This approach yields increasingly fruitful analyses of such cognitive activities as causal learning, concept learning, and planning in arbitrary environments as probabilistic inference enriched with causal/logical structure.

It's not obvious to me that the Church programming language and execution model is based on bounded rationality theory.

I mean, the idea of using MCMC to sample the executions of probabilistic programs is certainly neat, and you can trade off bias with computing time by varying the burn-in and samples lag parameters, but this trade-off is not provably optimal.

If I understand correctly, provably optimal bounded rationality is marred by unsolved theoretical questions such as the one-way functions conjecture and P != NP. Even assuming that these conjectures are true, the fact that we can't prove them implies that we can't often prove anything interesting about the optimality of many AI algorithms.