And you go this guy's full ofshit until you started trying
some of these and then you foundout wait a minute, it makes me
faster, smarter, harder to kill.
So it's not about me.
I just happened to be the guythat introduced you to the right
book in the library.
That's what this is about,brian.
What I think our primary job isis to create a legacy of
opening eyes on people thatdidn't see it that way, before

(30:32):
handing out those flashlights,so they can search the the box
instead of going outside of thebox.

Speaker 1 (30:37):
In all fairness, my, my reaction was actually oh my
god, this guy's fuckinghilarious and okay and he's
completely full of shit.

Speaker 2 (30:45):
But he's the two-point standard that you had
like.
It was like okay like he's a.

Speaker 1 (30:48):
He's a bullshitter.
I I got plenty of buddies likethis.
I've met people like this beforeand then I was like oh wait a
minute is hang on, there'ssomething going on here and to
to to kind of um, to furtherdefine, I guess.
Or we're talking about thecause, you, we brought up Bayes
theorem and this is anothergreat example of something you
unconsciously use every singleday of your life, right,

(31:19):
no-transcript?
Then you have an updated beliefand there's a million different
studies like this and I've usedones before in class where
they'll give someone someinformation or they'll say find
out what everyone's beliefs are,and usually in something that's
a very like politics are agreat one because a lot of
people are very set in theirways and how they look at
certain things.
They'll take a political issueand then find out which side of

(31:41):
it you're on, and then they'llforce you like hey, do like a
500 word essay on the opposingviewpoint and then all of a
sudden, when they have to do allthat work, they kind of go
their.
Their initial viewpoint waslike, oh man, like I guess I
wasn't taking into account allof these other things, and even
to the point where I did.
The one from today was I'll putit in there.
It was an interesting onebecause they use a great example
about, about our prior beliefsand the different kind of how

(32:05):
sometimes we we areoverconfident and when wrong, or
sometimes not confident in thethings that we do know, and
that's the two sides of the cointhat we're talking about right
here.
Yeah, exactly, we're going,we're going.
This is a lot more complex.
There's a lot behind this andsomeone's listening going man,
you're talking about all thesemathematical stuff.
I don't understand this, orwhat are you getting at?
It's like no, no, no, no, no,no.
You actually do know this.

(32:31):
They use a great example is youknow?
You walk up to someone and theygo.
Hey, do you know, do you knowhow a toilet works?
And they're like, yeah, andthen they go, well, can you
explain it to me?
And they're like well, I, Ijust kind of push the handle and
flush it.
Actually, I don't know how atoilet works, Right?
So so we, we have this.
Yeah, I think I understand it.
The cell phone is phone works.
I mean how it actually works.
Almost no one can, and we'repast the point of even getting

(33:00):
to the base, because it'sevolved so quickly, past what
original cell technology was,versus a landline Like.
It's highly complex, but weknow how to use it right and
that's all that matters to usand that's actually what HPP RNA
is really.

Speaker 2 (33:14):
It's simply.

Speaker 1 (33:15):
Exactly.
I don't care if you can tell meall of this stuff.
I want you to be able to use it, but understanding some of the
big picture concepts are reallyimportant.

Speaker 2 (33:23):
Yeah, I absolutely agree.
So let's go back to Bayes andBayesian thinking and let's talk
briefly about how it impactsyour life every day.
So I'll give you a fail inBayesian thinking.
Every single year that I'vebeen alive, I've done an article
or a report or talked aboutconfined space entry death where
methane gas has built up andthe farmer goes down to clear

(33:44):
out the vent.
He dies, then his son, then hiswife and the whole family is
dead Every year.
Okay, that happens Every year.
During graduation.
I do a story about a car full ofkids that go out and they're
not harming anybody, but the carhits a tree and rolls over and
does it.
So Bayesian thinking is a formof high-speed hypothesis testing
that says, when certainpre-event indications coalesce,

(34:06):
I have to update what I thoughtwas going on.
The kids are just going to aparty, they're just going to go
out for a drive.
Maybe it's methane gas, maybe Idon't see it.
It's colorless and odorless andtherefore that changes the
likely interaction with theenvironment.
Right, and so the?
The adjustment of expectationsis huge.
So what do I mean by that?

