We tend to think of deciding whether to commit to a partner, or where to go out for dinner, as uniquely and innately human problems. The message of the book is simply: they are not. In fact they correspond – really precisely in some cases – to some of the fundamental problems of computer science.
Ever felt that you were so busy you spent all your time paralysed trying to figure out where to start, and couldn’t get much done? Computer scientists have a term for this – thrashing – and it’s a common reason our computers freeze up. The solution, for people as well as laptops, is to ‘work dumber’: pick something at random and finish it, without wasting time thinking about the bigger picture.
Ever wonder why people reply more if you ask them for a meeting at 2pm on Tuesday, than if you offer to talk at whatever happens to be the most convenient time in the next month? The first requires a two-second check of the calendar; the latter implicitly asks them to solve a vexing optimisation problem.
What about estimating the probability of something you can’t model, and which has never happened before? Math has got your back: the likelihood is no higher than 1 in the number of times it hasn’t happened, plus one. So if 5 people have tried a new drug and survived, the chance of the next one dying is at most 1 in 6.
Bestselling author Brian Christian studied computer science, and in the book Algorithms to Live By he’s out to find the lessons it can offer for a better life. In addition to the above he looks into when to quit your job, when to marry, the best way to sell your house, how long to spend on a difficult decision, and how much randomness to inject into your life.
In each case computer science gives us a theoretically optimal solution. In this episode we think hard about whether its models match our reality.
One genre of problems Brian explores in his book are ‘optimal stopping problems’, the canonical example of which is ‘the secretary problem’. Imagine you’re hiring a secretary, you receive n applicants, they show up in a random order, and you interview them one after another. You either have to hire that person on the spot and dismiss everybody else, or send them away and lose the option to hire them in future.
It turns out most of life can be viewed this way – a series of unique opportunities you pass by that will never be available in exactly the same way again.
So how do you attempt to hire the very best candidate in the pool? There’s a risk that you stop before you see the best, and a risk that you set your standards too high and let the best candidate pass you by.
Mathematicians of the mid-twentieth century produced the elegant solution: spend exactly one over e, or approximately 37% of your search, just establishing a baseline without hiring anyone, no matter how promising they seem. Then immediately hire the next person who’s better than anyone you’ve seen so far.
It turns out that your odds of success in this scenario are also 37%. And the optimal strategy and the odds of success are identical regardless of the size of the pool. So as n goes to infinity you still want to follow this 37% rule, and you still have a 37% chance of success. Even if you interview a million people.
But if you have the option to go back, say by apologising to the first applicant and begging them to come work with you, and you have a 50% chance of your apology being accepted, then the optimal explore percentage rises all the way to 61%.
Today’s episode focuses on Brian’s book-length exploration of how insights from computer algorithms can and can’t be applied to our everyday lives. We cover:
- Is it really important that people know these different models and try to apply them?
- What’s it like being a human confederate in the Turing test competition? What can you do to seem incredibly human?
- Is trying to detect fake social media accounts a losing battle?
- The canonical explore/exploit problem in computer science: the multi-armed bandit
- How can we characterize a computational model of what people are actually doing, and is there a rigorous way to analyse just how good their instincts actually are?
- What’s the value of cardinal information above and beyond ordinal information?
- What’s the optimal way to buy or sell a house?
- Why is information economics so important?
- The martyrdom of being a music critic
- ‘Simulated annealing’, and the best practices in optimisation
- What kind of decisions should people randomize more in life?
- Is the world more static than it used to be?
- How much time should we spend on prioritisation? When does the best solution require less precision?
- How do you predict the duration of something when you you don’t even know the scale of how long it’s going to last?
- How many heists should you go on if you have a certain fixed probability of getting arrested and having all of your assets seized?
- Are pro and con lists valuable?
- Computational kindness, and the best way to schedule meetings
- How should we approach a world of immense political polarisation?
- How would this conversation have changed if there wasn’t an audience?
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the transcript below.
The 80,000 Hours Podcast is produced by Keiran Harris.