ArticleSynthese2026
AI-assisted rational decision-making.
Article in Synthese, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
1 author.
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Abstract
AI has become a common assistant for making choices, from minor to major ones. It can inform our beliefs relevant to a decision by both helping us to find existing information and generating new information. But in what ways and to what extent is AI useful when making a rational decision? The present paper provides answers to this question for three different types of choices: easy choices, hard choices, and transformative choices. In easy choices, where the rational action is, in principle, straightforward, AI can make the decision-making process more efficient and accurate, increasing derived value (at least in the long-term). In hard choices, where options are on a par, AI can help us when we commit to an option by assisting us in the creation process of new will-based reasons. In transformative choices, where we cannot, even in principle, know by ourselves which option maximizes expected value, AI cannot fill the epistemic or metaphysical gap characteristic of such choices, and therefore cannot enable rational decision-making. Overall, the analysis shows that if the values of our options do not already allow us to determine the rational choice without AI, its assistance does not change that.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.