(34:27):
Everybody that's a cop that'slistening to this, everybody
that's HR, or if you're ateacher or administrator at a
school, you saw a behaviorchange 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 orwhatever else, and you didn't
account for it.
And then you saw another thingwith the same person or

(34:48):
situation, or environment orfinancial, it doesn't matter
what the baseline is and youdidn't account for it.
And then, all of a sudden, herewas this failure.
Something went catastrophicallywrong.
That dysfunction showed itself.
And then we looked back and wesaid you know what?
Every one of those things waspresent.
I just didn't account for them.

(35:09):
So that predictive analysis toupdate your probabilities based
on the new and incoming evidenceis based.
That's the root of Bayesianthinking, but it's also
probability theory and thebeauty is it's also game theory.
So each one of these isintrinsically connected to how
you process information.
And if you fail to see how thebreadcrumbs coalesce, if you

(35:32):
fail to see that gosh damncottage in the wood with a lady
with a cauldron going, come in,have some candy, then you're
going to die.
I know we make thatoversimplified, brian, but we do
it for a reason.
So you go.

Speaker 1 (35:43):
Hey, I know what he's talking about and with there's
a thing to add in here with allof this, is that there's a few
things One, you One, statistics,probability theory, even Bayes
theory this is the most, I guess, the most newly defined parts
of math, meaning math's beenaround forever, it's just

(36:07):
existed, and then humans kind ofunderstand it as time
progresses, right.
But the reason why it'simportant to understand some of
this stuff, at least at atheoretical level and big
picture level, is it's importantto understand some of this
stuff at least at a theoreticallevel, you know, and big picture
level is it's one thing.
I was saying class, like humans, we don't intuitively
understand probability.
We really don't like weintuitively understand physics.

(36:27):
If you never got taughtanything about physics, you
still understand what gravity is.
You still understand what forceequals mass times.
Acceleration means, right.
You implicitly understand thatbecause it governs the way you,
you, you, uh, go through theworld, right?

Speaker 2 (36:42):
Locomotion, balance everything.
Yes.

Speaker 1 (36:45):
But with this stuff, it's, it's, it's, it's not.
This is not an intuitive way oflooking at things, because the
intuitive way of looking atthings is simple, it's
survival-based, and it's what Iknow in front of me and sort of
the reason why Bayes' theorem isso powerful too, because it's a

(37:07):
little bit different, in asense, of probability theory.
That probability theory is very.
There's a lot of rigor, it'svery mathematical base and it's
a lot of axioms.
There's a lot of rigor, it'svery mathematical base and it's
a lot of axioms, right, whereasbayes theorem kind of allows for
more subjective observations tobe put in right meaning, you
have different experiences thanI do, so so you can use this

(37:27):
kind of model, um and, and youmight get a more robust or
different answer than me and yeswe're.
We can both be right in a senseof what we think it is, but
maybe yours is more right thanmine.

Speaker 2 (37:42):
You know what I'm saying, right?
But I can also use that sametool, those three tools, to
transfer my knowledge andexperience to you, and I do that
by playing a game.
You and I do that by playing agame.
So what is a practicalapplication scenario?
It's a game.
There are players on both sides, there's an outcome that we

(38:06):
think is likely and people haveto use strategies to negotiate
the rules that we put into place.
Okay, don't look now.
Now watch.
This is a day in the life,those type of elements that we
put into it.
And let's go back to how longthis has been important.
Musashi said Miyamoto.
Musashi says you win or losebefore ever drawing your sword.
Musashi beat people with abodor.
Musashi beat the best samuraiswith the sheath from his gosh

(38:27):
damn katana right.

Speaker 1 (38:28):
If you've read Sun Tzu, then that's Bayes' theorem,
exactly Everything he says.

Speaker 2 (38:37):
So let's talk about that.
So why is there such an impetusin in now that the curiosity
and the training and everybodythat we listen to that that's
putting out great stuff?
There's nothing that somebody'sputting out that's wrong but
it's locus is on motor control.
Why?
Because we understand motorcontrol, we understand
sympathetic and parasympatheticexactly.
But the problem with when we'retalking about cognition is we

(38:59):
don't completely fullyunderstand the brain.
We don't understand all thecorticles, we don't understand
even what sections of the brainare responsible.
So it's mysterious, but it'snot.
It is Because, if you look atscience, if you look at nature,
how does nature warn us aboutwinter?
It gives us fall.
How does winter or nature warnus about the time to plant?

