[Script Info]
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[Events]
Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
Dialogue: 0,0:00:00.00,0:00:05.94,Default,,0000,0000,0000,,(intro music)
Dialogue: 0,0:00:05.94,0:00:07.27,Default,,0000,0000,0000,,My name is Laurie Santos.
Dialogue: 0,0:00:07.27,0:00:11.39,Default,,0000,0000,0000,,I teach psychology at Yale University,\Nand today I want to talk to you about
Dialogue: 0,0:00:11.39,0:00:13.54,Default,,0000,0000,0000,,reference dependence and loss aversion.
Dialogue: 0,0:00:13.54,0:00:17.07,Default,,0000,0000,0000,,This lecture is part of a\Nseries on cognitive biases.
Dialogue: 0,0:00:17.07,0:00:21.43,Default,,0000,0000,0000,,Imagine that you're a doctor heading a\Nmedical team that's trying to fight a new
Dialogue: 0,0:00:21.43,0:00:25.72,Default,,0000,0000,0000,,strain of deadly flu, one that's currently\Nspreading at an alarming rate.
Dialogue: 0,0:00:25.72,0:00:30.39,Default,,0000,0000,0000,,The new flu is so devastating that six\Nhundred million people have already
Dialogue: 0,0:00:30.39,0:00:34.23,Default,,0000,0000,0000,,been infected, and if nothing\Nis done, all of them will die.
Dialogue: 0,0:00:34.23,0:00:38.54,Default,,0000,0000,0000,,The good news is there are two, drugs\Navailable to treat the disease and your
Dialogue: 0,0:00:38.54,0:00:42.14,Default,,0000,0000,0000,,team can decide which one\Nto put into mass production.
Dialogue: 0,0:00:42.14,0:00:46.96,Default,,0000,0000,0000,,Clinical trials show that if you go with\Nthe first drug, drug A, you'll be able to
Dialogue: 0,0:00:46.96,0:00:49.88,Default,,0000,0000,0000,,save two hundred million\Nof the infected people.
Dialogue: 0,0:00:49.88,0:00:52.49,Default,,0000,0000,0000,,The second option is drug B, which has a
Dialogue: 0,0:00:52.49,0:00:56.38,Default,,0000,0000,0000,,one-third chance of saving all six hundred\Nmillion people, but a two-thirds
Dialogue: 0,0:00:56.38,0:00:58.53,Default,,0000,0000,0000,,chance that no one infected will be saved.
Dialogue: 0,0:00:58.53,0:01:00.43,Default,,0000,0000,0000,,Which drug do you pick?
Dialogue: 0,0:01:00.43,0:01:03.87,Default,,0000,0000,0000,,You probably thought drug\NA was the best one.
Dialogue: 0,0:01:03.87,0:01:07.85,Default,,0000,0000,0000,,After all, with drug A, two hundred\Nmillion people will be saved for sure,
Dialogue: 0,0:01:07.85,0:01:09.55,Default,,0000,0000,0000,,which is a pretty good outcome.
Dialogue: 0,0:01:09.55,0:01:13.09,Default,,0000,0000,0000,,But now imagine that your team is faced\Nwith a slightly different choice.
Dialogue: 0,0:01:13.09,0:01:16.16,Default,,0000,0000,0000,,This time, it's between drug C and drug D.
Dialogue: 0,0:01:16.16,0:01:19.75,Default,,0000,0000,0000,,If you choose drug C, four\Nhundred million infected
Dialogue: 0,0:01:19.75,0:01:21.25,Default,,0000,0000,0000,,people will die for sure.
Dialogue: 0,0:01:21.25,0:01:24.46,Default,,0000,0000,0000,,If you choose drug D, there's a one-third chance
Dialogue: 0,0:01:24.46,0:01:28.49,Default,,0000,0000,0000,,that no one infected will die, and a\Ntwo-thirds chance that six hundred million
Dialogue: 0,0:01:28.49,0:01:29.93,Default,,0000,0000,0000,,infected people will die.
