The Computer Science of Human Decision Making | Tom Griffiths | TEDxSydney

Summary

This talk explores the application of computer science to everyday decision-making, focusing on the '37% rule' for optimal stopping in scenarios like house hunting. It delves into the Explorer-Exploit trade-off, illustrating how to balance trying new options with sticking to known quantities. The speaker, a computational cognitive scientist, uses examples like choosing a restaurant and organizing a wardrobe to demonstrate how computer science principles can simplify complex human problems, ultimately encouraging a more forgiving approach to our own decision-making processes.

Takeaways

• 🏠 The '37 percent rule' is a strategy for making decisions when faced with a limited number of options, suggesting that one should look at 37% of the options before making a decision on the next best one.
• 🤔 The script discusses the challenges of decision-making, particularly in the context of finding a home in Sydney, and how to balance the risk of missing out on better options.
• 🧠 The presenter is a computational cognitive scientist who studies the computational structure of everyday problems and compares ideal solutions with actual human behavior.
• 💡 Applying computer science to human decision-making can simplify complex problems and provide strategies for making better-informed choices.
• 🍴 The 'Explorer-Exploit trade-off' is a concept that arises in various scenarios, such as choosing a restaurant, where one must decide between trying something new or sticking with a known preference.
• 👶 The value of information increases with the number of opportunities to use it, which is exemplified by babies exploring their environment to learn.
• 👴 Similarly, older individuals who stick to familiar routines are seen as making optimal decisions based on a lifetime of experiences.
• 👗 Computer memory principles, such as the Least Recently Used (LRU) strategy, can be applied to decluttering a wardrobe, emphasizing the importance of keeping frequently used items accessible.
• 📚 Yukio Noguchi's filing system and the concept of organizing documents by recent use can be applied to personal and office organization for efficiency.
• 🔑 The script highlights that even when using the best processes, outcomes are not guaranteed, which is a concept that can lead to a more forgiving approach to decision-making.
• 🤖 Computer science offers insights into problem-solving strategies that can help us understand and accept the limitations of human decision-making processes.

Q & A

What is the 37% rule mentioned in the script?

The 37% rule is a strategy to maximize the probability of finding the best option when faced with a series of choices. It suggests that you should look at 37% of the available options without making a decision, then choose the first option that is better than all the ones you've seen so far.

What is an optimal stopping problem?

An optimal stopping problem is a type of problem in decision theory where the decision-maker must choose the best time to stop information gathering and make a decision based on the information already obtained.

How does the script relate the concept of the Explorer-Exploit trade-off to everyday life?

The script relates the Explorer-Exploit trade-off to everyday life by illustrating it with the example of choosing a restaurant. It's the dilemma of whether to try something new (explore) or to stick with something known to be good (exploit).

What is the personal motivation behind the speaker's interest in computational cognitive science?

The speaker's personal motivation stems from growing up as an overly cerebral kid in Perth, always trying to act rationally and reason through every decision, which led to exhaustion and failure in some aspects of life, including personal relationships.

How does the script suggest we approach decision-making in life?

The script suggests that we should consult experts like computer scientists when faced with computational problems that are too hard to solve by applying sheer effort. It also encourages taking chances, exploring new options, and being forgiving of our own limitations.

What is the Explorer-Exploit trade-off in the context of technology companies?

For technology companies, the Explorer-Exploit trade-off is the decision between showing a new ad to learn about its effectiveness or showing an ad that is already known to perform well, thus exploiting the existing knowledge.

How does the value of information increase according to the script?

The value of information increases the more opportunities you have to use it. For example, if you are going to be in town for a longer time, it's worth exploring new options because the information gained can improve future choices.

What principle does the script suggest for tidying up a wardrobe?

The script suggests applying the least recently used principle, which is a strategy often used in computer memory systems, to decide what items to keep or give away in a wardrobe.

How does Yukio Noguchi's filing system work?

Yukio Noguchi's filing system works by placing documents in a box from the left-hand side and moving existing documents along each time a new document is added. When a document is accessed, it is put back on the left side, resulting in an order from left to right by how recently they've been used.

