Can CDT rationalise the ex ante optimal policy via modified anthropics?

TL;DR

This paper explores how modified anthropic reasoning and self-locating beliefs can reconcile causal decision theory with ex ante optimal policies in Newcomb-like problems, using simulation models and a novel approach called GGT.

Contribution

It introduces a framework for aligning CDT with ex ante optimality in Newcomb-like scenarios through modified beliefs and models, including the GGT approach.

Findings

CDT can be rationalised to recommend ex ante optimal policies under certain beliefs.

Simulation-based and GGT approaches can produce policies that include the ex ante optimum.

Theoretical characterisation of policies under different belief models.

Abstract

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.