Dialogue: 0,0:01:29.93,0:01:32.40,Default,,0000,0000,0000,,Which drug do you choose in this case?
Dialogue: 0,0:01:32.40,0:01:34.92,Default,,0000,0000,0000,,I bet you probably wen with drug D.
Dialogue: 0,0:01:34.92,0:01:38.56,Default,,0000,0000,0000,,After all, a chance that no one will\Ndie seems like a pretty good bet.
Dialogue: 0,0:01:38.56,0:01:41.10,Default,,0000,0000,0000,,If you picked drug A in the first scenario
Dialogue: 0,0:01:41.10,0:01:43.69,Default,,0000,0000,0000,,and drug D in the second, you're not alone.
Dialogue: 0,0:01:43.70,0:01:45.72,Default,,0000,0000,0000,,When behavioral economists Danny Kahneman
Dialogue: 0,0:01:45.72,0:01:48.45,Default,,0000,0000,0000,,and Amos Tversky gave these\Nscenarios to college students,
Dialogue: 0,0:01:48.45,0:01:52.26,Default,,0000,0000,0000,,seventy-two percent of people said\Nthat drug A was better than B,
Dialogue: 0,0:01:52.26,0:01:56.29,Default,,0000,0000,0000,,and seventy-eight percent of people\Nsaid that drug D was better than C.
Dialogue: 0,0:01:56.29,0:02:00.57,Default,,0000,0000,0000,,But let's take a slightly different\Nlook at both sets of outcomes.
Dialogue: 0,0:02:00.57,0:02:03.68,Default,,0000,0000,0000,,In fact, let's depicted both choices in
Dialogue: 0,0:02:03.68,0:02:06.62,Default,,0000,0000,0000,,terms of the number of people\Nwho will live and die.
Dialogue: 0,0:02:06.62,0:02:08.90,Default,,0000,0000,0000,,Here's your first choice.
Dialogue: 0,0:02:08.90,0:02:13.23,Default,,0000,0000,0000,,Drug A will save two hundred million\Npeople for sure, and for drug B, there's a
Dialogue: 0,0:02:13.23,0:02:17.40,Default,,0000,0000,0000,,one-third chance that all six hundred million\Ninfected people will be saved and a
Dialogue: 0,0:02:17.40,0:02:20.16,Default,,0000,0000,0000,,two-thirds chance that no\None infected will be saved.
Dialogue: 0,0:02:20.16,0:02:24.04,Default,,0000,0000,0000,,And now, let's do the same\Nthing for drugs C and D.
Dialogue: 0,0:02:24.04,0:02:28.62,Default,,0000,0000,0000,,Surprisingly, you can now see\Nthat the two options are identical.
Dialogue: 0,0:02:28.62,0:02:32.69,Default,,0000,0000,0000,,Drugs A and C will save two hundred\Nmillion people, while four hundred million
Dialogue: 0,0:02:32.69,0:02:34.10,Default,,0000,0000,0000,,people are certain to die.
Dialogue: 0,0:02:34.10,0:02:38.22,Default,,0000,0000,0000,,And with both drug B and drug D, you\Nhave a one-third chance of saving all
Dialogue: 0,0:02:38.22,0:02:41.78,Default,,0000,0000,0000,,six hundred million people and a\Ntwo-thirds chance of saving no one.
Dialogue: 0,0:02:41.78,0:02:46.46,Default,,0000,0000,0000,,We can argue about whether it's better to\Nsave two hundred million people for sure,
Dialogue: 0,0:02:46.46,0:02:49.16,Default,,0000,0000,0000,,or to take a one-third chance\Nof saving all of them.
Dialogue: 0,0:02:49.16,0:02:50.63,Default,,0000,0000,0000,,But one thing should be clear from
Dialogue: 0,0:02:50.63,0:02:56.35,Default,,0000,0000,0000,,the example: it's pretty weird for you to\Nprefer drug A over B at the same time as
Dialogue: 0,0:02:56.35,0:02:58.07,Default,,0000,0000,0000,,you prefer drug D over C.
