Translator: Ivana Krivokuća Reviewer: Carlos Arturo Morales
How to make good decisions?
If you open a book on rational choice,
you will likely read the following message:
look before you leap, analyze before you act.
List all alternatives, all the consequences,
and estimate the utilities and do the calculation.
This is a beautiful mathematical scheme,
but it doesn't describe how most people actually make decisions.
And not even how those who write these books make decisions,
as the following story illustrates.
A professor from Columbia University had an offer from a rival university,
and he could not make up his mind whether to accept, reject, go or stay.
A colleague took him aside and said, "What's the problem?
Just maximize your expected utility! You always write about doing this!"
Exasperated, the professor responded, "Come on, this is serious."
(Laughter)
The method of listing pros and cons and doing the calculation is an old one.
Benjamin Franklin once in a letter to his nephew recommended exactly that.
Listing all pros and cons, and then weighting and adding.
And at the end of his letter, he wrote the following:
"If you do not learn it, I apprehend you will never get married."
(Laughter)
Did you choose your partner by a calculation?
I asked my friends who teach this method as the only method of rational choice
how they chose their partner if they had any choice at all.
(Laughter)
All of them said, "No, no, no."
There was one exception.
And he told me that he applied his own theory.
He explained to me that he listed all the alternatives,
all the consequences.
For instance, will she still talk to me after being married?
Will she take care of the kids and let me work in peace?
And then he estimated for each of these women
the probability that it will actually occur.
And multiplied it by the utilities and made the calculation.
Then he proposed to the woman with the highest expected utility.
She accepted.
He never told her how he had chosen her.
(Laughter)
I've met him recently. Now they are divorced.
I will talk today about two ways of making decisions.
One is the one that is taught at the academia;
it has many names:
Benjamin Franklin's bookkeeping method or expected utility theory.
And this is a method that works in a world of risk,
that is, when the probabilities,
the consequences and alternatives are known.
Here, statistical thinking is enough.
A key example is lotteries or if you play the casino.
Then you can calculate how much you will lose.
In a different world, the world of uncertainty,
calculation is not enough.
You need smart rules of thumb, that are technically called heuristics,
and good intuitions, which are also based on smart rules of thumb.
I will talk today
about decision making under uncertainty
and also about the dangers
of using systems that work for known risk
and applying them to the world of uncertainty.
Choices between two jobs or between partners
are all in the world of uncertainty.
You can't calculate everything, you don't know the consequences,
and there will be surprises.
I will make four points
and then illustrate them with two examples.
First, the best decision under risk
is not the best decision under uncertainty.
Second, heuristics that you need
in order to make good decisions in the uncertainty
are indispensable for good decision making.
They are not, as it's often claimed,
a sign of a kind of mental retardation
or of just mental laziness.
Third, complex problems
do not always require complex solutions,
and that's again in the world of uncertainty.
And finally, more information,
more calculation, more time is not always better.
Less can be more.
Let's go to the first example.
This is sports.
How does an outfielder catch a flying ball?
In baseball, in cricket,
or maybe in soccer, where the goalie has to get it.
How does he or she know where to run?
There are two theories about that.
One is it's a complex problem, you need complex mental processes,
and the other one is it's a complex problem under uncertainty,
and you need to find a simple method for that.
Let's look for the first one.
Richard Dawkins, in his famous book "The Selfish Genes,"
proposed the complex method.
So what does the outfielder do? He or she calculates the trajectory.
Have you ever calculated a trajectory?
(Laughter)
Okay, that's what you do?
(Laughter)
And this formula doesn't even have wind in it or spin, so it's not enough.
But what else could it be?
What you see here is the idea to apply
a theory that works if you know everything,
like under known risk, to the world of uncertainty.
The key idea is that you say, "Oh, he behaves 'as if' -"
and Dawkins puts in the "as if" -
the player would calculate that.
What's the alternative? How do real players catch a ball?
That's my question.
And a number of experiments show
that real players use a number of simple heuristics.
I'll show you one.
This one works when the ball is already high up in the air.
It's called the gaze heuristic.
It has three steps.
First, fixate your eye on the ball, start running,
and finally, adjust the running speed
so that the angle of gaze remains constant.
This player here does exactly that.
He runs so that the angle of gaze remains constant,
and that brings him there where the ball will land.
Do you want to see it again? Here it is.
Importantly: the player can ignore to estimate or calculate
every variable that's necessary to estimate the trajectory.
Every one.
It's a heuristic that belongs to a family of heuristics
that just looks at one good reason.
And then you get there.
You'll find the same heuristics in evolutionary history,
so birds and fish,
when they hunt a prey or a mate -
which is sometimes not so different -
they just keep the optical angle constant in three-dimensional space,
and that's enough.
No trajectory correlations.
This heuristic is used by players unconsciously.
If you have ever interviewed a player
and asked him how is he doing this so well, then you get "intuition."
And it's intuition,
meaning the person knows what to do, but doesn't know why.
But how does this intuition function?
As you see here, it functions by simple rules.
The same rule can be used deliberately.
Every rule that we studied can be used deliberately,
and that's very different from what you might hear
in some claims about decision making
that think that heuristics are unconscious,
and statistical thinking is conscious.
Don't believe that.
Here's an example.
Remember the miracle of the Hudson River?
What had happened?
A plane hit shortly after takeoff
a formation of Canadian geese.