(39:20):
It brings us spring.
Do you see what I'm trying tosay?
So it forces us to learn byoutside experimentation.
And the more times that we gothrough those, brian, the
hypothesis testing, the betterwe learn.
And so Bayesian thinking ishypothesis testing.
So I'm in this pursuit, butthis guy's taken way too many

(39:43):
chances.
So what are my possibilities?
He's drunk, okay, but he'sholding 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 fewdecisions become crystal clear
on what might be going on.

Speaker 1 (39:57):
And you're talking about it right there in the
moment.
But I also have, in thatspecific example, historical
precedent.
What happens the longer thatpursuit goes on.

Speaker 2 (40:04):
Does it get better?
Exactly.

Speaker 1 (40:12):
Do the potentially good outcomes increase or
decrease over time?
Because what historicalprecedent says is that it gets
worse the longer that goes onthe more likely this is not
going to go well.
Right and so that informs thedecision making, and that's a
perfect example of how, rightthere in the moment, I'm
constantly updating myhypothesis.
Of course talking about istrying to be more aware, or, you

(40:35):
would say, mindful, of thisprocess so that I can actually
uh get better at it and and andthat's sort of the the, the way
these things coalesce of, ofgames, the game theory, and bay,
bayesian or bayesian thinking,and uh and you know that I I
call it bayesian only based onthe fact that's the way I was
trained and taught, and you knowthat I call it BSing and only

(40:56):
based on the fact that's the wayI was trained and taught.
You can use either one, I think.

Speaker 2 (40:59):
G-Rad and Amanda Miller.
Right, I mean, I respond to theway that I'm trained.
And, brian, I think your pointis so important.
I want to make sure that youclearly make it.

Speaker 1 (41:09):
Yeah, yeah, no, and when these things?
The reason why we're talkingabout all this together, I think
really is what you'reconstantly trying to do,
especially in complex situations.
Now, not just complexsituations.
Now add in that there'spotential danger involved in
that complexity because, likeyou know, a market system or
figuring out what you want to doin life can be a complex

(41:30):
situation, meaning there's anumber of inter and intra
dependent factors that affectyeah, of course.
And you don't control them all.
Right, that's the thing.
Hey, the bet, the bad guy getsa vote too.
You know what I mean.
They, they get, they get a sayin what's going on in the
situation too, and you don't you?
You, you don't get to how muchinfluence you have over that is
is finite.
But what we're, what we'retalking about is is balancing

(41:52):
the knowns and unknowns in everyinteraction, in every human
interaction, in every way, everysituation you go into.
So we've been talking about itfrom real situations, from
training situations and justcognitively how we think as
humans.
And you know, you kept sayinghypothesis, testing and that and
that that's the best way to doit, and that's what games do as

(42:14):
well.
Right, so so that that thefootball game or the college NFL
, whatever, when they're goingup against their opponent,
they've studied their opponent,they've studied what they do,
what, what their strengths andweaknesses are, and then I have
to compare that against my teamstrength and weaknesses and how
I'm going to play against thatand and and really play to my
strengths and try to try to uh,uh, you know, eliminate them
from exploiting my weaknesses.

(42:34):
And then there's a little bitof randomness in there.
If that temperature all of asudden drops 30 degrees that day
and maybe my team is more usedto playing in warmer climate,
the rain on the field, there'san injury right before the game
where someone who's a top playergets pulled out and they have
to sit that game out.
That's where that stuff reallystarts to affect it.

(42:56):
To sit that game out, that'swhere that stuff really starts
to affect it.
But it still comes down to at abasic level is if I get good at
identifying knowns and unknowns.
And so the knowns, that's justunderstanding and recognizing
different patterns, establishedbehaviors, environmental cues,
everything that we talk about,and I want to increase as much

(43:18):
as what I know about a searchsituation to reduce the
uncertainty.
So I don't want to throw inthere anything and then I can
compare that to what don't Iknow right.
And now I have the comparativebaseline.
The question that you have toask yourself, and this is
Bayesian as well to update.

