Characterising Decision Theories with Mechanised Causal Graphs

TL;DR

This paper uses mechanised causal graphs to characterize and differentiate key decision theories, providing a taxonomy that clarifies how decisions influence beliefs and expected outcomes.

Contribution

It introduces a novel approach using mechanised causal models to systematically compare and classify major decision theories.

Findings

Mechanised causal models can effectively differentiate decision theories.

A taxonomy of decision theories is developed based on mechanised causal graphs.

The approach clarifies how decisions impact beliefs and expected utility.

Abstract

How should my own decisions affect my beliefs about the outcomes I expect to achieve? If taking a certain action makes me view myself as a certain type of person, it might affect how I think others view me, and how I view others who are similar to me. This can influence my expected utility calculations and change which action I perceive to be best. Whether and how it should is subject to debate, with contenders for how to think about it including evidential decision theory, causal decision theory, and functional decision theory. In this paper, we show that mechanised causal models can be used to characterise and differentiate the most important decision theories, and generate a taxonomy of different decision theories.