[2604.09502] Strategic Algorithmic Monoculture:Experimental Evidence from Coordination Games

Computer Science > Artificial Intelligence arXiv:2604.09502 (cs) [Submitted on 10 Apr 2026] Title:Strategic Algorithmic Monoculture:Experimental Evidence from Coordination Games Authors:Gonzalo Ballestero, Hadi Hosseini, Samarth Khanna, Ran I. Shorrer View a PDF of the paper titled Strategic Algorithmic Monoculture:Experimental Evidence from Coordination Games, by Gonzalo Ballestero and 3 other authors View PDF Abstract:AI agents increasingly operate in multi-agent environments where outcomes depend on coordination. We distinguish primary algorithmic monoculture -- baseline action similarity -- from strategic algorithmic monoculture, whereby agents adjust similarity in response to incentives. We implement a simple experimental design that cleanly separates these forces, and deploy it on human and large language model (LLM) subjects. LLMs exhibit high levels of baseline similarity (primary monoculture) and, like humans, they regulate it in response to coordination incentives (strategic monoculture). While LLMs coordinate extremely well on similar actions, they lag behind humans in sustaining heterogeneity when divergence is rewarded. Subjects: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA); Theoretical Economics (econ.TH) Cite as: arXiv:2604.09502 [cs.AI]   (or arXiv:2604.09502v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.09502 Focus to learn more arXiv-issued DOI via DataCite (pending registration)...