Human adaptation to adaptive machines converges to game-theoretic equilibria.

Benjamin J Chasnov, Lillian J Ratliff, Samuel A Burden

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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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.

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Benjamin J ChasnovDepartment of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA.

Lillian J RatliffDepartment of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA.

Samuel A BurdenDepartment of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA. sburden@uw.edu.

Funding

Undergraduate and graduate training in neural computation and engineeringT90DA032436 · NIDA · UNIVERSITY OF WASHINGTON · PI FAIRHALL, ADRIENNE L, SHEA-BROWN, ERIC TODD · 2011 to 2020

$1.5M

National Science Foundation 1836819National Science Foundation 2045014NIDA NIH HHS T90 DA032436NIH HHS 5T90DA032436-09 MPI

6 · The paper itself

Abstract

Here we test three learning algorithms for machines playing general-sum games with human subjects. The algorithms enable the machine to select the outcome of the co-adaptive interaction from a constellation of game-theoretic equilibria in action and policy spaces. Importantly, the machine learning algorithms work directly from observations of human actions without solving an inverse problem to estimate the human's utility function as in prior work. Surprisingly, one algorithm can steer the human-machine interaction to the machine's optimum, effectively controlling the human's actions even while the human responds optimally to their perceived cost landscape. Our results show that game theory can be used to predict and design outcomes of co-adaptive interactions between intelligent humans and machines.

Indexed as

Game TheoryMachine LearningAlgorithmsHumansgame theoryhuman-AI interactionmulti-agent systems

Identifiers

PMID40790315

DOI10.1038/s41598-025-12998-1

PMCPMC12340018

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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Registered trials

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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.