Do AI Agents Exhibit Greed in Shared Resource Environments?

Abstract

Large Language Models (LLMs) are increasingly used in simulations, either in academic settings for research studies or in industry for prototyping. Previous research has investigated the extent to which agents can mimic human behavior in socioeconomic settings; however, there is limited research on greedy decision-making by agents in simulated resource allocation environments. Furthermore, there is limited work on cross-model evaluation. Our research investigates the decision-making of ten agents across two different experimental conditions: one in which agents are able to communicate with other agents, and one in which emotional contexts are directly injected into the prompt. Based on a conceptual framework of greed, we find that agents predominantly exhibited greed-like behavior across all conditions. Interaction and self-reported social connection did not meaningfully influence the agents’ decision-making. We evaluated four different models: gpt-5-mini, gemini-3-flash-preview, claude-haiku-4-5-20251001, and grok-3-mini-fast-beta, and observed that while models differed in their self-reported connection scores, they did not differ significantly in greediness scores. The code and experimental artifacts are available at https://github.com/tiaL-ops/simCo.