- 7 hours ago
Horizon uncovers the truth about how you really make decisions. Every day you make thousands of decisions, big and small, and behind all them is a powerful battle in your mind, pitting intuition against logic. This conflict affects every aspect of your life - from what you eat to what you believe, and especially to how you spend your money. And it turns out that the intuitive part of your mind is a lot more powerful than you may realise.
揭示决策的真相，展现你内心直觉的力量远超你的想象。我们每天都要做出成千上万个大大小小的决定，而每一个决定背后都隐藏着一场内心的激烈较量——直觉与逻辑的博弈。本节目将揭示你真正做决定的方式。
作为人类，我们总觉得自己很聪明。然而，我们却总是犯一些非理性且可预见的错误。这部影片以诺贝尔奖得主丹尼尔·卡尼曼的研究为基础，探讨了人类思维中直觉与逻辑的博弈——这场博弈影响着我们所有的决定，无论我们是否意识到这一点。
揭示决策的真相，展现你内心直觉的力量远超你的想象。我们每天都要做出成千上万个大大小小的决定，而每一个决定背后都隐藏着一场内心的激烈较量——直觉与逻辑的博弈。本节目将揭示你真正做决定的方式。
作为人类，我们总觉得自己很聪明。然而，我们却总是犯一些非理性且可预见的错误。这部影片以诺贝尔奖得主丹尼尔·卡尼曼的研究为基础，探讨了人类思维中直觉与逻辑的博弈——这场博弈影响着我们所有的决定，无论我们是否意识到这一点。
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TVTranscript
00:00MUSIC
00:04We like to think that as a species, we are pretty smart.
00:11We like to think we are wise, rational creatures.
00:14I think we all like to think of ourselves as Mr Spock, to some degree.
00:18You know, we make rational, conscious decisions.
00:21But we may have to think again.
00:23It's mostly delusion, and we should just wake up to that fact.
00:27Oh.
00:30In every decision you make, there's a battle in your mind
00:34between intuition and logic.
00:37It's a conflict that plays out in every aspect of your life.
00:42What you eat.
00:44What you believe.
00:47Who you fall in love with.
00:49And most powerfully, in decisions you make about money.
00:53The moment money enters the picture, the rules change.
00:58Scientists now have a new way to understand this battle in your mind.
01:03How it shapes the decisions you take.
01:06What you believe.
01:08And how it has transformed our understanding of human nature itself.
01:20What you think.
01:29And how it seems.
01:42Haveaya
01:43as one of the most influential psychologists alive today.
01:48Over the last 40 years,
01:50he's developed some extraordinary insights
01:52into the way we make decisions.
01:57I think it can't hurt to have a realistic view
02:01of human nature and of how the mind works.
02:06His insights come largely from puzzles.
02:13Take, for instance, the curious puzzle of New York cab drivers
02:17and their highly illogical working habits.
02:23Business varies according to the weather.
02:26On rainy days, everyone wants a cab.
02:29But on sunny days, like today, fares are hard to find.
02:34Logically, they should spend a lot of time driving on rainy days
02:39because it's very easy to find passengers on rainy days.
02:43And if they're going to take leisure, it should be on sunny days.
02:47But it turns out this is not what many of them do.
02:52Many do the opposite,
02:54working long hours on slow, sunny days
02:56and knocking off early when it's rainy and busy.
03:00Instead of thinking logically,
03:03the cabbies are driven by an urge
03:05to earn a set amount of cash each day,
03:08come rain or shine.
03:11Once they hit that target, they go home.
03:16They view being below the target as a loss
03:19and being above the target as a gain.
03:21And they care more about preventing the loss
03:25than about achieving the gain.
03:28So when they reach their goal on a rainy day, they stop.
03:34Which really doesn't make sense.
03:36If they were trying to maximise their income,
03:39they would take their leisure on sunny days
03:41and they would drive all day on rainy days.
03:49It was this kind of glitch in thinking
03:51that Kahneman realised could reveal something profound
03:54about the inner workings of them.
03:56Anyone want to take part in an experiment?
03:59And he began to devise a series of puzzles and questions
04:02which have become classic psychological tests.
04:08It's a simple experiment.
04:10It's a very attractive game.
04:12Don't worry, sir. Nothing's strenuous.
04:13Posing problems where you can recognise in yourself
04:16that your intuition is going the wrong way.
04:19The type of puzzle where the answer
04:22that intuitively springs to mind
04:23and that seems obvious
04:25is in fact wrong.
04:28Here is one that I think works on just about everybody.
04:33I want you to imagine a guy called Steve.
04:35You tell people that Steve, you know,
04:38is a meek and tidy soul
04:41with a passion for detail
04:43and very little interest in people.
04:46He's got a good eye for detail.
04:48And then you tell people he was drawn at random.
04:51From a census of the American population.
04:53What's the probability that he is a farmer
04:57or a librarian?
04:58So do you think it's more likely
04:59that Steve's going to end up working
05:00as a librarian or a farmer?
05:02What's he more likely to be?
05:04Uh, maybe a librarian.
05:07Librarian.
05:08Probably a librarian.
05:09A librarian.
05:10Immediately, you know,
05:12a thought pops to mind
05:13that, uh, it's a librarian
05:16because he resembled the prototype of a librarian.
05:19Probably a librarian.
05:20In fact, that's probably the wrong answer
05:23because, at least in the United States,
05:26there are 20 times as many male farmers
05:29as male librarians.
05:31Librarian.
05:32Librarian.
05:32So there are probably more meek and tidy souls,
05:35you know, who are farmers
05:37and meek and tidy souls who are librarians.
05:43This type of puzzle seemed to reveal a discrepancy
05:47between intuition and logic.
05:50Another example is...
05:51Imagine a dictionary.
05:52I'm going to pull a word out of it at random.
05:54Which is more likely
05:55that a word that you pick out at random
05:59has the letter R in the first position
06:02or has the letter R in the third position?
06:05Um, start with the letter R.
06:07OK.
06:09People think the first position
06:11because it's easier to think of examples.
06:13Start with it.
06:14First.
06:16In fact, there are nearly three times as many words
06:19with R as the third letter
06:20than words that begin with R.
06:23But that's not what our intuition tells us.
06:25So we have examples like that.
06:28Like, many of them.
06:33Kahneman's interest in human error
06:35was first sparked in the 1970s
06:37when he and his colleague Amos Tversky
06:40began looking at their own mistakes.
06:43It was all in ourselves.
06:45That is, all the mistakes that we studied
06:48were mistakes that we were prone to make.
06:50In my hand here, I've got £100.
06:53Kahneman and Tversky
06:54found a treasure trove of these puzzles.
06:57Which would you prefer?
06:58They unveiled a catalogue of human error.
07:02Would you rather go to Rome
07:04with a free breakfast?
07:05And opened a Pandora's box of mistakes.
07:08A year and a day.
07:10Uh, 25.
07:12But the really interesting thing
07:13about these mistakes
07:14is they're not accidents.
07:1675.
07:17They have a shape, a structure.
