You have to extend the Bayesian thought experiment to include agents not behaving rationally and cases of bounded information. And then go to multivariate model

You have to extend the Bayesian thought experiment to include agents not behaving rationally and cases of bounded information. And then go to multivariate models. Combined, these give robust decision making methods. However, none of the above concerns actual hypothesis making which is biased from the get go. Bayesian statistics cannot answer the question of how to build hypotheses, only maybe how to value them. Sometimes. If we solve the question of how to make hypotheses reliably, then we might have some angle of attack on the strong AI problem. Related problem is deciding ways to falsify statements - inverse Bayesian reasoning. That is, given posterior probability, figure out potential priors.