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Four agents are observing infinite streams of outcomes in {\{S,F\}}. None of them knows the future outcomes and as good Bayesianists they represent their beliefs about unknowns as probability distributions:

  • Agent 1 believes that outcomes are i.i.d. with probability {1/2} of success.
  • Agent 2 believes that outcomes are i.i.d. with probability {\theta} of success. She does not know {\theta}; She believes that {\theta} is either {2/3} or {1/3}, and attaches probability {1/2} to each possibility.
  • Agent 3 believes that outcomes follow a markov process: every day’s outcome equals yesterday’s outcome with probability {3/4}.
  • Agent 4 believes that outcomes follow a markov process: every day’s outcome equals yesterday’s outcome with probability {\theta}. She does not know {\theta}; Her belief about {\theta} is the uniform distribution over {[0,1]}.

I denote by {\mu_1,\dots,\mu_4\in\Delta\left(\{S,F\}^\mathbb{N}\right)} the agents’ beliefs about future outcomes.

We have an intuition that Agents 2 and 4 are in a different situations from Agents 1 and 3, in the sense that are uncertain about some fundamental properties of the stochastic process they are facing. I will say that they have `structural uncertainty’. The purpose of this post is to formalize this intuition. More explicitly, I am looking for a property of a belief {\mu} over {\Omega} that will distinguish between beliefs that reflect some structural uncertainty and beliefs that don’t. This property is ergodicity.

 

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I will have more to say about the Stony Brook conference, but first a word about David Blackwell, who passed away last week. We game theorists know Blackwell for several seminal contributions. Blackwell’s approachability theorem is at the heart of Aumann and Maschler’s result about repeated games with incomplete information which Eilon mentions below, and also of the calibration results which I mentioned in my presentation in Stony Brook (Alas, I was too nervous and forgot to mention Blackwell as I intended too). Blackwell’s theory of comparison of experiments has been influential in the game-theoretic study of value of information, and Olivier presented a two-person game analogue for Blackwell’s theorem in his talk. Another seminal contribution of Blackwell, together with Lester Dubins, is the theorem about merging of opinions, which is the major tool in the Ehuds’ theory of Bayesian learning in repeated games. And then there are his contributions to the theory of infinite games with Borel payoffs (now known as Blackwell games) and Blackwell and Fergurson’s solution to the Big Match game.

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