Stigmergic influence of simple bots on human cooperation in digital environments

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This preprint investigates how simple, model-driven bots that leave predefined patterns in a shared “digital environment” influence human cooperation versus defection in a competitive rating game modeled after online marketplaces. Participants interacted with either four human partners or four bots (cooperative, neutral, deceptive, or optimized for group performance) without knowing about the bots, and the authors report that bot presence and behavior significantly changed human strategies and performance. Higher bot cooperation improved human outcomes but also increased deceptive human strategies, and in less cooperative settings participants tended to choose more collaborative or neutral behaviors to preserve informational value; behavioral profiles (collaborators, neutrals, defectors) could be predicted using a linear regression with the average rated-cell value, diversity of rated cells, and player rank, which an adaptive agent-based model reproduced. As a Research Square preprint, it has not been peer reviewed, and the cited caveats are limited to that status. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract In the digital era, human cooperation is increasingly mediated by indirect social cues such as ratings, reviews, and other digital traces left in online environments. These traces often guide collective behavior via stigmergy, a coordination mechanism whereby individuals interact through modifications of a shared environment. In this study, we explore how simple model-driven bots can influence human cooperation or defection in a competitive rating game inspired by online marketplaces. Participants, unaware of the bots' presence, interacted with either four human partners or four bots exhibiting predefined behaviors—cooperative, neutral, deceptive, or optimized for group performance. We show that the presence and behavior of bots significantly affect human strategies and performance. Higher levels of cooperation among bots improve human outcomes but also increase the frequency of deceptive human strategies, suggesting exploitation of reliable social information. Conversely, in less cooperative environments, participants adopt more collaborative or neutral behaviors to preserve informational value. By classifying individuals into three behavioral profiles—collaborators, neutrals, and defectors—we develop a linear regression model using three cues: the average value of rated cells, the diversity of rated cells, and the player's rank. These cues allow accurate prediction of behavioral profile distributions across experimental conditions. An adaptive agent-based model further reproduces the empirical results. Our findings demonstrate that even simple bots can strongly influence collective dynamics in human groups. These insights have implications for the design of recommendation systems, the regulation of automated agents, and the understanding of cooperation and deception in digital societies.
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Stigmergic influence of simple bots on human cooperation in digital environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stigmergic influence of simple bots on human cooperation in digital environments Thomas Bassanetti, Stéphane Cezera, Maxime Delacroix, Ramón Escobedo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7169654/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the digital era, human cooperation is increasingly mediated by indirect social cues such as ratings, reviews, and other digital traces left in online environments. These traces often guide collective behavior via stigmergy, a coordination mechanism whereby individuals interact through modifications of a shared environment. In this study, we explore how simple model-driven bots can influence human cooperation or defection in a competitive rating game inspired by online marketplaces. Participants, unaware of the bots' presence, interacted with either four human partners or four bots exhibiting predefined behaviors—cooperative, neutral, deceptive, or optimized for group performance. We show that the presence and behavior of bots significantly affect human strategies and performance. Higher levels of cooperation among bots improve human outcomes but also increase the frequency of deceptive human strategies, suggesting exploitation of reliable social information. Conversely, in less cooperative environments, participants adopt more collaborative or neutral behaviors to preserve informational value. By classifying individuals into three behavioral profiles—collaborators, neutrals, and defectors—we develop a linear regression model using three cues: the average value of rated cells, the diversity of rated cells, and the player's rank. These cues allow accurate prediction of behavioral profile distributions across experimental conditions. An adaptive agent-based model further reproduces the empirical results. Our findings demonstrate that even simple bots can strongly influence collective dynamics in human groups. These insights have implications for the design of recommendation systems, the regulation of automated agents, and the understanding of cooperation and deception in digital societies. Human Cooperation and Deception Stigmergy Agent-based modeling Model-driven bots Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7169654","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499965544,"identity":"f1a0f7ed-7b59-4bec-af63-20d5d141969c","order_by":0,"name":"Thomas Bassanetti","email":"","orcid":"","institution":"Laboratoire de Physique Théorique","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Bassanetti","suffix":""},{"id":499965545,"identity":"e396cd04-d88a-4d0c-b880-725fe8832bda","order_by":1,"name":"Stéphane Cezera","email":"","orcid":"","institution":"Toulouse School of 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