Dialogue: 0,0:02:58.07,0:03:02.61,Default,,0000,0000,0000,,After all, they're exactly the same drugs\Nwith slightly different labels.
Dialogue: 0,0:03:02.61,0:03:05.13,Default,,0000,0000,0000,,Why does a simple change\Nin wording change our
Dialogue: 0,0:03:05.13,0:03:07.84,Default,,0000,0000,0000,,judgments about exactly the same options?
Dialogue: 0,0:03:07.84,0:03:12.97,Default,,0000,0000,0000,,Kahneman and Tversky figured out that this\Nstrange effect results from two classic
Dialogue: 0,0:03:12.97,0:03:15.00,Default,,0000,0000,0000,,biases that affect human choice,
Dialogue: 0,0:03:15.00,0:03:18.29,Default,,0000,0000,0000,,biases known as "reference\Ndependence" and "loss aversion."
Dialogue: 0,0:03:18.29,0:03:22.44,Default,,0000,0000,0000,,"Reference dependence" just refers the\Nfact that we think about our decisions
Dialogue: 0,0:03:22.44,0:03:26.84,Default,,0000,0000,0000,,not in terms of absolutes, but relative\Nto some status quo or baseline.
Dialogue: 0,0:03:26.84,0:03:29.36,Default,,0000,0000,0000,,This is why, when you find\Na dollar on the ground,
Dialogue: 0,0:03:29.36,0:03:32.89,Default,,0000,0000,0000,,you don't think about that dollar\Nas part of your entire net worth.
Dialogue: 0,0:03:32.89,0:03:35.75,Default,,0000,0000,0000,,Instead, you think in terms\Nof the change that the dollar
Dialogue: 0,0:03:35.75,0:03:37.19,Default,,0000,0000,0000,,made your status quo.
Dialogue: 0,0:03:37.19,0:03:39.59,Default,,0000,0000,0000,,You think, "Hey, I'm one dollar richer!"
Dialogue: 0,0:03:39.59,0:03:43.84,Default,,0000,0000,0000,,because of reference dependence, you\Ndon't think of the options presented earlier
Dialogue: 0,0:03:43.84,0:03:46.37,Default,,0000,0000,0000,,in terms of the absolute number of lives saved.
Dialogue: 0,0:03:46.37,0:03:50.47,Default,,0000,0000,0000,,Instead, you frame each choice\Nrelative to some status quo.
Dialogue: 0,0:03:50.47,0:03:52.60,Default,,0000,0000,0000,,And that's why the wording matters.
Dialogue: 0,0:03:52.60,0:03:54.89,Default,,0000,0000,0000,,The first scenario is described in terms
Dialogue: 0,0:03:54.89,0:03:56.48,Default,,0000,0000,0000,,of the number of life saved.
Dialogue: 0,0:03:56.48,0:03:58.04,Default,,0000,0000,0000,,That's your reference point.
Dialogue: 0,0:03:58.04,0:04:02.09,Default,,0000,0000,0000,,You're thinking in terms of how many\Nadditional lives you can save.
Dialogue: 0,0:04:02.09,0:04:03.88,Default,,0000,0000,0000,,And in the second, you think relative
Dialogue: 0,0:04:03.88,0:04:06.51,Default,,0000,0000,0000,,to how many less lives you can lose.
Dialogue: 0,0:04:06.51,0:04:09.51,Default,,0000,0000,0000,,And that second part, worrying about losing
Dialogue: 0,0:04:09.51,0:04:15.30,Default,,0000,0000,0000,,lives, leads to the second bias that's\Naffecting your choices: loss aversion.
Dialogue: 0,0:04:15.30,0:04:19.65,Default,,0000,0000,0000,,Loss aversion is our reluctance to\Nmake choices that lead to losses.
Dialogue: 0,0:04:19.65,0:04:24.11,Default,,0000,0000,0000,,We don't like losing stuff, whether\Nit's money, or lives, or even candy.
