Unforeseen Evidence
This addresses a foundational issue in decision theory and AI for agents that expand their awareness, offering a theoretical framework for belief updating in such scenarios.
The paper tackles the problem of updating beliefs when an agent becomes aware of new possibilities, proposing extended Bayesianism as a normative rule that generalizes standard Bayesian updating to allow posteriors on richer probability spaces than priors, and provides an observable criterion for consistency with this rule.
I propose a normative updating rule, extended Bayesianism, for the incorporation of probabilistic information arising from the process of becoming more aware. Extended Bayesianism generalizes standard Bayesian updating to allow the posterior to reside on richer probability space than the prior. I then provide an observable criterion on prior and posterior beliefs such that they were consistent with extended Bayesianism.