https://doi.org/10.1140/epjds/s13688-026-00653-2
Research
Stigmergic influence of simple bots on human cooperation in digital environments
1
Laboratoire de Physique Théorique, CNRS, Université Toulouse III – Paul Sabatier, 31062, Toulouse, France
2
Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, CNRS, Université Toulouse III – Paul Sabatier, 31062, Toulouse, France
3
Toulouse School of Economics, CNRS, 31080, Toulouse, France
4
Departamento de Matemáticas, Universidad Carlos III de Madrid, 28911, Leganés, Madrid, Spain
5
Institute for Advanced Study in Toulouse, 31080, Toulouse, France
a
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Received:
9
December
2025
Accepted:
30
March
2026
Published online:
13
April
2026
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.
Key words: Human cooperation / Deception / Stigmergy / Collective intelligence / Agent-based modeling / Model-driven bots
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-026-00653-2.
Handling Editor: Iain Couzin
© The Author(s) 2026
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