[1907.11752] Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:1907.11752 (cs) [Submitted on 26 Jul 2019 ( v1 ), last revised 28 Oct 2022 (this version, v6)] Title: Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models Authors: Mauricio Gonzalez Soto , David Danks , Hugo J. Escalante Balderas , L. Enrique Sucar View a PDF of the paper titled Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models, by Mauricio Gonzalez Soto and 3 other authors View PDF HTML (experimental) Abstract: Decision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning. Decision-making has been well studied when the available information is considered at the associative (probabilistic) level. The classical Theorems of von Neumann-Morgenstern and Savage provide a formal criterion for rational choice using associative information: maximize expected utility. There is an ongoing debate around the origin of probabilities involved in such calculation. In this work, we will show how the probabilities for decision-making can be grounded in causal models by considering decision problems in which the available actions and consequences are causally connected. In this setting, actions are regarded as an intervention over a causal model. Then, we extend a previous causal decision-making result, which relies on a known causal model, to the case in which the causal mechanism that controls some environment is unknown to a rational decision-maker. In this way, action-outcome probabilities can be grounded in causal models in known and unknown cases. Finally, as an application, we extend the well-known concept of Nash Equilibrium to the case in which the players of a strategic game consider causal information. Subjects: Artificial Intelligence (cs.AI) ; Methodology (stat.ME) Cite as: arXiv:1907.11752 [cs.AI] (or arXiv:1907.11752v6 [cs.AI] for this version) https://doi.org/10.48550/arXiv.1907.11752 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1007/s10670-025-00961-5 Focus to learn more DOI(s) linking to related resources Submission history From: Mauricio Gonzalez Soto [ view email ] [v1] Fri, 26 Jul 2019 18:44:39 UTC (19 KB) [v2] Mon, 9 Sep 2019 12:13:10 UTC (19 KB) [v3] Thu, 28 May 2020 23:18:32 UTC (44 KB) [v4] Mon, 5 Apr 2021 04:13:33 UTC (102 KB) [v5] Mon, 25 Apr 2022 14:09:34 UTC (102 KB) [v6] Fri, 28 Oct 2022 12:00:22 UTC (62 KB) Full-text links: Access Paper: View a PDF of the paper titled Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models, by Mauricio Gonzalez Soto and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2019-07 Change to browse by: cs stat stat.ME References & Citations NASA ADS Google Scholar Semantic Scholar DBLP - CS Bibliography listing | bibtex Mauricio Gonzalez-Soto Luis Enrique Sucar Hugo Jair Escalante export BibTeX citation Loading... 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