07:20I think Rome.
07:21Skewing our judgement.
07:2320.
07:24What makes them interesting
07:25is that they're not random errors,
07:27they're biases.
07:28So the difference between a bias
07:30and a random error
07:31is that a bias is predictable.
07:34It's a systematic error
07:35that is predictable.
07:38Um...
07:38Kahneman's puzzles
07:39prompt the wrong reply again...
07:42More likely.
07:43...and again...
07:44And more likely.
07:45More likely.
07:46...and again.
07:47Probably more likely.
07:48It's a pattern of human error
07:50that affects every single one of us.
07:54On their own,
07:56they may seem small.
07:58Ah.
07:59That seems to be the right drawer.
08:02But by rummaging around
08:03in our everyday mistakes...
08:05That's very odd.
08:07Kahneman started a revolution
08:09in our understanding
08:10of human thinking.
08:13A revolution so profound
08:15and far-reaching
08:16that he was awarded
08:17a Nobel Prize.
08:19So...
08:20If you want to see the medal...
08:24That's what it looks like.
08:28That's it.
08:36Psychologists have long strived
08:38to pick apart the moments
08:40when people make decisions.
08:43Much of the focus
08:44has been on our rational mind,
08:46our capacity for logic.
08:49But Kahneman
08:50saw the mind differently.
08:53He saw a much more powerful role
08:55for the other side of our minds.
08:57Intuition.
08:59And at the heart
09:00of human thinking,
09:01there's a conflict
09:02between logic and intuition
09:04that leads to mistakes.
09:07Kahneman and Tversky
09:08started this trend
09:09of seeing the mind differently.
09:13They found these decision-making illusions,
09:15these spots
09:16where our intuitions
09:17just make us decide
09:19these things
09:19that just don't make any sense.
09:21The work of Kahneman and Tversky
09:23has really been revolutionary.
09:26It kicked off
09:27a flurry of experimentation
09:29and observation
09:30to understand
09:31the meaning of these mistakes.
09:35People didn't really appreciate
09:36as recently as 40 years ago
09:39that the mind
09:40didn't really work
09:41like a computer.
09:42We thought that we were
09:44very deliberative,
09:45conscious creatures
09:46who weighed up
09:48the costs and benefits
09:49of action
09:49just like Mr. Spock would do.
09:54By now,
09:55it's a fairly coherent body of work
09:57about ways in which
10:01intuition
10:02departs from
10:03the rules of logic,
10:04if you will.
10:09and the body of evidence
10:10is growing.
10:14Some of the best clues
10:15to the working of our minds
10:16come not when we get things right,
10:18but when we get things wrong.
10:32In a corner of this
10:34otherwise peaceful campus,
10:36Professor Chris Chabrie
10:37is about to start a fight.
10:40All right, so
10:42what I want you guys to do
10:44is
10:45stay in this area over here.
10:47The two big guys grab you
10:48and sort of like
10:49start pretending to punch you,
10:51make some sound effects.
10:53All right, this looks good.
10:56All right,
10:56that seemed pretty good to me.
10:59It's part of an experiment
11:00that shows a pretty shocking mistake
11:02that any one of us could make.
11:06A mistake where you don't notice
11:08what's happening
11:08right in front of your eyes.
11:17As well as a fight,
11:18the experiment also involves
11:20a chase.
11:24It was inspired by an incident
11:25in Boston in 1995
11:27when a young police officer,
11:29Kenny Conley,
11:30was in hot pursuit
11:31of a murder suspect.
11:34It turned out
11:35that this police officer,
11:37while he was chasing the suspect,
11:38had run right past
11:39some other police officers
11:41who were beating up
11:42another suspect,
11:44which of course police officers
11:45are not supposed to do
11:46under any circumstances.
11:48When the police tried
11:49to investigate
11:50this case of police brutality,
11:52he said,
11:53I didn't see anything going on there.
11:54All I saw was the suspect
11:55I was chasing.
11:56And nobody could believe this.
11:58And he was prosecuted
11:59for perjury and obstruction
12:01of justice.
12:03Everyone was convinced
12:05that Conley was lying.
12:07We don't want you to be
12:08like closer than about...
12:09Everyone, that is,
12:10apart from Chris Chabrie.
12:14He wondered if our ability
12:16to pay attention
12:17is so limited
12:18that any one of us
12:19could run past
12:20a vicious fight
12:21without even noticing.
12:25And it's something
12:25he's putting to the test.
12:27And when you see someone
12:28jogging across
12:30the footbridge,
12:31then you should get started.
12:32Jackie, you can go.
12:37In the experiment,
12:39the subjects are asked
12:40to focus carefully
12:41on a cognitive task.
12:43They must count
12:44the number of times
12:45the runner taps her head
12:46with each hand.
12:54Would they,
12:55like the Boston police officer,
12:56be so blinded
12:57by their limited attention
12:58that they would completely
13:00fail to notice the fight?
13:05About 45 seconds
13:06or a minute
13:07into the run,
13:07there was the fight.
13:09And they could actually
13:09see the fight
13:10from a ways away.
13:12And it was about
13:1320 feet away from them
13:14when they got closest to them.
13:18The fight
13:18is right in their field of view
13:21and at least partially visible
13:22from as far back
13:23as the footbridge.
13:32It seems incredible
13:33that anyone would fail
13:34to notice something
13:35so apparently obvious.
13:40They completed
13:41the three-minute course
13:42and then we said,
13:43did you notice anything unusual?
13:45Yes.
13:46What was it?
13:47It was a fight.
13:48Sometimes they would
13:49have noticed the fight
13:50and they would say,
13:50yeah, I saw some guys fighting.
13:51But a large percentage
13:53of people said,
13:55we didn't see anything
13:55unusual at all.
13:56And when we asked them
13:58specifically about
13:59whether they saw anybody fighting,
14:00they still said no.
14:07In fact,
14:08nearly 50% of people
14:10in the experiment
14:11completely failed
14:12to notice the fight.
14:15Did you see anything
14:16unusual during the run?
14:18No.
14:19Okay.
14:20Did you see some people fighting?
14:22No.
14:26We did it at night time
14:27and we did it in the daylight.
14:30Even when we did it in daylight,
14:32many people ran right past the fight
14:34and didn't notice it at all.
14:39Did you see anything unusual
14:40during the run?
14:41Uh, no, not really.
14:42Okay.
14:43Did you see some people fighting?
14:45No.
14:46You really didn't see anyone fighting?
14:47No.
14:48Does it surprise you to,
14:49that you could have missed that?
14:50They were about 20 feet
14:51off the path?
14:52Oh.
14:53Yeah.
14:53You ran right past them.
14:54Completely missed that then.
14:56Okay.
14:58Maybe what happened to Conley
15:00was when you're really
15:01paying attention to one thing
15:02and focusing a lot
15:04of mental energy on it,
15:05you can miss things
15:06that other people are going to think
15:07are completely obvious.
15:08And in fact,
15:09that's what the jurors said
15:10after Conley's trial.
