A systematic method for updating beliefs based on new evidence, derived from Bayes' theorem in probability theory. Bayesian reasoners start with a prior belief (informed by existing knowledge), observe new data, and calculate a revised posterior probability that incorporates both the prior and the evidence.
This framework is particularly powerful for strategic decisions under uncertainty because it provides a principled way to incorporate new information without overreacting to noise or anchoring too heavily on initial assumptions. Bayesian experts in multi-expert systems naturally become more calibrated as evidence accumulates across rounds.