Can CDT rationalise the ex ante optimal policy via modified anthropics?
This addresses a foundational issue in decision theory for philosophers and AI researchers, but it is incremental as it builds on existing debates about CDT and EDT.
The paper tackles the problem of aligning causal decision theory (CDT) with ex ante optimal policies in Newcomblike problems by exploring self-locating beliefs, such as simulation models and a non-simulation approach called GGT, and proves that under certain conditions, CDT can recommend these optimal policies.
In Newcomb's problem, causal decision theory (CDT) recommends two-boxing and thus comes apart from evidential decision theory (EDT) and ex ante policy optimisation (which prescribe one-boxing). However, in Newcomb's problem, you should perhaps believe that with some probability you are in a simulation run by the predictor to determine whether to put a million dollars into the opaque box. If so, then causal decision theory might recommend one-boxing in order to cause the predictor to fill the opaque box. In this paper, we study generalisations of this approach. That is, we consider general Newcomblike problems and try to form reasonable self-locating beliefs under which CDT's recommendations align with an EDT-like notion of ex ante policy optimisation. We consider approaches in which we model the world as running simulations of the agent, and an approach not based on such models (which we call 'Generalised Generalised Thirding', or GGT). For each approach, we characterise the resulting CDT policies, and prove that under certain conditions, these include the ex ante optimal policies.