15:11They said,
15:11we couldn't believe
15:12that he could miss something like that.
15:14It didn't make any sense.
15:14He had to have been lying.
15:18It's an unsettling phenomenon
15:20called inattentional blindness
15:22that can affect us all.
15:25Some people have said things like,
15:26this shatters my faith in my own mind
15:28or now I don't know what to believe
15:29or I'm going to be confused from now on.
15:33But I'm not sure that that feeling
15:34really stays with them very long.
15:36They are going to go out
15:37from the experiment,
15:38you know,
15:38walk to the next place they're going
15:39or something like that.
15:40And they're going to have
15:41just as much inattentional blindness
15:42when they're walking down the street
15:44that afternoon.
15:45as they did before.
15:50This experiment reveals
15:52a powerful quandary
15:53about our minds.
15:56We glide through the world
15:57blissfully unaware
15:58of most of what we do
16:00and how little we really know our minds.
16:04For all its brilliance,
16:06the part of our mind
16:07we call ourselves
16:08is extremely limited.
16:12So how do we manage
16:14to navigate our way
16:15through the complexity
16:16of daily life?
16:28Every day,
16:29each one of us makes
16:30somewhere between
16:31two and ten thousand decisions.
16:38when you think about
16:39our daily lives,
16:40it's really a long, long
16:42sequence of decisions.
16:45We make decisions
16:46probably at the frequency
16:48that is close
16:49to the frequency
16:49we breathe.
16:50Every minute,
16:51every second,
16:52you're deciding
16:52where to move your legs
16:54and where to move your eyes
16:56and where to move your limbs
16:57and when you're eating a meal
16:58you're making all kinds of decisions.
17:01And yet the vast majority
17:02of these decisions
17:03we make without even realizing.
17:11It was Danny Kahneman's insight
17:13that we have two systems
17:15in the mind
17:15for making decisions.
17:18Two ways of thinking
17:20fast
17:22and slow.
17:27You know,
17:28our mind has really
17:29two ways of operating
17:30and one is sort of
17:33fast thinking,
17:34an automatic,
17:35effortless mode
17:36and that's the one
17:38we're in most of the time.
17:42This fast, automatic mode
17:44of thinking
17:44he called System One.
17:48It's powerful,
17:49effortless
17:50and responsible
17:51for most of what we do.
17:54And System One
17:55is, you know,
17:57that's what happens
17:57most of the time.
17:58You're there,
17:59the world around you
18:01provides all kinds of stimuli
18:03and you respond to them.
18:05Everything that you see
18:06and that you understand,
18:08you know,
18:09this is a tree,
18:10that's a helicopter
18:11back there,
18:12that's a Statue of Liberty.
18:13All of this
18:15visual perception,
18:16all of this
18:17comes through System One.
18:19The other mode
18:20is slow,
18:21deliberate,
18:22logical
18:23and rational.
18:25This is System Two
18:27and it's the bit
18:28you think of as you,
18:30the voice in your head.
18:32The simplest example
18:33of the two systems
18:34is really
18:352 plus 2
18:35is on one side
18:37and 17 times 24
18:38is on the other.
18:40What is 2 plus 2?
18:414.
18:414.
18:424.
18:42Fast System One
18:44is always in gear,
18:45producing instant answers.
18:48And what's 2 plus 2?
18:494.
18:49A number comes to your mind.
18:514.
18:524.
18:524.
18:53It is automatic.
18:54You do not intend
18:55for it to happen.
18:57It just happens to you.
18:58It's almost like a reflex.
18:59And what's that
19:0022 times 17?
19:02That's a good one.
19:08It's not a good one.
19:08But when we have to pay attention
19:09to a tricky problem,
19:11we engage slow
19:12but logical
19:13System Two.
19:15If you can do that
19:16in your head,
19:17you'll have to follow
19:18some rules
19:18and to do it sequentially.
19:22And that
19:23is not automatic
19:24at all.
19:24That involves work,
19:26it involves effort,
19:27it involves concentration.
19:2822 times 17.
19:34There will be
19:35physiological symptoms.
19:36Your heart rate
19:37will accelerate,
19:38your pupils will dilate,
19:39so many changes
19:40will occur
19:41while you're performing
19:42this computation.
19:453...
19:45Oh,
19:47I forgot.
19:49That's, um,
19:51is it 220
19:51and 7 times 22?
19:584...
19:583.74?
20:00Okay.
20:01And can I get you
20:02to just walk with me
20:02for a second?
20:04Okay.
20:04Who's the current...
20:05System Two
20:06may be clever
20:07but it's also slow,
20:09limited,
20:09and lazy.
20:11I live in Berkeley
20:12during summers
20:13and I walk a lot.
20:14And when I walk
20:16very fast,
20:16I cannot think.
20:17Can I get you
20:18to count backwards
20:19from 100 by 7?
20:21Sure.
20:22100, 93, 80.
20:25It's hard when you're walking.
20:26It takes up,
20:27interestingly enough,
20:28the same kind of
20:30executive function
20:32as thinking.
20:3440,
20:364,
20:374.
20:38If you are
20:40expected to do
20:41something that
20:42demands a lot of effort,
20:44you will stop,
20:45even walking.
20:4680,
20:486,
20:5051,
20:5516,
20:579,
20:582.
21:01Everything that you're
21:02aware of in your own
21:03mind is part of this
21:05slow, deliberative
21:06System Two.
21:07As far as you're
21:08concerned,
21:09it is the star
21:10of the show.
21:13Actually, I describe
21:14System Two as
21:16not the star.
21:17I describe it as
21:18a minor character
21:19who thinks he is the star
21:21because, in fact,
21:23most of what goes on
21:24in our mind
21:25is automatic.
21:26You know,
21:27it's in the domain
21:28that I call System One.
21:29System One is an old,
21:31evolved bit of our brain
21:32and it's remarkable.
21:33We couldn't survive
21:34without it
21:35because System Two
21:36would explode.
21:36If Mr Spock
21:37had to make
21:37every decision
21:38for us,
21:39it would be very
21:40slow and effortful
21:41and our heads
21:42would explode.
21:43And this vast,
21:45hidden domain
21:45is responsible
21:46for far more
21:47than you would
21:48possibly believe.
21:50Having an opinion,
21:51you have an opinion
21:52immediately,
21:53whether you like it
21:54or not,
21:54whether you like
21:55something or not,
21:56whether you're
21:56for something or not,
21:58liking someone
21:58or not liking them.
22:00That, quite often,
22:01is something
22:01you have no control over.
22:03Later,
22:03when you're asked
22:04for reasons,
22:05you will invent reasons.
22:06And a lot of what
22:07System Two does
22:08is it provides reason,
22:11it provides rationalizations,
22:12which are not necessarily
22:14the true reasons
22:15for our beliefs
22:16and our emotions
22:17and our intentions
22:19and what we do.
22:26You have two systems
22:27of thinking
22:28that steer you
22:29through life.
22:32Fast, intuitive
22:34System One
22:34that is incredibly powerful
22:36and does most
22:37of the driving.