Speaker 2 (43:32):
The question that you have to ask yourself and this
is Bayesian as well to update iswhat am I missing here?
Okay, this guy's driving likeI've never seen anybody drive
before.
This person's fighting harderthan I've ever seen anybody.
This person, no matter what I'mtelling them, they want to jump
off that bridge or hide thatevidence or do whatever else.
What am I missing?
There's something here.

(43:52):
There's an environmental,there's a piece of information,

(44:16):
no-transcript.
Okay, look, that's the jack inthe box.
We try to reduce the jack in thebox every time that we do the
training, but guess what?
Because there's a spectrum ofpotential possibilities.
That's always a possibility.
But if you only train for that,how is that affecting your
de-escalation technique or theuse of cover or all the other

(44:39):
things that are infinitelyimportant as well?
So if you just look at that andgo, yeah, but that can happen,
then we're right back to thetheorem that you posited at the
very beginning of this.
Well, anything could happen.
No, that's one of the thingsthat could happen.
So, yes, I need to be ready forthat.
But you know what?
Every single day, there'sencounters exactly like that
that don't end in a fatalshooting.

(44:59):
What we've done is we'veupended the apple cart and we've
only taken a look at thosethings because they're fun and
they get clicks and people wantto 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, and it, and it's, it's it.

Speaker 1 (45:17):
We don't implicitly, you know, recognize these things
when they're laid out in frontof us.
It's a dude, this is not a bigdeal.
Look, yeah, you rolled througha stop sign, but like that's not
.
You know, it's not the end ofthe world, it's just.
There's a school here and Iwant to maybe talk to like, hey,
did you get that stuff done foryour math assignment or

(45:37):
whatever.
And then all of a sudden shegoes off the top.
I told you and I was like well,this is not a typical response.
There's clearly something elsegoing on and there's some other
thing that she has on her mindversus the question that I asked
.
And that dissonance, thatdisconnect there means okay,
there's something else here thatI need to investigate, or get
some time and distance and comeback to that and find out what

(45:58):
else is going on.
But that's the thing, it's notjust the well, that's weird.
Or hey, don't talk to me likethat, or what are you doing?

Speaker 2 (46:05):
Right.

Speaker 1 (46:06):
There has to be that instant recognition of I'm
missing a piece of informationthat's critical right now.
Right, I may need to get sometime and distance right now and
figure out what that is before Imake my next decision.
And that's the point where wedon't, but we don't do that.
In those we don't take thenon-centered observations in
general, as humans, those aremost likely the marginal

(46:30):
information.

Speaker 2 (46:31):
The space between the words and the paragraph that
you're reading are as importantas the words that you're reading
.
And we don't do that.
We don't do that holisticapproach.
Again, back to Hoberman and whythat Hoberman sphere is so
important because we got to makethat problem a 360.
We've got to take a look atthat.
And when we talk aboutpredictive analysis, we're
talking about being able torecognize patterns that would

(46:53):
tend to show a reasonable personthat a thing's going to turn
into a shit sandwich or thingsare going well, and Bayes tells
us to constantly update that.
And guess what, if you're inhigh-risk encounter after
high-risk encounter and it'sunknowns traffic stops are an
unknown, domestics are anunknown suicidal subject, an
unknown medical, mental all ofthese are unknowns.

(47:13):
You've got to be able to make afast, intelligent, informed
decision.
That's on an unpredictable setof circumstances and it has to
be legal, moral and ethical.
So where are you going to dothat?
On the street?
Are you going to do that on thestreet?
No, you do that in a game.
And the more that you do thatin a game and the more that you
start at the academy when you'rea kid and it matures during

(47:34):
your FTO and when you're old.
Now you're the gray-beardstreet vet and you reinforce
those Brianrian.
Now that becomes a way of lifeand habits are hard to break.

