Post by Admin on Jan 10, 2024 14:07:14 GMT
How to think like a Bayesian
In a world of few absolutes, it pays to be able to think clearly about probabilities. These five ideas will get you started
by Michael G Titelbaum
psyche.co/guides/how-to-think-like-a-bayesian-and-make-better-decisions
You’re often asked what you believe. Do you believe in God? Do you believe in global warming? Do you believe in life after love? And you’re often told that your beliefs are central to who you are, and what you should do: ‘Do what you believe is right.’
These belief-questions demand all-or-nothing answers. But much of life is more complicated than that. You might not believe in God, but also might not be willing to rule out the existence of a deity. That’s what agnosticism is for.
For many important questions, even three options aren’t enough. Right now, I’m trying to figure out what kinds of colleges my family will be able to afford for my children. My kids’ options will depend on lots of variables: what kinds of schools will they be able to get into? What kinds of schools might be a good fit for them? If we invest our money in various ways, what kinds of return will it earn over the next two, five, or 10 years?
Suppose someone tried to help me solve this problem by saying: ‘Look, it’s really simple. Just tell me, do you believe your oldest daughter will get into the local state school, or do you believe that she won’t?’ I wouldn’t know what to say to that question. I don’t believe that she will get into the school, but I also don’t believe that she won’t. I’m perhaps slightly more confident than 50-50 that she will, but nowhere near certain.
One of the most important conceptual developments of the past few decades is the realisation that belief comes in degrees. We don’t just believe something or not: much of our thinking, and decision-making, is driven by varying levels of confidence. These confidence levels can be measured as probabilities, on a scale from zero to 100 per cent. When I invest the money I’ve saved for my children’s education, it’s an oversimplification to focus on questions like: ‘Do I believe that stocks will outperform bonds over the next decade, or not?’ I can’t possibly know that. But I can try to assign educated probability estimates to each of those possible outcomes, and balance my portfolio in light of those estimates.
We know from many years of studies that reasoning with probabilities is hard. Most of us are raised to reason in all-or-nothing terms. We’re quite capable of expressing intermediate degrees of confidence about events (quick: how confident are you that a Democrat will win the next presidential election?), but we’re very bad at reasoning with those probabilities. Over and over, studies have revealed systematic errors in ordinary people’s probabilistic thinking.
Luckily, there once lived a guy named the Reverend Thomas Bayes. His work on probability mathematics in the 18th century inspired a movement we now call Bayesian statistics. You may have heard ‘Bayesian’ talk thrown around in conversation, or mentioned in news articles. At its heart, Bayesianism is a toolkit for reasoning with probabilities. It tells you how to measure levels of confidence numerically, how to test those levels to see if they make sense, and then how to manage them over time.
In a world of few absolutes, it pays to be able to think clearly about probabilities. These five ideas will get you started
by Michael G Titelbaum
psyche.co/guides/how-to-think-like-a-bayesian-and-make-better-decisions
You’re often asked what you believe. Do you believe in God? Do you believe in global warming? Do you believe in life after love? And you’re often told that your beliefs are central to who you are, and what you should do: ‘Do what you believe is right.’
These belief-questions demand all-or-nothing answers. But much of life is more complicated than that. You might not believe in God, but also might not be willing to rule out the existence of a deity. That’s what agnosticism is for.
For many important questions, even three options aren’t enough. Right now, I’m trying to figure out what kinds of colleges my family will be able to afford for my children. My kids’ options will depend on lots of variables: what kinds of schools will they be able to get into? What kinds of schools might be a good fit for them? If we invest our money in various ways, what kinds of return will it earn over the next two, five, or 10 years?
Suppose someone tried to help me solve this problem by saying: ‘Look, it’s really simple. Just tell me, do you believe your oldest daughter will get into the local state school, or do you believe that she won’t?’ I wouldn’t know what to say to that question. I don’t believe that she will get into the school, but I also don’t believe that she won’t. I’m perhaps slightly more confident than 50-50 that she will, but nowhere near certain.
One of the most important conceptual developments of the past few decades is the realisation that belief comes in degrees. We don’t just believe something or not: much of our thinking, and decision-making, is driven by varying levels of confidence. These confidence levels can be measured as probabilities, on a scale from zero to 100 per cent. When I invest the money I’ve saved for my children’s education, it’s an oversimplification to focus on questions like: ‘Do I believe that stocks will outperform bonds over the next decade, or not?’ I can’t possibly know that. But I can try to assign educated probability estimates to each of those possible outcomes, and balance my portfolio in light of those estimates.
We know from many years of studies that reasoning with probabilities is hard. Most of us are raised to reason in all-or-nothing terms. We’re quite capable of expressing intermediate degrees of confidence about events (quick: how confident are you that a Democrat will win the next presidential election?), but we’re very bad at reasoning with those probabilities. Over and over, studies have revealed systematic errors in ordinary people’s probabilistic thinking.
Luckily, there once lived a guy named the Reverend Thomas Bayes. His work on probability mathematics in the 18th century inspired a movement we now call Bayesian statistics. You may have heard ‘Bayesian’ talk thrown around in conversation, or mentioned in news articles. At its heart, Bayesianism is a toolkit for reasoning with probabilities. It tells you how to measure levels of confidence numerically, how to test those levels to see if they make sense, and then how to manage them over time.