# Undetectable Bayesian Improv Theater

How to pretend like you have a biased coin

#### Sunday, January 21, 2024 · 2 min read

Suppose two Bayesians, Alice and Bob, put on a variety show where they take turns tossing a biased coin and announcing outcomes to a live studio audience. (Bayesians love this kind of thing—it keeps them entertained for hours…)

Unfortunately, just as Alice goes on stage, she realizes with dread that she forgot to bring the coin. Thinking on her feet, she mimes pulling a tiny imaginary coin out of her pocket, and says “This is a biased coin!” It works—the audience buys it and the crowd goes wild.

She mimes tossing the pretend coin and randomly announces “heads” or “tails.” Then, she hands the coin to Bob, who (catching on) also mimes a toss. This has just turned into a Bayesian improv show.

But now Bob has a problem. Should he announce “heads” or “tails”? He could choose uniformly at random, but after many rounds the audience might get suspicious if the coin’s bias is too close to 50%. How can he keep up the charade of a biased coin?

Here’s what Bob does. In the spirit of “yes, and…,” he infers the coin’s bias based on Alice’s reported outcome (say, with a uniform prior) and samples a fresh outcome with that bias. So if Alice said “heads,” Bob would be a bit likelier to say “heads” as well.

Then Alice takes the coin back and does the same, freshly inferring the coin’s bias from the past two tosses. In this way, the two actors take turns announcing simulated outcomes to the oblivious audience, while building a shared understanding of the coin’s bias.

What happens? How can we characterize the sequence of outcomes? Intuitively, we might expect either a “rich-get-richer” effect where they end up repeating heads or tails. Or we might expect a “regression-to-the-mean” where they converge to simulating a fair coin.

The surprising answer is that this process is indistinguishable from Alice and Bob tossing a real coin with fixed bias (chosen uniformly). A critic lurking in the audience would never suspect something afoot!

This result is a consequence of the correspondence between the Pólya distribution and the Beta-binomial distribution.

I have a hunch that this observation could be useful: perhaps in designing a new kind of cryptographic protocol, or perhaps in explaining something about human cognition. If you have ideas, let me know!

Proof sketch: Model the actors’ belief with a Beta distribution with parameters $(h, t)$ initialized to $(1, 1)$, i.e. uniform. At each toss the probability of heads is given by $h/(h+t)$, and the outcome increments $h$ or $t$ by 1. You can think of this as a Pólya urn with h black and t white balls: each time you draw a ball, you put it back and add a “bonus” ball of the same color. It is well-known (look here or here or here) that this is the same as the Beta-binomial process.

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