3 ways to make better decisions -- by thinking like a computer | Tom Griffiths

The Challenge of Finding a Home in Sydney

This section introduces the problem of finding a place to buy or rent in Sydney and highlights the difficulty of not missing out on the best options.

The Cruel Problem of Finding the Best Place

• To maximize the probability of finding the best place, it is recommended to look at 37% of what's on the market and then make an offer on the next place that is better than anything seen so far.

• Setting a time frame, such as 11 days, can also be effective in determining when to act.

Optimal Stopping Problem

• Finding a place to live is an example of an optimal stopping problem.

• Optimal stopping problems have been extensively studied by mathematicians and computer scientists.

Applying Computer Science to Human Decision-Making

The speaker discusses their background as a computational cognitive scientist and how they apply computer science principles to understand human decision-making.

Computational Structure of Everyday Problems

• Understanding how human minds work involves considering the computational structure of everyday problems.

• Comparing ideal solutions with actual behavior helps uncover insights into decision-making processes.

Leveraging Computer Science for Easier Decision-Making

• Applying computer science principles can simplify human decision-making.

• By studying computational problems, we can gain valuable insights into improving decision-making processes.

The Explore-Exploit Trade-off in Decision-Making

The explore-exploit trade-off is introduced as a fundamental aspect of decision-making and its relevance in various scenarios.

Explore-Exploit Trade-off

• The explore-exploit trade-off arises when choosing between trying something new (exploring) or sticking with what is already known to be good (exploiting).

• This trade-off applies to decisions like choosing restaurants, spending time with people, and even technology companies deciding which ads to show.

Insights from Computer Science

• Computer scientists have made progress in understanding the explore-exploit trade-off.

• The decision of whether to explore or exploit depends on factors such as the duration of stay in a location.

Applying Computational Thinking to Choosing Restaurants

The speaker uses choosing a restaurant as an example to illustrate how computational thinking can guide decision-making processes.

Computational Structure of Choosing Restaurants

• Choosing a restaurant involves a computational structure where options are evaluated and decisions need to be made repeatedly.

• The explore-exploit trade-off is relevant in this context.

Factors Influencing Decision-Making

• The duration of stay in a location determines whether it is better to exploit known good options or explore new ones.

• Gathering information through exploration can improve future choices.

New Section

This section discusses the explore/exploit trade-off and how it applies to human life, from babies exploring new things to older individuals optimizing their choices based on experience.

The Explore/Exploit Trade-Off

• Babies are constantly exploring and trying new things, which is an important phase in their lives.

• Babies' exploration can lead to discovering delicious things.

• Older individuals who stick to routines and familiar choices are not boring but rather optimal.

• They exploit the knowledge gained through a lifetime of experience.

Applying the Explore/Exploit Trade-Off

• Understanding the explore/exploit trade-off can help us make decisions with less pressure.

• It's not necessary to always choose the best option; taking chances and trying something new can lead to learning experiences.

• The information gained from exploration is often more valuable than immediate rewards.

New Section

This section explores how computer science principles can be applied in everyday life, such as organizing wardrobes and filing systems.

Organizing Wardrobes

• When deciding what items to keep or give away in your wardrobe:

• Consider factors like how long you've had it, if it still functions, if it duplicates something you already own, and when was the last time you wore or used it.

• Apply the "least recently used" principle from computer memory systems. Keep accessible what you're most likely to need.

Filing Systems

• Japanese economist Yukio Noguchi devised a filing system based on the least recently used principle:

• Documents are added from the left-hand side of a cardboard box, moving existing documents along.

• Each time a document is accessed, it is put back on the left-hand side.

• This creates an order based on how recently documents were used, making it easier to find what you need.

New Section

This section highlights the organization of a messy pile of papers on a desk and how it follows the least recently used principle.

Messy Pile of Papers

• A pile of papers on a desk, often seen as disorganized, can actually be perfectly organized.

• When a paper is taken out, if it's put back on top of the pile, the papers are ordered from top to bottom based on how recently they were used.

The transcript provided does not contain any timestamps beyond this point.

Organizing Problems with Computer Science

In this section, the speaker discusses how computer science can offer strategies and solace when faced with difficult problems. The best algorithms focus on efficiency and finding solutions in the least amount of time. Computers simplify hard problems by breaking them down into simpler ones through randomness, removing constraints, or allowing approximations. This approach helps gain insight into harder problems and often leads to effective solutions.

Computer Science Strategies for Hard Problems

• Sometimes we encounter very challenging problems that require solving.

• Computer science provides strategies and solace in such cases.

• The best algorithms prioritize efficiency and finding optimal solutions quickly.

• Computers tackle hard problems by simplifying them into smaller, more manageable ones.

• Understanding these strategies can help us relax when making decisions.

Taking Chances and Embracing Imperfection

• It is impossible to consider all options when making decisions.

• Even following the optimal strategy does not guarantee a perfect outcome.

• Applying the 37 percent rule as an example, taking chances is necessary.

• Failure is common, but it's the best we can do sometimes.

• Computer science teaches us to be forgiving of our limitations.

Rationality in Decision-Making

• We cannot control outcomes; we can only control processes.

• Being rational means accepting that settling for pretty good solutions or not considering all options are valid choices.

The transcript is already in English language.