22:40And slow,
22:41logical System Two
22:43that is clever
22:44but a little lazy.
22:47Trouble is,
22:48there's a bit
22:48of a battle
22:49between them
22:50as to which one
22:51is driving your decisions.
23:00and this is where
23:02the mistakes creep in
23:04when we use
23:05the wrong system
23:06to make a decision.
23:07I'm just going to ask you
23:08a few questions.
23:09We're interested
23:09in what you think.
23:10This question concerns
23:11this nice bottle
23:12of champagne
23:12I have here.
23:14Millicene 2005
23:15is a good year.
23:16Genuinely,
23:16nice vintage bottle.
23:19These people think
23:20These people think
23:20they're about to use
23:21slow, sensible System Two
23:23to make a rational decision
23:25about how much
23:26they would pay
23:26for a bottle of champagne.
23:29But what they don't know
23:30is that their decision
23:31will actually be taken
23:33totally without their knowledge
23:34by their hidden
23:35fast autopilot
23:37System One.
23:40And with the help
23:41of a bag of ping pong balls
23:43we can influence
23:44that decision.
23:46I've got a set
23:47of numbered balls here
23:48from 1 to 100
23:49in this bag.
23:50I'd like you to reach in
23:51and draw one out
23:52at random for me
23:52if you would.
23:54First,
23:55they've got to choose a ball.
23:56The number says 10.
23:5710?
23:5710.
23:5810.
23:59They think it's
24:00a random number
24:01but in fact it's rigged.
24:03All the balls
24:03are marked with
24:04the low number 10.
24:06This experiment
24:07is all about
24:07the thoughtless
24:09creation of habits.
24:11It's about
24:12how we make
24:12one decision
24:13and then other decisions
24:15follow it
24:16as if the first decision
24:17was actually meaningful.
24:19All right.
24:20What we do
24:21is purposefully
24:22we give people
24:23a first decision
24:24that is clearly meaningless.
24:2610.
24:2710.
24:27Okay.
24:27Would you be willing
24:28to pay 10 pounds
24:29for this nice bottle
24:29of vintage champagne?
24:31I would, yes.
24:33No.
24:33Yeah, I guess.
24:35Okay.
24:36This first decision
24:37is meaningless
24:38based as it is
24:39on a seemingly
24:40random number
24:42but what it does do
24:43is lodge
24:44the low number 10
24:45in their heads.
24:48Would you buy it
24:48for 10 pounds?
24:49Yes, I would.
24:50You would?
24:50Okay.
24:53Now for the real question
24:54where we ask them
24:55how much they'd actually
24:56pay for the champagne.
24:58What's the maximum amount
25:00you think you'd be willing
25:00to pay?
25:0120?
25:03Okay.
25:037 pounds.
25:047 pounds.
25:04Okay.
25:05Probably 10 pounds.
25:06A range of fairly low offers.
25:10But what happens
25:11if we prime people
25:12with a much higher number
25:1365 instead of 10?
25:18What does that one say?
25:1965.
25:2065.
25:20Okay.
25:2165.
25:21Okay.
25:22It says 65.
25:25How will this affect
25:26the price people
25:27are prepared to pay?
25:29What's the maximum
25:30you would be willing
25:31to pay for this bottle
25:32of champagne?
25:3340?
25:3445 pounds.
25:3545.
25:35Okay.
25:3650.
25:3840 quid?
25:39Okay.
25:3950 pounds.
25:4050 pounds?
25:41Yeah, I'd pay between
25:4250 and 80 pounds.
25:44Between 50 and 80?
25:46Logic has gone out
25:47the window.
25:50The price people
25:51are prepared to pay
25:52is influenced
25:53by nothing more
25:53than a number
25:54written on a ping-pong bowl.
25:59It suggests that
26:00when we come
26:01to make decisions
26:02we don't evaluate
26:03the decision in itself.
26:05Instead what we do
26:06is we try to look
26:07at other similar decisions
26:09we've made in the past
26:10and we take those decisions
26:12as if they were
26:13good decisions
26:14and we say to ourselves
26:15oh, I've made
26:16this decision before.
26:18Clearly I don't need
26:19to go ahead
26:19and solve this decision.
26:21Let me just use
26:21what I did before
26:22and repeat it.
26:23Maybe with some
26:24modifications.
26:28This anchoring effect
26:29comes from the conflict
26:31between our two
26:32systems of thinking.
26:36Fast System 1
26:37is a master
26:38of taking shortcuts
26:39to bring about
26:40the quickest
26:41possible decision.
26:43What happens
26:44is I ask you a question
26:45and if the question
26:47is difficult
26:48but there is
26:49a related question
26:50that is a lot
26:51that is somewhat simpler
26:53you're just going
26:54to answer
26:54the other question
26:55and not even notice.
26:58So the system
26:59does all kinds
26:59of shortcuts
27:00to feed us
27:01the information
27:01in a faster way
27:02we can make actions
27:04and the system
27:04is accepting
27:05some mistakes.
27:08we make decisions
27:10using fast System 1
27:12when we really
27:13should be using
27:13slow System 2
27:16and this is why
27:17we make the mistakes
27:18we do.
27:20Systematic mistakes
27:21known as
27:22cognitive biases.
27:27Nice day.
27:34Since Kahneman
27:35first began
27:36investigating
27:37the glitches
27:38in our thinking
27:38more than
27:40150
27:40cognitive biases
27:42have been identified.
27:44We are riddled
27:45with these
27:46systematic mistakes
27:47and they affect
27:48every aspect
27:49of our daily lives.
27:52Wikipedia has
27:53a very big list
27:54of biases
27:55and we are
27:56finding new ones
27:57all the time.
27:59One of the biases
27:59that I think
28:00is the most important
28:01is what's called
28:01the present bias focus.
28:03It's the fact
28:03that we focus on now
28:04and don't think
28:06very much
28:06about the future
28:07and that's the
28:08bias that causes
28:09things like
28:10overeating
28:10and smoking
28:11and texting
28:12and driving
28:12and having
28:13unprotected sex.
28:15Another one
28:16is called
28:17the halo effect
28:18and this is the idea
28:19that if you like
28:21somebody
28:22or an organization
28:23you're biased
28:24to think
28:25that all of its
28:25aspects are good
28:27and everything
28:27is good about it.
28:28If you dislike it
28:29everything is bad.
28:31People really
28:31are quite uncomfortable
28:32by the idea
28:34that Hitler
28:34loved children.
28:37He did.
28:38Now that doesn't
28:39make him a good person
28:40but we feel
28:42uncomfortable
28:43to see
28:44an attractive trait
28:45in a person
28:46that we consider
28:47you know
28:47the epitome of evil
28:49we're prone
28:50to think
28:50that what we like
28:51is all good
28:52and what we dislike
28:53is all bad
28:53that's a bias.
28:55Another particular
28:56favorite of mine
28:57is the bias
28:58to get attached
28:59to things
28:59that we ourselves
29:00have created.
