Evidence map›Paper›PMID 41884609›Full record

ArticleSynthese2026

AI-assisted rational decision-making.

Daniel Villiger

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Daniel VilligerInstitute of Philosophy, University of Zurich, Zollikerstrasse 117, Zürich, 8008 Switzerland.ORCID 0000-0003-0851-624X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AIHard choicesParityRational choiceTransformative experience

Identifiers

PMID41884609
PMCPMC13009003

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.