This is a linkpost for https://kevindorst.substack.com/p/bayesians-commit-the-gamblers-fallacy

TLDR: Rational people who start out uncertain about an (in fact independent) causal process and then learn from unbiased data will rule out “streaky” hypotheses more quickly than “switchy” hypotheses. As a result, they’ll commit the gambler's fallacy: expecting the process to switch more than it will. In fact, they’ll do so in ways that match a variety of empirical findings about how real people commit the gambler's fallacy. Maybe it's not a fallacy, after all.

(This post is based on a full paper.)

Baylee is bored. The fluorescent lights hum. The spreadsheets blur. She needs air.

As she steps outside, she sees the Prius nestled happily in the front spot. Three days in a row now—the Prius is on a streak. The Jeep will probably get it tomorrow, she thinks.

This parking battle—between a Prius and a Jeep—has been going on for months. [...]

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Outline:

(02:49) Asymmetric Convergence

(08:16) Empirical Predictions

(08:31) Nonlinear Expectations

(10:10) Experience-Dependence

(12:13) Other results

(13:15) So What?

(16:17) What next?

The original text contained 5 footnotes which were omitted from this narration.

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