Using an algorithmic approach to shape human decision-making through attraction to patterns

Abstract Evidence suggests that people are attracted to patterns and regularity. We hypothesized that decision-makers, intending to maximize profit, may be lured by the existence of regularity, even when it does not confer any additional value. An algorithm based on this premise outperformed all oth...

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Main Authors: Haran Shani-Narkiss, Baruch Eitam, Oren Amsalem
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-59131-4
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author Haran Shani-Narkiss
Baruch Eitam
Oren Amsalem
author_facet Haran Shani-Narkiss
Baruch Eitam
Oren Amsalem
author_sort Haran Shani-Narkiss
collection DOAJ
description Abstract Evidence suggests that people are attracted to patterns and regularity. We hypothesized that decision-makers, intending to maximize profit, may be lured by the existence of regularity, even when it does not confer any additional value. An algorithm based on this premise outperformed all other contenders in an international challenge to bias individuals’ preferences. To create the bias, the algorithm allocates rewards in an evolving, yet easily trackable, pattern to one option but not the other. This leads decision-makers to prefer the regular option over the other 2:1, even though this preference proves to be relatively disadvantageous. The results support the idea that humans assign value to regularity and more generally, for the utility of qualitative approaches to human decision-making. They also suggest that models of decision making that are based solely on reward learning may be incomplete.
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publishDate 2025-05-01
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spelling doaj-art-dbeb26ad6b4241d3b3cd27f17ea2dd002025-08-20T02:55:38ZengNature PortfolioNature Communications2041-17232025-05-0116111110.1038/s41467-025-59131-4Using an algorithmic approach to shape human decision-making through attraction to patternsHaran Shani-Narkiss0Baruch Eitam1Oren Amsalem2UCL Sainsbury Wellcome Centre for Neural Circuits and BehaviourSchool of Psychological Sciences, University of Haifa, Mount CarmelDivision of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical SchoolAbstract Evidence suggests that people are attracted to patterns and regularity. We hypothesized that decision-makers, intending to maximize profit, may be lured by the existence of regularity, even when it does not confer any additional value. An algorithm based on this premise outperformed all other contenders in an international challenge to bias individuals’ preferences. To create the bias, the algorithm allocates rewards in an evolving, yet easily trackable, pattern to one option but not the other. This leads decision-makers to prefer the regular option over the other 2:1, even though this preference proves to be relatively disadvantageous. The results support the idea that humans assign value to regularity and more generally, for the utility of qualitative approaches to human decision-making. They also suggest that models of decision making that are based solely on reward learning may be incomplete.https://doi.org/10.1038/s41467-025-59131-4
spellingShingle Haran Shani-Narkiss
Baruch Eitam
Oren Amsalem
Using an algorithmic approach to shape human decision-making through attraction to patterns
Nature Communications
title Using an algorithmic approach to shape human decision-making through attraction to patterns
title_full Using an algorithmic approach to shape human decision-making through attraction to patterns
title_fullStr Using an algorithmic approach to shape human decision-making through attraction to patterns
title_full_unstemmed Using an algorithmic approach to shape human decision-making through attraction to patterns
title_short Using an algorithmic approach to shape human decision-making through attraction to patterns
title_sort using an algorithmic approach to shape human decision making through attraction to patterns
url https://doi.org/10.1038/s41467-025-59131-4
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