Reproducibility Study of "Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents"

• URL: http://arxiv.org/abs/2505.09289v1
• Date: Wed, 14 May 2025 11:15:14 GMT
• Title: Reproducibility Study of "Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents"
• Authors: Pedro M. P. Curvo, Mara Dragomir, Salvador Torpes, Mohammadmahdi Rahimi,
• Abstract summary: GovSim is a simulation framework designed to assess the cooperative decision-making capabilities of large language models (LLMs) in resource-sharing scenarios.<n>We validate claims regarding the performance of large models, such as GPT-4-turbo, compared to smaller models.<n>Results show that large models can achieve sustainable cooperation, with or without the universalization principle, while smaller models fail without it.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study evaluates and extends the findings made by Piatti et al., who introduced GovSim, a simulation framework designed to assess the cooperative decision-making capabilities of large language models (LLMs) in resource-sharing scenarios. By replicating key experiments, we validate claims regarding the performance of large models, such as GPT-4-turbo, compared to smaller models. The impact of the universalization principle is also examined, with results showing that large models can achieve sustainable cooperation, with or without the principle, while smaller models fail without it. In addition, we provide multiple extensions to explore the applicability of the framework to new settings. We evaluate additional models, such as DeepSeek-V3 and GPT-4o-mini, to test whether cooperative behavior generalizes across different architectures and model sizes. Furthermore, we introduce new settings: we create a heterogeneous multi-agent environment, study a scenario using Japanese instructions, and explore an "inverse environment" where agents must cooperate to mitigate harmful resource distributions. Our results confirm that the benchmark can be applied to new models, scenarios, and languages, offering valuable insights into the adaptability of LLMs in complex cooperative tasks. Moreover, the experiment involving heterogeneous multi-agent systems demonstrates that high-performing models can influence lower-performing ones to adopt similar behaviors. This finding has significant implications for other agent-based applications, potentially enabling more efficient use of computational resources and contributing to the development of more effective cooperative AI systems.

Related papers

• KCM: KAN-Based Collaboration Models Enhance Pretrained Large Models [62.658961779827145]
  We propose a KAN-based Collaborative Model (KCM) as an improved approach to large-small model collaboration.<n>KAN offers superior visualizability and interpretability while mitigating catastrophic forgetting.
  arXiv  Detail & Related papers  (2025-10-23T07:06:21Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• MCP: A Control-Theoretic Orchestration Framework for Synergistic Efficiency and Interpretability in Multimodal Large Language Models [0.0]
  This study proposes a three-layer collaboration framework based on model-controller-task adaptation (MCP)<n> Experiments show that the MCP framework improves the performance of cross-modal benchmarking tasks, such as GLUE, COCO, ScienceQA, etc., by 15-30% compared with the baseline model, improves the reasoning efficiency by 40%, and generates the interpretable intermediate results through the Presenter layer, obtaining 90% of the manual interpretability scores.
  arXiv  Detail & Related papers  (2025-09-20T09:44:11Z)
• Ensemble Bayesian Inference: Leveraging Small Language Models to Achieve LLM-level Accuracy in Profile Matching Tasks [0.0]
  This study explores the potential of small language model(SLM) ensembles to achieve accuracy comparable to proprietary large language models (LLMs)<n>We propose Ensemble Bayesian Inference (EBI), a novel approach that applies Bayesian estimation to combine judgments from multiple SLMs.
  arXiv  Detail & Related papers  (2025-04-24T15:55:10Z)
• A Collaborative Ensemble Framework for CTR Prediction [73.59868761656317]
  We propose a novel framework, Collaborative Ensemble Training Network (CETNet), to leverage multiple distinct models. Unlike naive model scaling, our approach emphasizes diversity and collaboration through collaborative learning. We validate our framework on three public datasets and a large-scale industrial dataset from Meta.
  arXiv  Detail & Related papers  (2024-11-20T20:38:56Z)
• On the Modeling Capabilities of Large Language Models for Sequential Decision Making [52.128546842746246]
  Large pretrained models are showing increasingly better performance in reasoning and planning tasks. We evaluate their ability to produce decision-making policies, either directly, by generating actions, or indirectly. In environments with unfamiliar dynamics, we explore how fine-tuning LLMs with synthetic data can significantly improve their reward modeling capabilities.
  arXiv  Detail & Related papers  (2024-10-08T03:12:57Z)
• On the limits of agency in agent-based models [13.130587222524305]
  Agent-based modeling offers powerful insights into complex systems, but its practical utility has been limited by computational constraints. Recent advancements in large language models (LLMs) could enhance ABMs with adaptive agents, but their integration into large-scale simulations remains challenging. We present LLM archetypes, a technique that balances behavioral complexity with computational efficiency, allowing for nuanced agent behavior in large-scale simulations.
  arXiv  Detail & Related papers  (2024-09-14T04:17:24Z)
• MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate [24.92465108034783]
  Large Language Models (LLMs) have shown exceptional results on current benchmarks when working individually. The advancement in their capabilities, along with a reduction in parameter size and inference times, has facilitated the use of these models as agents. We evaluate the behavior of a network of models collaborating through debate under the influence of an adversary.
  arXiv  Detail & Related papers  (2024-06-20T20:09:37Z)
• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• Can LLM-Augmented autonomous agents cooperate?, An evaluation of their cooperative capabilities through Melting Pot [2.3732649191362483]
  This paper explores the cooperative capabilities of Large Language Model-augmented Autonomous Agents (LAAs) using the well-known Meltin Pot environments. The study's contributions include an abstraction layer to adapt Melting Pot game scenarios for LLMs. The evaluation of cooperation capabilities using a set of metrics tied to the Melting Pot's "Commons Harvest" game.
  arXiv  Detail & Related papers  (2024-03-18T00:13:43Z)
• Large Language Model-based Human-Agent Collaboration for Complex Task Solving [94.3914058341565]
  We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving. We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
  arXiv  Detail & Related papers  (2024-02-20T11:03:36Z)
• Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning [18.651307543537655]
  We propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentangled World Model. We present experimental results in Easy, Hard, and Super-Hard StarCraft II micro-management challenges to demonstrate that our method achieves high sample efficiency and exhibits superior performance in defeating the enemy armies compared to other baselines.
  arXiv  Detail & Related papers  (2023-09-08T22:12:43Z)
• Multi-Agent Imitation Learning with Copulas [102.27052968901894]
  Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
  arXiv  Detail & Related papers  (2021-07-10T03:49:41Z)
• Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts [52.844741540236285]
  This paper investigates the model-based methods in multi-agent reinforcement learning (MARL) We propose a novel decentralized model-based MARL method, named Adaptive Opponent-wise Rollout Policy (AORPO)
  arXiv  Detail & Related papers  (2021-05-07T16:20:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.