Speaker 1 (47:45):
Habit over mindset any day and I mean that you know
me yeah, and and so let's,let's so.
For the purposes of thisdiscussion, then, um, we let's,
let's talk about knowns, becausemy argument is especially like
you know, you're giving theselaw enforcement examples and I

(48:05):
tell this every course we go toor every time I've trained or
worked with whatever.
I mean that was even the firsttime when I uh, I mean like he's
obviously had a militarytactical government contracting
background and then startedworking with law enforcement as
well.
Right, and I'm like my thingwas holy shit, these folks know

(48:26):
more than they realize.
Like your knowns are.
So you gather so much tacitknowledge and experience and
that's anyone with any likesubject matter expertise in
anything right.
I don't care if you're a pilot,I don't care if you've been
doing you know HR your wholelife.
I don't care.

Speaker 2 (48:42):
Lawn maintenance.
It doesn't matter.
You're exactly right.

Speaker 1 (48:44):
You're exactly right, you know so much more and those
get talked when you get intothe science of, like you know,
intuitive decision making andsubject matter expertise.
Right, that's all there.
That's, those intuitivedecisions that people make are
based on their selected priors,based on those really good
cognition biases that they'vedeveloped through experience.

(49:05):
But I, you know, rather thanbecause this goes into the
argument of people like, well,what do I need to look for and
what do I need to do, and it'slike no, no, no, Focus, focus on
vanilla.
You even said it.
The margins in the paragraph,the piece of paper.
It's written on the title of thebook, the name of this chapter.

Speaker 2 (49:25):
The dust on the spine of the book and where it's at
on the shelf, in which roomYou're exactly right, those all
are interesting and you can pullthose out right.

Speaker 1 (49:35):
I can contextualize my knowns of my past experiences
to carry me forward, right.
So, rather than learning thelesson of okay, if I ever run
into that situation again, youknow I'm not going to do that,
or I'm going to go to this rightaway, or next time I go to a
call and I see that same, youknow a woman in a mental health
episode doing something I knowI'm going to have to kill her.

(49:58):
It's like no, no, no, no, no,hang on Wait a minute.
That's.
That's not what we mean, andI'm oversimplifying it in the
sense of people go well, yeah,that's not really what happens,
and I'm saying, yes, it is whathappens.
That's actually what happens iswe don't learn those lessons
because I don't unpack my knownsand so the better I get at
articulating all of my pastexperiences and what occurred

(50:21):
and the decisions that went intoit, so meaning I'm getting this
from, from taking everythingwe're talking about, and rather
than trying to say, all right,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 ateverything as a game.
It's like well, hang on.
Before we get to that, go back.
Go back to your own personalexperiences in your life.

(50:43):
Now I can start to pull apartwho the players were, what were
the conditions that were set?

Speaker 2 (50:48):
What conditions did?
I set when were the rulesUnintentionally.

Speaker 1 (50:52):
Did I unintentionally put myself in a position of
disadvantage and go?

Speaker 2 (50:58):
holy shit.

Speaker 1 (50:58):
What were the mistakes I made, what were those
indicators that I should haverecognized earlier and been able
to draw a reasonable conclusion?
Because I went through it and Iwent damn it.
I knew that was going to happen.
So that allows me that fasterand better intervention strategy
because I'm seeing thingssooner, the recognition happens
faster.

Speaker 2 (51:17):
So spot on, and that's such an important detail.
Again, folks mark this part andgo back and listen to it again.
What Brian just told you isthat when we talk about knowns
and unknowns, we have a knownwhere there's your definition, a
high likelihood that it's goingto occur, based on any
artifacts and evidence that I'mwitnessing, and you have an

(51:38):
unknown where there's a lowlikelihood okay which means that
if it's a low likelihood and itcomes to surface, it's going to
come out like a jack-in-the-boxand you're going to have to
respond to it rather than beingprepared for it.
So the game is to give yourselfenough time and distance to
balance high and low likelihoodand then the likelihood goes

(52:01):
into most likely course ofaction or most deadly course of
action, dangerous course ofaction.
If you can do that for yourentire life, then you're going
to be fine, like you can't loseweight with those Zempik and
pills and not manage your dietand your workout.
That's unsustainable.
So what you have to do is youhave to say I can't just get

(52:21):
through my police careerlearning how to use my gun and
my baton and my lesson lethal Alot of what you do.
And this is Brian.
You'll remember this argument.
I think it was 2006 in the fall, when we were all at the back
of the tomato cannery thatbecame the IIT.
Yeah, but you know what?
It was one of you.
You know what I'm trying to say.

