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(intro music) My name is Laurie Santos. I teach psychology at Yale University,
and today I want to talk to you about reference dependence and loss aversion. This lecture is part of a
series on cognitive biases. Imagine that you're a doctor heading a
medical team that's trying to fight a new strain of deadly flu, one that's currently
spreading at an alarming rate. The new flu is so devastating that six
hundred million people have already been infected, and if nothing
is done, all of them will die. The good news is there are two, drugs
available to treat the disease and your team can decide which one
to put into mass production. Clinical trials show that if you go with
the first drug, drug A, you'll be able to save two hundred million
of the infected people. The second option is drug B, which has a one-third chance of saving all six hundred
million people, but a two-thirds chance that no one infected will be saved. Which drug do you pick? You probably thought drug
A was the best one. After all, with drug A, two hundred
million people will be saved for sure, which is a pretty good outcome. But now imagine that your team is faced
with a slightly different choice. This time, it's between drug C and drug D. If you choose drug C, four
hundred million infected people will die for sure. If you choose drug D, there's a one-third chance that no one infected will die, and a
two-thirds chance that six hundred million infected people will die. Which drug do you choose in this case? I bet you probably wen with drug D. After all, a chance that no one will
die seems like a pretty good bet. If you picked drug A in the first scenario and drug D in the second, you're not alone. When behavioral economists Danny Kahneman and Amos Tversky gave these
scenarios to college students, seventy-two percent of people said
that drug A was better than B, and seventy-eight percent of people
said that drug D was better than C. But let's take a slightly different
look at both sets of outcomes. In fact, let's depicted both choices in terms of the number of people
who will live and die. Here's your first choice. Drug A will save two hundred million
people for sure, and for drug B, there's a one-third chance that all six hundred million
infected people will be saved and a two-thirds chance that no
one infected will be saved. And now, let's do the same
thing for drugs C and D. Surprisingly, you can now see
that the two options are identical. Drugs A and C will save two hundred
million people, while four hundred million people are certain to die. And with both drug B and drug D, you
have a one-third chance of saving all six hundred million people and a
two-thirds chance of saving no one. We can argue about whether it's better to
save two hundred million people for sure, or to take a one-third chance
of saving all of them. But one thing should be clear from the example: it's pretty weird for you to
prefer drug A over B at the same time as you prefer drug D over C. After all, they're exactly the same drugs
with slightly different labels. Why does a simple change
in wording change our judgments about exactly the same options? Kahneman and Tversky figured out that this
strange effect results from two classic biases that affect human choice, biases known as "reference
dependence" and "loss aversion." "Reference dependence" just refers the
fact that we think about our decisions not in terms of absolutes, but relative
to some status quo or baseline. This is why, when you find
a dollar on the ground, you don't think about that dollar
as part of your entire net worth. Instead, you think in terms
of the change that the dollar made your status quo. You think, "Hey, I'm one dollar richer!" because of reference dependence, you
don't think of the options presented earlier in terms of the absolute number of lives saved. Instead, you frame each choice
relative to some status quo. And that's why the wording matters. The first scenario is described in terms of the number of life saved. That's your reference point. You're thinking in terms of how many
additional lives you can save. And in the second, you think relative to how many less lives you can lose. And that second part, worrying about losing lives, leads to the second bias that's
affecting your choices: loss aversion. Loss aversion is our reluctance to
make choices that lead to losses. We don't like losing stuff, whether
it's money, or lives, or even candy. We have an instinct to avoid
potential losses at all costs. Economists have found that
loss aversion causes us to do a bunch of irrational stuff. Loss aversion causes people to
hold onto property that's losing in value in the housing market, just because they don't want to sell
their assets at a loss. Loss aversion also leads people to
invest more poorly, even avoid risky stocks that overall will do well, because we're afraid of a small probability of losses. Loss aversion causes to latch onto the fact that drugs C and D involve losing lives. Our aversion to any potential losses causes us to avoid drug C and to go with drug D, which is the chance of not losing anyone. Our loss aversion isn't as activated when we hear about drugs A and B. Both of them involve saving people,
so why not go with the safe option, drug A over drug B? Merely describing the outcomes differently changes which scenarios
we find more aversive. If losses are mentioned, we want to reduce them as much as possible, so much so, that we take on a bit
more risk than we usually like So describing the decision one
way, as opposed to another, can cause us to make a
completely different choice. even in a life-or-death decision like this, we're at the mercy of our
minds interpret information. And how our minds interpret information
is at the mercy of our cognitive biases. Subtitles by the Amara.org community