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May 30, 2026Open Access

Evolutionary Game Theory Without Evolution: Emergent Equilibria in LLM Agent Populations on Networks

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Authors

SDStefanos Drakos

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Overview

Randomized trial tests emergent equilibria in LLM populations, indicating dynamics in network structures.

Key Points

  • The aim is to evaluate if large language model agent populations align with predictions made by evolutionary game theory in various games and network structures.
  • Tested three 2 × 2 games (Hawk-Dove, Stag Hunt, and pure coordination) across five network topologies.
  • Conducted ∼37,500 game decisions over 150 independent trials.
  • Formulated four falsifiable propositions relating to game outcomes and equilibrium frequencies.
  • Frontier LLM populations approximate the Hawk-Dove ESS at V/C = 2/3 on complete graphs, with equilibrium frequency declining in sparsity or clustering (F(4, 20) = 7.19, p = 0.0009).
  • Empirical Hawk frequency shows an attenuated response to V/C, with a slope of 0.50 ± 0.04, strongly rejecting the slope-1 null (t(38) = −13.35, p < 10−15).
  • In Stag Hunt, all runs achieved 100% Stag, and pure coordination led to deterministic selection of the first listed action (29/29 runs).

Cite This Study

Stefanos Drakos (2026) studied this question.

synapsesocial.com/papers/6a1a80c00307b78509432a12https://doi.org/10.5281/zenodo.20432106
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