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  • 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 percent.

  • (Laughter)

  • If you want to maximize the probability that you find the very best place,

  • you should look at 37 percent 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 percent 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.

  • 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 ...

  • (Laughter)

  • 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 --

  • (Laughter)

  • was just leaving me exhausted.

  • (Laughter)

  • She pointed out that I was taking the wrong approach

  • to solving this problem --

  • and she later became my wife.

  • (Laughter)

  • (Applause)

  • 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.

  • (Laughter)

  • 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.

  • 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.

  • But if you're going to be there for a longer time, explore.

  • Try something new, because the information you get

  • is something that can improve your choices in the future.

  • The value of information increases

  • the more opportunities you're going to have to use it.

  • This principle can give us insight

  • into the structure of a human life as well.

  • Babies don't have a reputation for being particularly rational.

  • They're always trying new things,

  • and you know, trying to stick them in their mouths.

  • But in fact, this is exactly what they should be doing.

  • They're in the explore phase of their lives,

  • and some of those things could turn out to be delicious.

  • At the other end of the spectrum,

  • the old guy who always goes to the same restaurant

  • and always eats the same thing

  • isn't boring --

  • he's optimal.

  • (Laughter)

  • He's exploiting the knowledge that he's earned

  • through a lifetime's experience.

  • More generally,

  • knowing about the explore/exploit trade-off

  • can make it a little easier for you to sort of relax and go easier on yourself

  • when you're trying to make a decision.

  • You don't have to go to the best restaurant every night.

  • Take a chance, try something new, explore.

  • You might learn something.

  • And the information that you gain

  • is going to be worth more than one pretty good dinner.

  • Computer science can also help to make it easier on us

  • in other places at home and in the office.

  • If you've ever had to tidy up your wardrobe,

  • you've run into a particularly agonizing decision:

  • you have to decide what things you're going to keep

  • and what things you're going to give away.

  • Martha Stewart turns out to have thought very hard about this --

  • (Laughter)

  • and she has some good advice.

  • She says, "Ask yourself four questions:

  • How long have I had it?

  • Does it still function?

  • Is it a duplicate of something that I already own?

  • And when was the last time I wore it or used it?"

  • But there's another group of experts

  • who perhaps thought even harder about this problem,

  • and they would say one of these questions is more important than the others.

  • Those experts?

  • The people who design the memory systems of computers.

  • Most computers have two kinds of memory systems:

  • a fast memory system,

  • like a set of memory chips that has limited capacity,

  • because those chips are expensive,

  • and a slow memory system, which is much larger.

  • In order for the computer to operate as efficiently as possible,

  • you want to make sure

  • that the pieces of information you want to access

  • are in the fast memory system,

  • so that you can get to them quickly.

  • Each time you access a piece of information,

  • it's loaded into the fast memory

  • and the computer has to decide which item it has to remove from that memory,

  • because it has limited capacity.

  • Over the years,

  • computer scientists have tried a few different strategies

  • for deciding what to remove from the fast memory.

  • They've tried things like choosing something at random

  • or applying what's called the "first-in, first-out principle,"

  • which means removing the item

  • which has been in the memory for the longest.

  • But the strategy that's most effective

  • focuses on the items which have been least recently used.

  • This says if you're going to decide to remove something from memory,

  • you should take out the thing which was last accessed the furthest in the past.

  • And there's a certain kind of logic to this.

  • If it's been a long time since you last accessed that piece of information,

  • it's probably going to be a long time

  • before you're going to need to access it again.

  • Your wardrobe is just like the computer's memory.

  • You have limited capacity,

  • and you need to try and get in there the things that you're most likely to need

  • so that you can get to them as quickly as possible.

  • Recognizing that,

  • maybe it's worth applying the least recently used principle

  • to organizing your wardrobe as well.

  • So if we go back to Martha's four questions,

  • the computer scientists would say that of these,

  • the last one is the most important.

  • This idea of organizing things

  • so that the things you are most likely to need are most accessible

  • can also be applied in your office.

  • The Japanese economist Yukio Noguchi

  • actually invented a filing system that has exactly this property.

  • He started with a cardboard box,

  • and he put his documents into the box from the left-hand side.

  • Each time he'd add a document,

  • he'd move what was in there along

  • and he'd add that document to the left-hand side of the box.

  • And each time he accessed a document, he'd take it out,

  • consult it and put it back in on the left-hand side.

  • As a result, the documents would be ordered from left to right

  • by how recently they had been used.

  • And he found he could quickly find what he was looking for

  • by starting at the left-hand side of the box

  • and working his way to the right.

  • Before you dash home and implement this filing system --

  • (Laughter)

  • it's worth recognizing that you probably already have.

  • (Laughter)

  • That pile of papers on your desk ...

  • typically maligned as messy and disorganized,

  • a pile of papers is, in fact, perfectly organized --

  • (Laughter)

  • as long as you, when you take a paper out,

  • put it back on the top of the pile,

  • then those papers are going to be ordered from top to bottom

  • by how recently they were used,

  • and you can probably quickly find what you're looking for

  • by starting at the top of the pile.

  • Organizing your wardrobe or your desk

  • are probably not the most pressing problems in your life.

  • Sometimes the problems we have to solve are simply very, very hard.

  • But even in those cases,

  • computer science can offer some strategies

  • and perhaps some solace.

  • The best algorithms are about doing what makes the most sense

  • in the least amount of time.

  • When computers face hard problems,

  • they deal with them by making them into simpler problems --

  • by making use of randomness,

  • by removing constraints or by allowing approximations.

  • Solving those simpler problems

  • can give you insight into the harder problems,

  • and sometimes produces pretty good solutions in their own right.

  • Knowing all of this has helped me to relax when I have to make decisions.

  • You could take the 37 percent rule for finding a home as an example.

  • There's no way that you can consider all of the options,

  • so you have to take a chance.

  • And even if you follow the optimal strategy,

  • you're not guaranteed a perfect outcome.

  • If you follow the 37 percent rule,