(52:41):
It was one of you and we werehaving the argument and General
Amos was there and the argumentcame up about Greg.
I've watched three back-to-backscenarios and they've all been
non-kin, non-kinetic.
And it's like, yes, sir.
And he's like, well, I didn'tpay all this effing money to
watch a non-kin scenario.
And it's like, yeah, but, sir,it's a game.

(53:10):
That's how your brain learns.
Your brain learns just as muchfrom watching the normal daily
baseline activity that's goingon, because then an anomaly
becomes immediately apparent andyou know what?
There was a gap for so long andthey go holy shit.
And then look at all thematerial started coming out
about non-kin, non-kin villageand all that other stuff.
Now I love to say that I'm thestart of everything.
You know the sun coming up andthis, and that because it's my
massive ego.
But I'll tell you what thosearguments were going around out
there and people didn't listen.

(53:31):
And now take a look at the timethat you have in a simulator.
Somebody's going to go.
Well, if I'm going to spendtime in a simulator, it better
be a gosh damn shootout, itbetter be there and I better
feel a recoil and I better getshot once in a while so I can
put on a thing.
Trust me, those things aregoing to happen in your own life
You'll do fine.
But if you handle the mentalportion and that means

(53:55):
overcoming uncertainty byanticipating likely outcomes,
that's the game.
The whole game is which move isthis person going to make next?
And I anticipate three or fourthat I know.
Then there's a couple that Idon't know yet.
But if I see the pattern form,then I can think that that
pattern is suggestive of alikely outcome.
Oh my God, I mean that rightthere.

(54:16):
That should have been a bookand not because it came from me.
All I'm doing is I'm like agosh, damn serenity.
I can only shit out what Ilearned, and so I just learned
this stuff.
You get what I'm saying andit's what I know better than
anything else.
And that's why we have toreassess what we do in training,
because training on the mat, ofcourse that's important.
Training on the range, ofcourse that's important.

(54:37):
But if you don't have an equalor greater amount of cognitive
decision-making, of sense-makingand of in extremis, uh, a
critical decision-making, brian,then when the time comes and
it's you and the spotlightshines on you, you might not
sing, you might not dance, youmight freeze up and and that's a
uh, uh could be a detrimentaloutcome.
That could be a shitty day foreverybody involved.

(54:59):
I uh because because, in yourline of work, the failure to act
may be just as bad as youacting in the wrong manner.

Speaker 1 (55:07):
Right, right and and um, you know, you're, you're,
you're bringing up some, somevery relevant examples and
showing it from, uh, the idea ofhow, how do I sort of account
for this plan, for this getbetter at it.
Um, because that's that's thewhole point with all of this.
And to your point of you know,I only shit out what I learned.
This plan for this, get betterat it.
Um, because that's that's thewhole point with all this.
And to your point of you know,I only shit out what I learned.

(55:28):
That's that's.
You know.
I have the same thing.
Look, I could always tellpeople.

Speaker 2 (55:31):
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 it's a little bitdifferent they're convinced um
yeah, well, and, and that's thethe another point of talking
about these subjects is theseare problems and tales as old as

(55:52):
time yes these are nothing new.

Speaker 1 (55:56):
But we, as humans, what do we do?
Well, there's got to be sometechnology.
You know and, and you know, yes, when, when, whoever invented
the wheel that was revolutionary?
Okay, uh, the, the printingpress, um, industrialization,
you know the, the internalcombustion engine.
You can rattle off some thingsthat have that, that are
revolutionary, but most aren'tlike most things.

(56:19):
Don't do that.
You don't need somerevolutionary solution or
technological application.
You need to go back to focusingon the knowns.
Well, what do we know?
That's consistently worked inevery situation across time,
ageless, timeless.
Come on.
So that's part of the.

(56:41):
So what is why we likediscussing this stuff?
because it's like take a stepback.
It's time and distance.
We're coming up with solutionsto problems that don't exist, or
we're coming up with solutionsto problems that are the wrong
solution because we haven'tclearly defined what the problem
is.