What is the significance of the 37% rule in the context of finding a home?

The 37% rule in the context of finding a home suggests that after viewing 37% of the available properties, you should make an offer on the next property that is better than any of the ones you've seen so far, with the aim of maximizing the probability of finding the best home.

How can computer science help in making decisions in difficult problems?

Computer science can help by breaking down difficult problems into simpler ones, making use of randomness, removing constraints, or allowing approximations. Solving these simpler problems can provide insight into the harder problems and sometimes produce good solutions.

Outlines

00:00

🏠 Optimal Stopping in Housing Search

The script discusses the challenge of finding a home in Sydney, using the '37% rule' as a strategy to maximize the probability of finding the best place. It explains that after examining 37% of the available options, one should make an offer on the next better option that comes along. This approach is rooted in the field of optimal stopping problems, which has been extensively studied by mathematicians and computer scientists. The speaker, a computational cognitive scientist, uses this rule as an example of applying computer science to everyday life decisions, emphasizing the importance of balancing exploration and exploitation in decision-making.

05:01

👶 The Explorer-Exploit Trade-off in Life Decisions

This paragraph explores the concept of the Explorer-Exploit trade-off, a dilemma faced when deciding between trying something new (exploration) or sticking with what is known to be good (exploitation). It uses the example of choosing a restaurant and how this trade-off is present throughout life, from infancy to old age. The speaker suggests that understanding this trade-off can help ease decision-making pressure, as it's not necessary to make the best choice every time. The paragraph also touches on how computer science principles, such as the least recently used (LRU) strategy for memory management, can be applied to everyday tasks like tidying a wardrobe, illustrating the practicality of computer science in simplifying complex decisions.

10:03

🛠️ Applying Computer Science to Life's Challenges

The final paragraph emphasizes the value of computer science in tackling difficult life problems by breaking them down into simpler ones, using randomness, or allowing for approximations. It highlights the 37% rule for home buying as an example of an optimal strategy that does not guarantee perfect outcomes but offers the best possible chance. The speaker reflects on the importance of process over outcomes and the acceptance of 'good enough' solutions, suggesting that computer science can provide strategies for dealing with life's complexities and making peace with our limitations.

Keywords

💡Optimal stopping problem

An optimal stopping problem is a challenge in decision theory where the decision-maker must choose the best time to stop information gathering and make a decision based on the information obtained so far. In the video, this concept is applied to the difficulty of finding a home in Sydney, suggesting that after evaluating 37% of the options, one should make an offer on the next best option, as this maximizes the probability of finding the best home.

💡37% rule

The 37% rule is a specific strategy for solving the optimal stopping problem. It suggests that one should sample the first 37% of the options and then make a decision based on the best option found thereafter. The video uses this rule to illustrate how to make a decision when looking for a home, implying that after seeing 37% of the available properties, one should make an offer on the next better option they encounter.

💡Computational cognitive scientist

A computational cognitive scientist is a professional who applies computational methods to understand the human mind, including both its successes and failures. In the video, the speaker identifies as such, aiming to compare ideal solutions to everyday problems with how people actually behave, which helps in understanding the computational structure of human decision-making.

💡Explorer-exploit trade-off

The explorer-exploit trade-off is a fundamental concept in decision-making that involves choosing between exploring new options (which may yield better long-term results) and exploiting current knowledge to make immediate gains. The video discusses this trade-off in the context of choosing a restaurant, suggesting that the decision depends on how long one plans to stay in a place and the value of information gathered.

💡Fast and slow memory systems

Fast and slow memory systems refer to the different types of memory in computing, where fast memory is quicker to access but more expensive, while slow memory is larger but slower. The video compares these to human memory and decision-making, particularly in the context of tidying up a wardrobe, suggesting that the least recently used items should be discarded, mirroring how computers manage memory.

💡Least recently used (LRU) principle

The least recently used (LRU) principle is a strategy used in computer memory management to determine which data to remove from fast memory when it is full. According to this principle, the data that has been accessed least recently should be removed first. The video suggests applying this principle to organizing a wardrobe, emphasizing the importance of keeping the most frequently used items most accessible.