They flew in both engines and silenced both engines.
The pilots turned around
to see whether they could get back to La Guardia Airport,
or they would have to do something more risky, like the Hudson River.
How did they make this decision?
Did they do calculations? They didn't have much time.
They used the same heuristic, now deliberately, the gaze heuristic.
How does it work in this case?
You fixate the tower through your windshield -
that's what the pilots did -
and if the tower is slowly moving upwards, you won't make it.
And that's exactly what Jeffrey Skiles, the co-pilot, is saying, in other words.
Here is another instance where heuristics can help us to make a safer world,
and the decisions are done very fast.
My second illustration is the world of finance.
We know now that the theory of finance
is part of the problem,
or was part of the problem of the financial crisis, not its solution.
Why? Because it's a theory about known risk,
and it's applied to the world of uncertainty,
and suggests certainties that are illusiory.
Calculations of value, of risk, and that sort of things.
I'll give you one example.
Assume you want to invest money,
and you don't want to put everything in one basket,
but you want to diversify.
But how?
Harry Markowitz, from the University of Chicago,
got his Nobel Prize for finding the solution.
When Harry Markowitz made his own investments
for the time after his retirement,
he used his Nobel Prize-winning optimization method.
So we might think. No, he did not.
He relied on a simple heuristic that we call 1/N.
You divide your assets equally.
For instance, if you have just two alternatives,
you divide your money 50-50, and so on.
The interesting question is how good is this simple heuristic
that doesn't need much calculation in the real world of investment,
as opposed to the theory of investment?
A study by DeMiguel et al. looked at that
and gave the complex Nobel Prize-winning method
10 years of data to estimate its parameters,
and then to estimate what's happening the following months.
The window wasn't shifted until there was no data left.
What was the result?
According to common measures, 1/N made more money
than the Nobel Prize-winning mean-variance model.
The interesting question is now
not just to show that something simpler does something better,
but the real question is, can we identify the world
where simplicity pays or where the complex calculation pays?
I'll show you here three features of this world
where 1/N outperforms mean-variance,
or at least very likely outperforms mean-variance.
One feature is predictive uncertainty is large - that's the case with stocks.
The second one is the number of alternatives is large,
and you can see this because the complex methods
need to estimate more parameters,
1/N not,
and then generate more errors.
And finally, the learning sample is small; it was 10 years.
Now one can ask the following question:
if I would have 50 alternatives, how many years of data do I need
so that mean-variance actually gets better than 1/N?
What do you think?
Ten years is too little. Eleven? Twelve?
The best estimate is 500 years.
So in the year 2500,
we can switch from our intuitions, 1/N,
to doing the calculations,
provided that the same stocks are still around in the stock market
in the first place.
Do our banks understand that? No.
I recently got a letter from my internet bank which said:
"With Nobel Prize-winning strategy to success in investment."
And then I read,
"Do you know Harry Markowitz? No? You should know him."
And then a story was told
that he won the Nobel Prize for solving the problem
and the bank has now adopted his method,
and there was a warning about people's intuitions.
What this bank has not understood is
that they sent the letter 500 years too early.
(Laughter)
This is my second illustration
about the power of simple rules in an uncertain world,
and also about the damage that can happen
when you rely on methods that work very well in risk,
but apply them blindly to real world.
Ask your own bank what they use.
That this is not just a one-shot is shown by this slide.
There are 20 studies, and what you see here -
we have an optimization model that's widely used multiple regression,
and we have three heuristics.
The minimalist is too simple. It just picks something randomly.
The other two heuristics have a different philosophy.
You already know 1/N, you just throw the weights away and do it equally.
One good reason
is a heuristic that goes with the first good reason that it can find,
and then it's there.
I'm not going into details, they are all mathematically studied,
and what you see here is something important.
When you know already all the data,
that's called fitting,
then the complex model is the best one.
So you can, you're flexible enough, and you explain the hindsight.
When you have to predict, then something interesting happens;
it's a crossover.
Every one of the simplifications is more accurate,
not just more frugal and faster.
This condition is like hindsight.
For instance, I hear often on the radio
a financial adviser being asked
why did Microsoft go down yesterday,
and he has always an answer.
This is hindsight.
If you would be asked if Microsoft is going up or down tomorrow,
that would be prediction.
And prediction is hard.
That's an illustration.
Let me get the general picture.
What we're studying at the Max Planck Institute
is how do people and should people make decisions under uncertainty,
and the first question is a descriptive one:
what's in the adaptive toolbox?
There are many more heuristics that I can't tell you today,
and lots of social heuristics.
So, people trust their doctor,
and the study of ecological rationality asks the question:
in what situation is this a good idea and when not?
If your doctor knows the medical evidence,
has no conflicts of interest, and doesn't do defensive decision making -
he has fear that you might turn into a plaintiff -
that's a good idea.
But that's not the case in most countries.
Most doctors - we have studies - don't know the evidence,
they have conflicts of interest, and they do defensive decision making.
And finally, how to create situations, environments, and also strategies
that are intuitive and that help people make better decisions?
Let me finish.
Decision making under uncertainty is different
from decision making under risk,
and heuristics are not the second best strategies, that we often hear.
They can do better than even optimization strategies
in a world of uncertainty, not in a world of risk.
And finally, more information, more time, and more computation
is not always better.
Less can be more.
Thank you for your attention.
(Applause)