Speaker 2 (56:57):
Or are so sporadic that they're likely never to
occur, never going to happenagain.
Happen once and let's put allour money there.

Speaker 1 (57:05):
And that goes back to we gotta have on whenever his
book comes out.
I know you've seen it onLinkedIn my cousin Pat he deals
with.

Speaker 2 (57:13):
Oh, what a high thinker, by the way.

Speaker 1 (57:16):
That's his.
He does kind of like a lot ofthis type of gaming and
strategic level gaming andplanning and his background and
he's a harvard guy but then alsoyou know some intelligence
community and government workand stuff like that.
Right, he is a consulting firmwhere he does this.
But that was the funny pointwas you know some of the stories
from immediately following 9-11.

(57:38):
You know he's in with thisgroup of people and you know the
, the generals, and they'regoing.
Okay, I mean I'm talking likeimmediately following 9-11, like
like there's still smoke andthey're going like okay, we
gotta start thinking outside thebox.
We gotta look at everything.
What other buildings do wethink they're?
Gonna hit and it's like rightoh my god, it's like no, no, no,
no, no, sir, that that ship is.

(57:59):
That ship has sailed uh, it'sthe next problem but, but, um,
it would just remind me of thatexample.
But yeah, um we, we, we wentover a lot.
So I kind of want to read someof this stuff and and what I'll
do too for for our patreonsubscribers I'll kind of give
you some outlines and some stufffrom this that that we, we

(58:20):
discussed, and with some of theinformation, so, so you can
always go on there.
But it goes back to thebeginning thing.
I want to debunk this myth ofanything can happen.
I think that really has abigger effect than we realize
when people start doing that,and it there's always, sure, a
meteor strike, you knowsomething random, a black Swan

(58:42):
event, but you know, you can goback after those things and look
and go well, here were all ofthe pre-event indicators, but
because it was so rare or unique, that's a major contributing
factor to why no one saw itcoming.
But but most things, 99 of thethings you see in life, are
never going to be like that.
So human behavior, you know,while complex, it follows

(59:05):
patterns.
It can be understood and it canbe anticipated, especially,
especially greg, and this is why, you know, I bring this in in
comparison to like differenteconomic models or when you get
into this with, with with marketsystems and game theory, like
with when the clear, the moreclearly you define the context
of the situation, the lesspotential outcomes there are if

(59:27):
you're looking at, and the lessambiguous it is exactly, exactly
if you're trying to see wherethe market's going to be at in
six years from now, witheverything, that's really really
difficult to do and I think alot of people that are good at
that.
Um, a lot of that is luck andsome unique intuitive or inside
knowledge.
And I don't even mean like atan illegal, like insider, I mean

(59:48):
they, they have a unique, youknow insight that they don't
even realize that they have, andthat's why they're getting good
at making those decisions.
But you know most things.
Especially, the clearer youdefine the context, the the less
complex it becomes and a greatway to do this is is what we
talk about with games, and gamesreflect life.
You know there's fundamentalaspects of games that help us

(01:00:10):
comprehend and understandstrategic interactions in real
life.
It's all about looking at games, game theory and decision
making applying the game theory.
It allows us to anticipateother actions, plan strategies
accordingly the Bayesianthinking.
That is what allows us toenhance our predictions and what
we talk about all the time inthis podcast.

(01:00:30):
What we do for a living, whatwe train, is the HBPRNA is the
goal is reducing the uncertainty, reduce the ambiguity, reduce
the complexity.
So I can.
I can have a make a more uh, a,a more informed uh decision
sooner and and be more confidentin my decisions right.

(01:00:52):
Because I have a leg to stand on, I can, I can show my work, I
can, I can, I can show theteacher, you know my work.
To get to the to, to get to theanswer, and and maybe, even if
the answer was maybe not thebest answer, it's better than
rolling the dice, right?
I mean the other thing is iswhat?

Speaker 2 (01:01:11):
That's what informed means you know that you're going
to be wrong sometime, but guesswhat?
You're much more informed thanthe person that's constantly
just guessing.

Speaker 1 (01:01:19):
You, because you're going to be right more than
you're wrong.

Speaker 2 (01:01:21):
And when you are wrong, you're going to know.

