3 Ways to Make Better Decisions – By Thinking Like a Computer: Tom Griffiths (Transcript)

Full transcript of Psychology professor Tom Griffiths’ talk on: 3 Ways to Make Better Decisions — By Thinking Like a Computer @ TED conference.


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Tom Griffiths – Professor of Psychology and Cognitive Science

If there’s one city in the world where it’s hard to find a place to buy or rent, it’s Sydney. And if you’ve tried to find a home here recently, you’re familiar with the problem.

Every time you walk into an open house, you get some information about what’s out there and what’s on the market. But every time you walk out, you’re running the risk of the very best place passing you by.

So how do you know when to switch from looking to being ready to make an offer?

This is such a cruel and familiar problem that it might come as a surprise that it has a simple solution: 37%. If you want to maximize the probability that you find the very best place, you should look at 37% of what’s on the market, and then make an offer on the next place you see, which is better than anything that you’ve seen so far.

Or, if you’re looking for a month, take 37% of that time – 11 days, to set a standard – and then you’re ready to act.

We know this, because trying to find a place to live is an example of an optimal stopping problem – a class of problems that has been studied extensively by mathematicians and computer scientists.

I’m a computational cognitive scientist. I spend my time trying to understand how it is that human minds work, from our amazing successes to our dismal failures. To do that, I think about the computational structure of the problems that arise in everyday life, and compare the ideal solutions to those problems to the way that we actually behave.

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As a side effect, I get to see how applying a little bit of computer science can make human decision-making easier. I have a personal motivation for this.

Growing up in Perth as an overly cerebral kid, I would always try and act in the way that I thought was rational, reasoning through every decision, trying to figure out the very best action to take. But this is an approach that doesn’t scale up when you start to run into the sorts of problems that arise in adult life.

At one point, I even tried to break up with my girlfriend, because trying to take into account her preferences as well as my own and then find perfect solutions was just leaving me exhausted.

She pointed out that I was taking the wrong approach to solving this problem — and she later became my wife.

Whether it’s as basic as trying to decide what restaurant to go to, or as important as trying to decide who to spend the rest of your life with, human lives are filled with computational problems that are just too hard to solve by applying sheer effort.

For those problems, it’s worth consulting the experts: computer scientists. When you’re looking for life advice, computer scientists probably aren’t the first people you think to talk to. Living life like a computer — stereotypically deterministic, exhaustive and exact — doesn’t sound like a lot of fun.

But thinking about the computer science of human decisions reveals that in fact, we’ve got this backwards. When applied to the sorts of difficult problems that arise in human lives, the way that computers actually solve those problems looks a lot more like the way that people really act.

Take the example of trying to decide what restaurant to go to. This is a problem that has a particular computational structure. You’ve got a set of options, you’re going to choose one of those options, and you’re going to face exactly the same decision tomorrow.

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In that situation, you run up against what computer scientists call the “explore-exploit trade-off.” You have to make a decision about whether you’re going to try something new — exploring, gathering some information that you might be able to use in the future — or whether you’re going to go to a place that you already know is pretty good — exploiting the information that you’ve already gathered so far.

The explore/exploit trade-off shows up any time you have to choose between trying something new and going with something that you already know is pretty good, whether it’s listening to music or trying to decide who you’re going to spend time with.

It’s also the problem that technology companies face when they’re trying to do something, like decide what ad to show on a web page. Should they show a new ad and learn something about it, or should they show you an ad that they already know there’s a good chance you’re going to click on?

Over the last 60 years, computer scientists have made a lot of progress understanding the explore/exploit trade-off, and their results offer some surprising insights.

When you’re trying to decide what restaurant to go to, the first question you should ask yourself is how much longer you’re going to be in town. If you’re just going to be there for a short time, then you should exploit. There’s no point gathering information. Just go to a place you already know is good.

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