Group Selection Promotes Prosocial Prompts in Populations of LLM Agents

2026-06-22

AI Summary

This study addresses how cooperative behavior can spontaneously emerge in populations of large language model (LLM) agents without relying on human-designed prosocial instructions. It introduces, for the first time, the evolutionary biology concept of group selection into multi-agent systems by constructing a simulation framework that integrates repeated social dilemma games, intergenerational transmission of natural language prompts, and replication–mutation dynamics. Experimental results demonstrate that under group selection, prompts from high-cooperation-performing groups are effectively inherited, significantly enhancing and stabilizing collective cooperation, whereas individual-level selection leads to widespread defection. Analyses using advanced models such as GPT-5.4 further confirm the robustness of this mechanism and reveal LLMs’ capacity for prospective adaptation to selection pressures, thereby overcoming the limitations of conventional individual-reward paradigms.

Abstract

Current approaches to instill prosociality in large language model (LLM) agents often rely on humans specifying desired behaviors at the individual level, which does not guarantee cooperation within LLM populations. As frontier training shifts toward individual rewards for verifiable tasks, such as mathematics and coding, this outcome-based focus may further undermine cooperation in multi-agent settings. Large-scale cooperation in human populations emerged via unguided evolutionary mechanisms, not a central architect. Group selection, in which cooperative groups within a population outcompete less cooperative ones, has been argued to be essential. In this study, we explore whether group selection can promote cooperation in populations of LLM agents. We introduce a multi-agent simulation framework in which LLM agents play a repeated social dilemma game and transmit their natural-language prompts across generations under either individual- or group-level selection. Under group selection, prompts from high-performing groups are transmitted, thereby promoting prosociality and stabilizing cooperation. Under individual selection, self-interested prompts dominate, causing populations to collapse into collective defection. This gap is robust across prompt ablations, alternative game framings, and model swaps. We theoretically reproduce key results using a replicator-mutator model, whose empirical transmission kernel predicts a phase transition at a critical threshold. Preliminary findings show that, when informed about the selection mechanism, GPT-5.4 preemptively and gradually adjusts first-generation donations. This demonstrates strong anticipatory behavior that was not observed in the other tested models. These results demonstrate that prosocial prompts and cooperative behaviors evolve in LLM agent populations under group selection.

Problem

• group selection
• prosociality
• large language models
• multi-agent cooperation
• social dilemma

Innovation

• group selection
• prosocial prompts
• multi-agent LLM
• evolutionary dynamics
• cooperation emergence