Speaker 1 (01:01:23):
You're going to be able to know where it went wrong
and we didn't even really getinto, obviously, like, what does
a correct decision or successlook like?
Because that's defined a numberof things, because you're not
looking for the right answer,you're looking for, like, the
best answer, given the availableinformation that I have.

Speaker 2 (01:01:42):
And at least know what right looks like.
So I know that I was on theright path, you know, so you're
exactly right.

Speaker 1 (01:01:51):
I would encourage everyone to kind of think about
this stuff and use it in youreveryday life, in the most
simple interactions with youknow family, friends, whatever
and try to become more aware, ormindful, as Greg would say, of
the different factors andcharacters that are at play and
the different roles and thedifferent rules that govern your

(01:02:13):
everyday social interactions.
There's, there's things in waysthat are more appropriate to to
you know, to say or do at aloud bar at midnight on a Friday
versus you know, the church onSunday morning.
So think about those.
So, greg, I'll throw you forsort of a final thought.

Speaker 2 (01:02:32):
Real quick one, brian .
So we talked about Bayesiantheory and probability theory
and game theory, and so thehardest opponents to anything
are always our law enforcementsubject matter experts, because
you learn one way and then thewalls start going.
And now all of a sudden you gota silo.
So I tell you this all of asudden you got that younger
copper and you just called off apursuit and they show up at

(01:02:52):
roll call and they're like hey,this is bullshit.
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 him, you go didyou check the oil on that car
today?
Did you check the tire pressureon that car?
Is that the car that you'vedriven through all of the time?
When was the last time you'retraining?
The idea is that we makedecisions and we think that it's
in the best interest.

(01:03:12):
If we don't go back andunderstand Bayes and probability
and game theory, then what'shappening is we're riding for a
fall because we're allowing allthat hubris to get up ahead of
us and we're not crystal clearin our thinking.
So making a better decision andsometimes no is the right
decision is much better thangetting into that

(01:03:33):
roll-of-the-dice mentality andsaying, look, I can beat the
house.
I know I can beat the housethis time, because that's
dangerous thinking.
So all HPPRNA is reducinguncertainty in the most austere
and complex environments in aspeed that you should feel
comfortable with.
Meaning, with training you'llget faster.

Speaker 1 (01:03:55):
Yeah, and that's, the house always wins right.
Physics, math, mother nature isalways going, gonna, gonna win
time in the, in the.

Speaker 2 (01:04:06):
Yeah, that, that's what's gonna come on right.

Speaker 1 (01:04:07):
It's people who go oh we gotta, we gotta, save the
environment.
No, we, we.
The environment's gonna be finewhether we're here or not.
It has a way of fixing things.
No, it's good.

Speaker 2 (01:04:18):
It's good like we're the ones we need to fix, so do
governments externally brian, sodo governments, so do diseases,
so does everything else.
So we need to fix.
So do governments.
Brian so do governments, so dodiseases, so does everything
else.
So we need to look at that.
And all of those followscientific theories.
So we're more interested inbuilding your mental acumen than
your speed of returning yourweapon to the holster or doing a

(01:04:39):
tactical relook.
So if you're looking for thatstuff, we're probably not your
game.

Speaker 1 (01:04:43):
Yeah, that's true, all right, Well, that was a lot,
so we appreciate everyonelistening, especially if you've
made it all the way to the end.

Speaker 2 (01:04:52):
Holy smokes.

Speaker 1 (01:04:53):
Please check out our Patreon page and always reach
out to us.
The Human Behavior Podcast atgmailcom More than happy to
answer questions and get intostuff on here.
We do that all the time for ourPatreon subscribers, who we do
love and appreciate, and we getsome good ones.
We get them to come on sometimeand bring their expertise as
well.
Shout out to Scott Kirshnerfrom.

Speaker 2 (01:05:12):
Kirshner's fun.
He's great.

Speaker 1 (01:05:16):
And I love the name of his company because there's a
lot of meaning behind it.

Speaker 2 (01:05:19):
I believe that too.

Speaker 1 (01:05:21):
We thank everyone for tuning in.
We appreciate it.
Share the episode with a friendif you enjoyed it and um, don't
forget that training changesbehavior.