29:02We call it
29:02the Achaia effect.
29:03Well you've got
29:04loss aversion
29:05risk aversion
29:06present bias.
29:08Spotlight effect
29:09and the spotlight
29:09effect is the idea
29:10that we think
29:11that other people
29:12pay a lot of
29:12attention to us
29:13when in fact
29:14they don't.
29:15Confirmation bias
29:17overconfidence
29:17is a big one.
29:18But what's clear
29:19is that there's
29:20lots of them.
29:21There's lots of ways
29:22for us to get
29:22things wrong.
29:23There's one way
29:24to do things right
29:25and many ways
29:26to do things wrong
29:26and we're capable
29:27of many of them.
29:35These biases
29:35explain so many things
29:37that we get wrong.
29:40Our impulsive spending,
29:43trusting the wrong people,
29:46not seeing the other
29:47person's point of view,
29:49succumbing to temptation.
29:54We are so riddled
29:56with these biases
29:57it's hard to believe
29:58we ever make
29:59a rational decision.
30:08But it's not just
30:09our everyday decisions
30:10that are affected.
30:26What happens
30:27if you're an expert,
30:29trained in making decisions
30:30that are a matter
30:31of life and death?
30:35Are you still destined
30:37to make these
30:37systematic mistakes?
30:42on the outskirts
30:44of Washington, D.C.,
30:45Horizon has been
30:46granted access
30:47to spy on the spooks.
30:54Welcome to
30:55analytic exercise
30:57number four.
30:59Former intelligence
31:00analyst Donald Kretz
31:02is running
31:03an ultra-realistic
31:04spy game.
31:06This exercise
31:07will take place
31:08in the fictitious
31:09city of Vastopolis.
31:12Taking part
31:14are a mixture
31:14of trained
31:15intelligence analysts
31:16and some novices.
31:20Due to an emerging
31:22threat,
31:24a terrorism task force
31:25has been stood up.
31:27I will be
31:28the terrorism task force
31:29lead and I have
31:30recruited all of you
31:31to be our
31:32terrorism analysts.
31:37The challenge
31:38facing the analysts
31:39is to thwart
31:40a terrorist threat
31:41against a U.S. city.
31:45The threat
31:46at this point
31:46has not been determined.
31:48It's up to you
31:49to figure out
31:49the type of terrorism
31:52and who's responsible
31:53for planning it.
31:56The analysts
31:57face a number
31:58of tasks.
31:59They must first
32:00investigate any groups
32:02who may pose a threat.
32:03Your task
32:04is to write a report.
32:06The subject
32:06in this case
32:07is the network
32:08of dread.
32:09The mayor
32:10has asked for this
32:1115 minutes from now.
32:15Just like
32:16in the real world,
32:17the analysts
32:18have access
32:19to a huge amount
32:20of data streaming in
32:21from government
32:22agencies,
32:23social media,
32:24mobile phones
32:25and emergency services.
32:31The network
32:32of dread
32:32turns out
32:33to be a well-known
32:34international terror group.
32:36They have the track record,
32:38the capability
32:39and the personnel
32:40to carry out an attack.
32:42The scenario
32:43that's emerging
32:44is a bio-terror event,
32:46meaning it's a biological
32:48terrorism attack
32:49that's going to take place
32:50against the city.
32:54If there is
32:55an emerging threat,
32:56they are the likely candidate.
32:58We need to move on
32:59to the next task.
33:02It's now 9 April.
33:04This is another request
33:05for information.
33:06This time on something
33:08or someone
33:09called the Masters of Chaos.
33:14The Masters of Chaos
33:16are a group
33:17of cyber hackers,
33:18a local bunch
33:20of misfits
33:20with no history
33:21of violence.
33:24And while the analysts
33:26continue to sift
33:27through the incoming data,
33:29behind the scenes,
33:31Kretz is watching
33:32their every move.
33:34In this room,
33:35we're able to monitor
33:35what the analysts
33:37are doing
33:37throughout the entire exercise.
33:40We have some
33:41set up a knowledge base
33:42into which we have been
33:43inserting data
33:44throughout the course
33:45of the day.
33:46Some of them are related
33:47to our terrorist threat.
33:48Many of them are not.
33:51Amidst the wealth
33:52of data
33:52on the known terror group,
33:54there's also evidence
33:55coming in
33:55of a theft
33:56at a university biology lab.
33:59And someone
34:00has hacked into
34:01the computers
34:01of a local freight firm.
34:04Each of these messages
34:05represents essentially
34:06a piece
34:06of the puzzle.
34:08But it's a puzzle
34:09that you don't have
34:11the box top, too.
34:13So you don't have
34:14the picture in advance,
34:16so you don't know
34:17what pieces go where.
34:19Furthermore,
34:19what we have
34:20is a bunch of puzzle pieces
34:21that don't even go
34:22with this puzzle.
34:25The exercise
34:26is part of a series
34:27of experiments
34:28to investigate
34:29whether expert
34:30intelligence agents
34:31are just as prone
34:32to mistakes
34:33from cognitive bias
34:34as the rest of us
34:36or whether
34:37their training
34:38and expertise
34:39makes them immune.
34:42I have a sort
34:43of insider's
34:43point of view
34:44of this problem.
34:45I worked a number
34:46of years
34:46as an intelligence analyst.
34:48The stakes
34:49are incredibly high.
34:52Mistakes
34:53can often be
34:54life and death.
34:58We roll ahead now.
34:59The date is 21 May.
35:02If the analysts
35:03are able to think
35:04rationally,
35:05they should be able
35:06to solve the puzzle.
35:08But the danger is
35:09they will fall
35:10into the trap
35:11set by Kretz
35:12and only pay attention
35:14to the established
35:15terror group,
35:16the Network of Dread.
35:19Their judgment
35:20may be clouded
35:21by a bias
35:21called confirmation bias.
35:25Confirmation bias
35:25is the most prevalent
35:26bias of all.
35:28And it's where we
35:29tend to search
35:30for information
35:31that supports
35:31what we already believe.
35:35Confirmation bias
35:36can easily lead people
35:37to ignore the evidence
35:39in front of their eyes.
35:41And Kretz
35:42is able to monitor
35:43if the bias kicks in.
35:45If we still see
35:47that they're searching
35:47for Network of Dread,
35:48that's an indication
35:49that we may have
35:51a confirmation bias
35:52operating.
35:54The Network of Dread
35:56are the big guys.
35:57They've done it before.
35:58So you would expect
36:00they'd do it again.
36:02And I think we're starting
36:03to see some biases here.
36:06Analysts desperately want
36:07to get to the correct answer.
36:09But they're affected
36:10by the same biases
36:11as the rest of us.
36:13So far,
36:14most of our analysts
36:15seem to believe
36:17that the Network of Dread
36:18is responsible
36:19for planning this attack
36:20attack, and that is
36:23completely wrong.
36:28How are we doing?
36:31It's time for the analysts
36:32to put themselves
36:33on the line
36:34and decide who
36:35the terrorists are
36:36and what they're planning.
