 Perceptual interventions ameliorate statistical discrimination in learning agentsEdgar A. Duéñez-Guzmán (),
Ramona Comanescu,
Yiran Mao,
Kevin R. McKee,
Ben Coppin,
Suzanne Sadedin,
Silvia Chiappa,
Alexander S. Vezhnevets,
Michiel A. Bakker,
Yoram Bachrach,
William Isaac,
Karl Tuyls and
Joel Z. Leibo
Proceedings of the National Academy of Sciences, 2025, vol. 122, issue 25, e2319933121 Abstract: Choosing social partners is a potentially demanding task which involves paying attention to the right information while disregarding salient but possibly irrelevant features. The resultant trade-off between cost of evaluation and quality of decisions can lead to undesired bias. Information-processing abilities mediate this trade-off, where individuals with higher ability choose better partners leading to higher performance. By altering the salience of features, technology can modulate the effect of information-processing limits, potentially increasing or decreasing undesired biases. Here, we use game theory and multiagent reinforcement learning to investigate how undesired biases emerge, and how a technological layer (in the form of a perceptual intervention) between individuals and their environment can ameliorate such biases. Our results show that a perceptual intervention designed to increase the salience of outcome-relevant features can reduce bias in agents making partner choice decisions. Individuals learning with a perceptual intervention showed less bias due to decreased reliance on features that only spuriously correlate with behavior. Mechanistically, the perceptual intervention effectively increased the information-processing abilities of the individuals. Our results highlight the benefit of using multiagent reinforcement learning to model theoretically grounded social behaviors, particularly when real-world complexity prohibits fully analytical approaches. Keywords: statistical discrimination; reinforcement learning; partner choice; perceptual interventions (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:122:y:2025:p:e2319933121 Access Statistics for this article More articles in Proceedings of the National Academy of Sciences from Proceedings of the National Academy of Sciences |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.