I recently realized that the formalism of incomplete models provides a rather natural solution to all decision theory problems involving "Omega" (something that predicts the agent's decisions). An incomplete hypothesis may be thought of a zero-sum game between the agent and an imaginary opponent (we will call the opponent "Murphy" as in Murphy's law). If we assume that the agent cannot randomize against Omega, we need to use the deterministic version of the formalism. That is, an agent that learns an incomplete hypothesis converges to the corresponding max
Yeah, I agree that the objective descriptions can leave out vital information, such as how the information you know was acquired, which seems important for determining the counterfactuals.