36:40So, what do you think?
36:41It was a bioterroristic attack.
36:44I had a different theory.
36:46All right, so what's your theory?
36:47Because I may be missing
36:48something here, too.
36:49They know that the Network of Dread
36:50is a terrorist group.
36:52They know that the Masters of Chaos
36:54is a cyberhacking group.
36:56Either to the meat factory
36:58or in the water supply.
37:00Abundance of dead fish
37:01floating up in the river.
37:02The first degree of building.
37:03The Carousel of Bioterrorism.
37:04The question is,
37:05did any of the analysts
37:06manage to dig out
37:07the relevant clues
37:08and find the true threat?
37:11In this case,
37:13the actual threat
37:14is due to a cybergroup,
37:16the Masters of Chaos,
37:17who becomes increasingly radicalized
37:19throughout the scenario
37:20and decides to take out
37:22their anger on society, essentially.
37:24Who convinced them
37:25to switch from cybercrime
37:27to bioterrorism?
37:29Or did they succumb
37:30to confirmation bias
37:32and simply pin the blame
37:34on the usual suspects?
37:36Will they make that connection?
37:37Will they process that evidence
37:39and assess it accordingly?
37:41Or will their confirmation bias
37:43drive them to believe
37:44that it's a more traditional
37:46type of terrorist group?
37:47I believe that the Masters of Chaos
37:49are actually the ones behind it.
37:51It's either a threat
37:52or not a threat,
37:52but the Network of Dread.
37:54And time's up.
37:55Please go ahead
37:56and save those reports.
37:59At the end of the exercise,
38:01Kretz reveals
38:02the true identity
38:03of the terrorists.
38:05We have a priority message
38:07from City Hall.
38:09The terrorist attack
38:11was thwarted.
38:12The planned bioterrorist attack
38:14by the Masters of Chaos
38:17against Vastopoulos
38:19was thwarted.
38:20The mayor expresses his thanks
38:21for a job well done.
38:23Show of hands,
38:26who got it?
38:28Yeah.
38:30Out of 12 subjects,
38:3211 of them
38:33got the wrong answer.
38:36The only person
38:37to spot the true threat
38:38was in fact a novice.
38:41All the trained experts
38:43fell prey
38:44to confirmation bias.
38:51It is not typically the case
38:52that simply being trained
38:53as an analyst
38:55gives you the tools you need
38:57to overcome cognitive bias.
38:59You can learn techniques
39:01for memory improvement.
39:03You can learn techniques
39:04for better focus,
39:06but techniques to eliminate
39:08cognitive bias
39:09just simply don't work.
39:16And for intelligence analysts
39:18in the real world,
39:19the implications
39:21of making mistakes
39:22from these biases
39:23are drastic.
39:26Government reports
39:27and studies
39:28over the past decade or so
39:30have cited experts
39:32as believing
39:32that cognitive bias
39:34may have played a role
39:35in a number
39:36of very significant
39:37intelligence failures.
39:39And yet,
39:40it remains
39:40an understudied problem.
39:49Heads.
39:52Heads.
39:55But the area
39:57of our lives
39:57in which these
39:58systematic mistakes
39:59have the most
40:00explosive impact
40:01is in the world
40:03of money.
40:05The moment money
40:06enters the picture,
40:08the rules change.
40:11Many of us think
40:12that we're at our most
40:13rational when it comes
40:15to decisions about money.
40:17We like to think
40:18we know how to spot
40:19a bargain,
40:20to strike a good deal,
40:22sell our house
40:23at the right time,
40:24invest wisely.
40:27thinking about money
40:29the right way
40:30is one of the most
40:32challenging things
40:33for human nature.
40:36But if we're not
40:37as rational
40:38as we like to think,
40:39and there is
40:40a hidden force at work
40:41shaping our decisions,
40:42are we deluding ourselves?
40:45Money brings with it
40:46a mode of thinking.
40:48It changes the way
40:50we react to the world.
40:53When it comes to money,
40:55cognitive biases
40:56play havoc
40:57with our best intentions.
40:59There are many mistakes
41:00that people make
41:01when it comes to money.
41:16Kahneman's insight
41:17into our mistakes
41:18with money
41:19were to revolutionize
41:20our understanding
41:21of economics.
41:24It's all about
41:25a crucial difference
41:26in how we feel
41:27when we win or lose.
41:30And our readiness
41:31to take a risk.
41:33I would like to take a risk.
41:35Take a risk?
41:36Okay.
41:37Let's take a risk.
41:38Our willingness
41:39to take a gamble
41:40is very different
41:41depending on whether
41:42we're faced with a loss
41:43or a gain.
41:45Excuse me, guys.
41:46You spare two minutes
41:47to help us
41:47with a little experiment.
41:48We have to try
41:49and win as much money
41:49as you can, okay?
41:50Okay.
41:51Okay?
41:51In my hands here,
41:52I have 20 pounds.
41:54Okay?
41:54Here are two scenarios.
41:56And I'm going to give you 10.
41:57In the first case,
41:58you are given 10 pounds.
42:01That's not yours.
42:01Put it in your pocket.
42:02Take it away.
42:03Spend it on a drink
42:04on the south bank later.
42:05Okay.
42:06Okay?
42:07Okay?
42:09Then, you have to make a choice
42:10about how much more
42:12you could gain.
42:13You can either take
42:14the safe option,
42:15in which case I give you
42:16an additional five,
42:17or you can take a risk.
42:19Take a risk.
42:21I'm going to flip this coin.
42:23When it comes up heads,
42:24you win 10.
42:26But if it comes up tails,
42:28you're not going to win any more.
42:30Would you choose the safe option
42:32and get an extra five pounds?
42:34Or take a risk
42:35and maybe win an extra 10
42:37or nothing?
42:39Which is it going to be?
42:43I'd go safe.
42:45Safe five?
42:46Yeah.
42:47Take five.
42:48Take five?
42:48Yeah, man.
42:49Sure.
42:49There we go.
42:50Most people presented
42:51with this choice
42:52go for the certainty
42:53of the extra fiver.
42:54Thank you very much.
42:55All right.
42:56Told you it was easy.
42:57In a winning frame of mind,
42:58people are naturally
42:59rather cautious.
43:01That's yours too.
43:03That was it?
43:04That was it.
43:05Really?
43:05Yes.
43:06Yeah.
43:09But what about losing?
43:11Are we similarly cautious
43:12when faced with a potential loss?
43:15In my hands.
43:15I've got 20 pounds.
43:16I'm going to give that to you.
43:17That's now yours.
43:18Okay.
43:19You can put it in your handbag.
43:21This time,
43:22you're given 20 pounds.
43:26And again,
43:27you must make a choice.
43:30Would you choose to accept
43:31a safe loss of five pounds
43:33or would you take a risk?
43:36If you take a risk,
43:38I'm going to flip this coin.
43:40If it comes up heads,
43:41you don't lose anything.
43:43But if it comes up tails,
43:44then you lose 10 pounds.
43:46In fact,
43:47it's exactly the same outcome.
