Characterising Decision Theories with Mechanised Causal Graphs
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 32 references that are in the map. First posted on arXiv 20 Jul 2023.
Builds on
8 of 32 references.
References · 8
- 2018Scalable agent alignment via reward modeling: a research directioncited by 438
- 2018AI safety via debatecited by 384
- 2022Is Power-Seeking AI an Existential Risk?cited by 101
- 2021Agent Incentives: A Causal Perspectivecited by 51
- 2020Hidden Incentives for Auto-Induced Distributional Shiftcited by 42
- 2022Discovering Agentscited by 28
- 2023Reasoning about Causality in Gamescited by 20
- 2017Functional Decision Theory: A New Theory of Instrumental Rationalitycited by 6
Built on by
Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.
- 2024Measuring Goal-Directednesscited by 7
- 2024Attaining Human`s Desirable Outcomes in Human-AI Interaction via Structural Causal Gamescited by 1
- 2026A causal modeling perspective on decision theorycited by 0
- 2026A causal modeling perspective on decision theory29 Jun 2026
- 2024Measuring Goal-Directedness6 Dec 2024
- 2024Attaining Human`s Desirable Outcomes in Human-AI Interaction via Structural Causal Games26 May 2024