Dialogue: 0,0:04:24.11,0:04:28.13,Default,,0000,0000,0000,,We have an instinct to avoid\Npotential losses at all costs.
Dialogue: 0,0:04:28.13,0:04:31.70,Default,,0000,0000,0000,,Economists have found that\Nloss aversion causes us to do
Dialogue: 0,0:04:31.70,0:04:33.48,Default,,0000,0000,0000,,a bunch of irrational stuff.
Dialogue: 0,0:04:33.48,0:04:37.25,Default,,0000,0000,0000,,Loss aversion causes people to\Nhold onto property that's losing in
Dialogue: 0,0:04:37.25,0:04:39.46,Default,,0000,0000,0000,,value in the housing market, just because
Dialogue: 0,0:04:39.46,0:04:41.96,Default,,0000,0000,0000,,they don't want to sell\Ntheir assets at a loss.
Dialogue: 0,0:04:41.96,0:04:46.93,Default,,0000,0000,0000,,Loss aversion also leads people to\Ninvest more poorly, even avoid risky
Dialogue: 0,0:04:46.93,0:04:49.18,Default,,0000,0000,0000,,stocks that overall will do well, because
Dialogue: 0,0:04:49.18,0:04:52.73,Default,,0000,0000,0000,,we're afraid of a small probability of losses.
Dialogue: 0,0:04:52.73,0:04:55.47,Default,,0000,0000,0000,,Loss aversion causes to latch onto the
Dialogue: 0,0:04:55.47,0:04:58.99,Default,,0000,0000,0000,,fact that drugs C and D involve losing lives.
Dialogue: 0,0:04:59.00,0:05:01.91,Default,,0000,0000,0000,,Our aversion to any potential losses causes
Dialogue: 0,0:05:01.91,0:05:04.84,Default,,0000,0000,0000,,us to avoid drug C and to go with drug D,
Dialogue: 0,0:05:04.84,0:05:07.98,Default,,0000,0000,0000,,which is the chance of not losing anyone.
Dialogue: 0,0:05:07.98,0:05:10.47,Default,,0000,0000,0000,,Our loss aversion isn't as activated
Dialogue: 0,0:05:10.47,0:05:12.38,Default,,0000,0000,0000,,when we hear about drugs A and B.
Dialogue: 0,0:05:12.38,0:05:16.69,Default,,0000,0000,0000,,Both of them involve saving people,\Nso why not go with the safe option,
Dialogue: 0,0:05:16.69,0:05:18.83,Default,,0000,0000,0000,,drug A over drug B?
Dialogue: 0,0:05:18.83,0:05:21.83,Default,,0000,0000,0000,,Merely describing the outcomes differently
Dialogue: 0,0:05:21.83,0:05:24.99,Default,,0000,0000,0000,,changes which scenarios\Nwe find more aversive.
Dialogue: 0,0:05:24.99,0:05:27.08,Default,,0000,0000,0000,,If losses are mentioned, we want to
Dialogue: 0,0:05:27.08,0:05:28.82,Default,,0000,0000,0000,,reduce them as much as possible,
Dialogue: 0,0:05:28.82,0:05:33.04,Default,,0000,0000,0000,,so much so, that we take on a bit\Nmore risk than we usually like
Dialogue: 0,0:05:33.04,0:05:36.57,Default,,0000,0000,0000,,So describing the decision one\Nway, as opposed to another,
Dialogue: 0,0:05:36.57,0:05:39.19,Default,,0000,0000,0000,,can cause us to make a\Ncompletely different choice.
Dialogue: 0,0:05:39.19,0:05:41.16,Default,,0000,0000,0000,,even in a life-or-death decision
Dialogue: 0,0:05:41.16,0:05:45.31,Default,,0000,0000,0000,,like this, we're at the mercy of our\Nminds interpret information.
Dialogue: 0,0:05:45.31,0:05:50.24,Default,,0000,0000,0000,,And how our minds interpret information\Nis at the mercy of our cognitive biases.