43:50In both cases,
43:51you face a choice
43:52between ending up
43:53with a certain 15 pounds
43:55or tossing a coin
43:56to get either 10 or 20.
43:59I will risk these
44:01and turn it up.
44:02Okay.
44:03But the crucial surprise here
44:05is that when the choice
44:06is framed in terms of a loss,
44:08most people take a risk.
44:12Take a risk?
44:13Take a risk, okay.
44:14I'll risk it.
44:15You'll risk it?
44:15Okay.
44:16Our slow system too
44:17could probably work out
44:19that the outcome
44:19is the same in both cases.
44:21And that's heads.
44:22You win.
44:22But it's too limited
44:23and too lazy.
44:25That's the easiest 20 pounds
44:26you'll ever make.
44:27Instead,
44:28fast system one
44:29makes a rough guess
44:30based on change.
44:32And that's all there is to it.
44:33Thank you very much.
44:33Wherever you want.
44:35Oh, no.
44:36And system one
44:37doesn't like losing.
44:39Woo!
44:44If you were to lose 10 pounds
44:46in the street today
44:47and then find 10 pounds tomorrow,
44:49you would be financially unchanged.
44:51But actually,
44:52we respond to changes.
44:53So the pain of the loss of 10 pounds
44:55looms much larger.
44:57It feels more painful.
44:58In fact,
44:59you probably have to find 20 pounds
45:00to offset the pain
45:02that you feel by losing 10.
45:03Heads.
45:04At the heart of this
45:05is a bias called loss aversion,
45:08which affects many of us
45:10of financial decisions.
45:12People think in terms
45:13of gains and losses.
45:16It's tails.
45:18And in their thinking,
45:20typically,
45:20losses loom larger than gains.
45:24We even have an idea
45:26by how much,
45:27by roughly a factor of two
45:29or a little more than two.
45:32That is loss aversion.
45:34And it certainly
45:35was the most important thing
45:36that emerged from our work.
45:40It's a vital insight
45:42into human behavior.
45:44So important,
45:45it led to a Nobel Prize
45:47and the founding
45:48of an entirely new branch
45:49of economics.
45:52When we think we're winning,
45:54we don't take risks.
45:57But when we're faced
45:58with a loss,
45:59frankly,
46:00we're a bit reckless.
46:11But loss aversion
46:12doesn't just affect people
46:14making casual five-pound bets.
46:17It can affect anyone
46:19at any time,
46:21including those who work
46:23in the complex system
46:24of high finance
46:25in which trillions
46:26of dollars are traded.
46:29In our current
46:31complex environment,
46:33we now have
46:34the means
46:36as well as the motive
46:37to make very serious mistakes.
46:41The bedrock of economics
46:43is that people
46:44think rationally.
46:46They calculate risks,
46:48rewards,
46:49and decide accordingly.
46:52But we're not
46:53always rational.
46:55We rarely behave
46:56like Mr. Spock.
46:59For most of our decisions,
47:01we use fast,
47:02intuitive,
47:03but occasionally unreliable
47:04system one.
47:08And in a global
47:10financial market,
47:11that can lead
47:12to very serious problems.
47:16I think what
47:17the financial crisis did
47:18was it simply said,
47:21you know what?
47:22People are a lot
47:24more vulnerable
47:26to psychological pitfalls
47:27than we really
47:29understood before.
47:31Basically,
47:32human psychology
47:33is just too flawed,
47:36to expect
47:36that we could
47:37avert a crisis.
47:46Understanding these pitfalls
47:48has led to a new branch
47:49of economics.
47:52Behavioral economics.
47:56Thanks to psychologists
47:57like Hersh Sheffrin,
47:59it's beginning to establish
48:00a toehold in Wall Street.
48:03It takes account
48:04of the way
48:05we actually make decisions,
48:06rather than
48:07how we say we do.
48:15Financial crisis,
48:16I think,
48:16was as large a problem
48:18as it was,
48:19because certain
48:20psychological traits
48:21like optimism,
48:22overconfidence,
48:23and confirmation bias
48:24played a very large role
48:26among a part of the economy
48:30where serious mistakes
48:33could be made and work.
48:41But for as long
48:43as our financial system
48:44assumes we are rational,
48:46our economy
48:47will remain vulnerable.
48:49I'm quite certain
48:51if the regulators
48:52listened to
48:53behavioral economists
48:54early on,
48:56we would have designed
48:57a very different
48:58financial system,
49:00and we wouldn't have had
49:01the incredible increase
49:03in housing market,
49:05and we wouldn't have
49:05this financial catastrophe.
49:09And so when Kahneman
49:10collected his Nobel Prize,
49:12it wasn't for psychology,
49:14it was for economics.
49:22The big question is,
49:24what can we do
49:24about these
49:25systematic mistakes?
49:28Can we hope to find
49:30a way round
49:30our fast-thinking biases
49:32and make better decisions?
49:36To answer this,
49:37we need to know
49:38the evolutionary origins
49:40of our mistakes.
49:44Just off the coast
49:45of Puerto Rico,
49:46Puerto Rico
49:46is probably the best
49:48place in the world
49:49to find out.
49:57The tiny island
49:59of Cayo Santiago.
50:03So we're now
50:04in the boat
50:04heading over
50:05to Cayo Santiago.
50:06This is an island
50:07filled with
50:08a thousand rhesus monkeys.
50:11Once you pull in,
50:12it looks a little bit
50:12like you're going
50:14to Jurassic Park.
50:15you're not sure
50:15what you're going to see.
50:16Then you'll see
50:17your first monkey
50:18and then it'll be
50:18comfortable.
50:19You're like,
50:19ah, the monkeys are here.
50:20Everything's great.
50:28It's an island
50:29devoted to monkey research.
50:32You can see the guys
50:33hanging out on the cliff
50:33up there.
50:34Pretty cool.
50:39The really special thing
50:40about Cayo Santiago
50:41is that the animals here,
50:42because they've grown up
50:43over the last seven years
50:44around humans,
50:45they're completely
50:46habituated.
50:46And that means we can
50:48get up close to them,
50:49show them stuff,
50:50look at how they make decisions.
50:51We're able to do this here
50:52in a way that we'd never
50:53be able to do it
50:54anywhere else, really.
50:55It's really unique.
51:00Lori Santos is here
51:01to find out
51:02if monkeys make
51:03the same mistakes
51:04in their decisions
51:04that we do.
51:09Most of the work
51:10we do is comparing
51:11humans and other primates,
51:12trying to ask,
51:13what's special about humans?
51:15But really,
51:15what we want to understand
51:16is what's the evolutionary
51:18origin of some of our
51:19dumber strategies,
51:20some of the spots
51:20where we get things wrong.
51:21If we can understand
51:22where those came from,
51:23that's where we'll get
51:24some insight.
51:28If Santos can show us
51:30that monkeys have
51:31the same cognitive biases
51:32as us,
51:33it would suggest
51:34they evolved a long time ago.
51:41And a mental strategy
51:43that old
51:44would be almost impossible
51:45to change.