💡Martha Stewart

Martha Stewart is mentioned in the video as an example of someone who has thought about the process of tidying up and making decisions about what to keep or discard. She is known for her advice on home organization and lifestyle, and her method of asking four questions to decide what to keep or give away from one's wardrobe is referenced in the script.

💡Yukio Noguchi

Yukio Noguchi is a Japanese economist who invented a filing system based on the LRU principle. The video describes his method of organizing documents in a box from left to right based on how recently they were used, allowing for efficient retrieval of documents. This system is used as an example of applying computer science principles to everyday life.

💡Approximations

Approximations in computer science refer to solutions that may not be exact but are close enough to be useful, especially when dealing with complex problems. The video mentions that computers deal with hard problems by simplifying them, using randomness, or allowing for approximations, which can provide insight into the problems and sometimes yield satisfactory solutions.

💡Rational decision-making

Rational decision-making is the process of making choices based on logic and reason, aiming for the most efficient or effective outcome. The video discusses the limitations of trying to be perfectly rational in all situations, suggesting that sometimes being rational involves taking chances, not considering all options, or settling for a 'pretty good' solution.

Highlights

Sydney's housing market is notoriously difficult for finding a place to buy or rent.

The '37% rule' is suggested for maximizing the probability of finding the best home.

An optimal stopping problem is a mathematical concept used to decide when to make a decision.

The speaker is a computational cognitive scientist studying human decision-making processes.

Applying computer science to everyday problems can simplify human decision-making.

The speaker's personal experience with rational decision-making and its limitations.

The Explorer-Exploit trade-off in decision-making involves choosing between new experiences and known outcomes.

The value of information increases with more opportunities to use it.

Babies and the elderly exemplify the explore and exploit phases of life respectively.

The least recently used (LRU) principle can be applied to organizing a wardrobe.

Martha Stewart's advice on decluttering and the importance of the least recently used item.

Computer memory systems use the LRU principle to optimize information retrieval.

Yukio Noguchi's filing system organizes documents by recent use.

A pile of papers on a desk can be organized by recent use, making it easy to find documents.

Computer science offers strategies for dealing with hard problems by simplifying, randomizing, or approximating.

The 37% rule in home buying does not guarantee a perfect outcome but offers the best probability.

Computer science can help us be more forgiving of our limitations in decision-making.

Rational decision-making sometimes means taking chances and settling for good enough solutions.

Transcripts

[Music]

if there's one city in the world where

it's hard to find a place to buy or rent

it's Sydney if you try 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

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 for 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

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 despair 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 as

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 Explorer 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

Explorer exploit trade-off shows up

anytime 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 webpage

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 pretty good chance you're going to

click on over the last 60 years computer

scientists have made a lot of progress

understanding the Explorer 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 there

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

he's exploiting the knowledge that he's

earned through a lifetime's experience

more generally knowing about the

Explorer 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 very 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 but to

make it easier on us and other places at

home and in the office if you've ever

had to tidy up your wardrobe you 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

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 that 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 try and make sure that the

pieces of information that 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

what you should take out is 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 access that piece of

information it's probably gonna 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 want

that you're 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 for

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're 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'd been used and he

found that he could very 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

- how him and implement this filing

system

it's worth recognizing that you probably

already have that pile of papers on your

desk

typically maligned as messy and

disorganized a pile of papers is in fact

perfectly organized as long as you when

you take a paper out and put it back on

the top of the the pile then those

papers are going to be ordered from top

to bottom by how recently they were used

and you can probably pretty 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 that 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

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 the

probability that you find the very best

place is funnily enough 37 percent you

fail most of the time but that's the

best that you can do ultimately computer

science can help to make us more

forgiving of our own limitations you

can't control outcomes just processes

and as long as you've used the best

process you've done the best that you

can sometimes those best processes

involve taking a chance not considering

all of your options or being willing to

settle for a pretty good solution these

are the concessions that we make when we

turn irrational they're what being

rational means thank you

[Applause]

[Music]