(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.
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