51:47We started this work
51:49around the time
51:50of the financial collapse.
51:53So when we were thinking
51:54about what dumb strategies
51:56could we look at
51:57in monkeys,
51:58it was pretty obvious
51:59that some of the
52:00human economic strategies,
52:01which were in the news,
52:02might be the first thing
52:03to look at.
52:03And one of the particular
52:05things we wanted
52:06to look at was whether
52:07or not the monkeys
52:07are loss-averse.
52:10But monkeys,
52:11smart as they are,
52:12have yet to start
52:13using money.
52:15And so that was
52:15kind of where we started.
52:16We said,
52:17well, how can we even
52:18ask this question
52:19of if monkeys
52:20make financial mistakes?
52:21And so we decided
52:23to do it
52:23by introducing
52:24the monkeys
52:25to their own
52:25new currency
52:26and just let them
52:28buy their food.
52:30So I'll show you
52:31some of the stuff
52:31we've been up to
52:32with the monkeys.
52:35Back in her lab
52:36at Yale,
52:37she introduced
52:38a troop of monkeys
52:39to their own market,
52:40giving them round,
52:42shiny tokens
52:42they could exchange
52:43for food.
52:46So here's Holly.
52:48She comes in,
52:48hands over a token.
52:49You can see she just
52:50gets to grab the
52:51grape there.
52:53One of the first
52:54things we wondered
52:54was just can they
52:55in some sense learn
52:56that a different store
52:57sells different food
52:58at different prices?
53:00So what we did
53:01was we presented
53:02the monkeys
53:02with situations
53:03where they met
53:05traders who sold
53:06different goods
53:06at different rates.
53:08So what you'll see
53:09in this clip
53:09is the monkey's
53:10meeting a new trader.
53:11She's actually selling
53:13grapes for three grapes
53:14per one token.
53:19And what we found
53:20is that in this case
53:21the monkeys
53:21are pretty rational.
53:22So when they get a choice
53:23of a guy who sells
53:24three goods for one token,
53:26they actually shop
53:27more at that guy.
53:34Having taught the monkeys
53:35the value of money,
53:36the next step
53:37was to see if monkeys,
53:39like humans,
53:40suffer from that
53:41most crucial bias,
53:42loss aversion.
53:46And so what we did
53:47was we introduced
53:48the monkeys to traders
53:49who either gave out
53:50losses or gains
53:52relative to what
53:53they showed.
53:54So I could make
53:55the monkey think
53:56he's getting a bonus
53:58simply by having him
53:59trade with a trader
54:00who's starting
54:01with a single grape,
54:02but then when the monkey
54:03pays this trader,
54:04she actually gives him
54:04an extra,
54:05so she gives him a bonus.
54:06At the end,
54:07the monkey gets two,
54:08but he thinks he got
54:09that second one
54:09as a bonus.
54:11We can then compare
54:12what the monkeys do
54:12with that guy
54:13versus a guy
54:14who gives the monkey
54:15losses.
54:16This is a guy
54:16who shows up,
54:17who pretends he's
54:18going to sell
54:19three grapes,
54:20but then when the monkey
54:21actually pays this trader,
54:23he'll take one of the grapes
54:24away and give the monkeys
54:25only two.
54:28The big question then
54:29is how the monkeys react
54:30when faced with a choice
54:32between a loss and a gain.
54:35So she'll come in,
54:36she's met these two guys
54:37before,
54:37you can see she goes
54:38with the bonus option,
54:40even waits patiently
54:41for her additional piece
54:43to be added here
54:45and then takes the bonus,
54:47avoiding the person
54:48who gives her losses.
54:54So monkeys hate losing
54:56just as much as people.
55:05And crucially,
55:07Santos found that monkeys
55:08as well are more likely
55:10to take risks
55:11when faced with a loss.
55:17This suggests to us
55:18that the monkeys
55:18seem to frame their decisions
55:20in exactly the same way
55:22we do.
55:22They're not thinking
55:23just about the absolute,
55:25they're thinking relative
55:25to what they expect
55:27and when they're getting
55:28less than they expect,
55:29when they're getting losses,
55:30they too become
55:31more risk-seeking.
55:36The fact that we share
55:37this bias with these monkeys
55:39suggests it's an ancient strategy
55:41etched into our DNA
55:43more than 35 million years ago.
55:48And what we learn
55:49from the monkeys
55:50is that if this bias
55:52is really that old,
55:53if we really have had
55:54this strategy
55:55for the last 35 million years,
55:57simply deciding
55:58to overcome it
55:59is just not going to work.
56:00We need better ways
56:01to make ourselves
56:02avoid some of these pitfalls.
56:06Making mistakes,
56:07it seems,
56:08is just part of what it is
56:10to be human.
56:17We are stuck
56:18with our intuitive
56:19inner stranger.
56:23The challenge this poses
56:25is profound.
56:29If it's human nature
56:30to make these predictable mistakes
56:32and we can't change that,
56:34what then can we do?
56:37We need to accept ourselves
56:39as we are.
56:41The cool thing
56:42about being a human
56:43versus a monkey
56:44is that we have
56:45a deliberative self
56:46that can reflect
56:47on our biases.
56:48System 2 in us
56:49has for the first time
56:50realized there's a system 1.
56:52And with that realization,
56:54we can shape
56:55the way we set up policies,
56:58we can shape the way
56:58we set up situations
56:59to allow ourselves
57:00to make better decisions.
57:02This is the first time
57:03in evolution
57:03this has happened.
57:09If we want
57:10to avoid mistakes,
57:12we have to reshape
57:13the environment
57:14we've built around us
57:15rather than hope
57:17to change ourselves.
57:25We've achieved a lot
57:26despite all of these biases.
57:29If we are aware of them,
57:30we can probably do things
57:32like design our institutions
57:33and our regulations
57:36and our own personal environments
57:38and working lives
57:39to minimize the effect
57:42of those biases
57:43and help us think about
57:46how to overcome them.
57:52We are limited,
57:54we're not perfect,
57:55we're irrational
57:55in all kinds of ways,
57:56but we can build a world
57:58that is compatible with this
57:59and get us to make
58:00better decisions
58:01rather than worse decisions.
58:03That's my hope.
58:08And by accepting
58:09our inner stranger,
58:10we may come to
58:12a better understanding
58:13of our own minds.
58:15I think it is important
58:17in general
58:19to be aware
58:19of where beliefs come from.
58:23And if we think
58:25that we have reasons
58:27for what we believe,
58:28that is often a mistake.
58:31That our beliefs
58:33and our wishes
58:34and our hopes
58:35are not always
58:37anchored in reasons,
58:39they're anchored
58:39in something else
58:40that comes
58:41from within
58:43and is different.
58:48Next on BBC4,
58:50filtering our understanding
58:52of air
58:52with every breath
58:54we take.
58:54Stay with us.
58:56live and learn
58:56Bye.
58:57Bye.
58:57Bye.
59:07Bye.
59:11Bye.
59:12Bye.