Beyond the Tragedy of the Commons: Building A Reputation System for Generative Multi-agent Systems

• URL: http://arxiv.org/abs/2505.05029v2
• Date: Mon, 12 May 2025 07:23:01 GMT
• Title: Beyond the Tragedy of the Commons: Building A Reputation System for Generative Multi-agent Systems
• Abstract summary: RepuNet is a dual-level reputation framework that models both agent-level reputation dynamics and system-level network evolution.<n>We show that RepuNet effectively mitigates the 'tragedy of the commons', promoting and sustaining cooperation in generative MASs.
• Score: 14.839510542470734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The tragedy of the commons, where individual self-interest leads to collectively disastrous outcomes, is a pervasive challenge in human society. Recent studies have demonstrated that similar phenomena can arise in generative multi-agent systems (MASs). To address this challenge, this paper explores the use of reputation systems as a remedy. We propose RepuNet, a dynamic, dual-level reputation framework that models both agent-level reputation dynamics and system-level network evolution. Specifically, driven by direct interactions and indirect gossip, agents form reputations for both themselves and their peers, and decide whether to connect or disconnect other agents for future interactions. Through two distinct scenarios, we show that RepuNet effectively mitigates the 'tragedy of the commons', promoting and sustaining cooperation in generative MASs. Moreover, we find that reputation systems can give rise to rich emergent behaviors in generative MASs, such as the formation of cooperative clusters, the social isolation of exploitative agents, and the preference for sharing positive gossip rather than negative ones.

Related papers

• Emergence of cooperation: A reputation-modulated reinforcement learning [19.357969561684982]
  Reputation is widely recognized as a key mechanism for sustaining cooperation.<n>We propose a spatial prisoner's dilemma game grounded in the reinforcement learning paradigm.<n>Our results reveal that reputation-modulated learning significantly promotes the emergence of cooperative behavior.
  arXiv  Detail & Related papers  (2026-08-20T13:27:41Z)
• Emergent Social Intelligence Risks in Generative Multi-Agent Systems [106.60961969939676]
  Multi-agent systems are rapidly moving from lab prototypes to real-world deployments, where they jointly plan, negotiate, and allocate resources to solve complex tasks.<n>Here, we present a pioneer study of such emergent multi-agent risk in pathologies that involve competition over shared resources, sequential handoff collaboration, collective decision aggregation, and others.<n>We observe that such group behaviors arise frequently across repeated trials and a wide range of interaction conditions, rather than as rare or pathological cases.<n>These findings expose the dark side of intelligent multi-agent systems: a social intelligence risk where agent collectives spontaneously reproduce familiar failure patterns from human societies.
  arXiv  Detail & Related papers  (2026-03-29T17:10:28Z)
• Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation [67.09494009022471]
  We propose a novel Personalized Semi-Autoregressive with online knowledge Distillation (PSAD) framework for reranking.<n>In this framework, the teacher model adopts a semi-autoregressive generator to balance generation quality and efficiency, while its ranking knowledge is distilled online into a lightweight scoring network.<n>Experiments conducted on three large-scale public datasets demonstrate that PSAD significantly outperforms state-of-the-art baselines in both ranking performance and inference efficiency.
  arXiv  Detail & Related papers  (2026-03-07T08:37:13Z)
• Realistic gossip in Trust Game on networks: the GODS model [0.0]
  We develop an agent-based model that pairs realistic gossip processes with different variants of Trust Game.<n>Results show that cooperators suffer when local interactions govern spread of gossip, because they cannot discriminate against defectors.<n> Realistic gossiping increases the overall amount of resources, but is more likely to promote defection.
  arXiv  Detail & Related papers  (2025-11-25T12:23:05Z)
• The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems [90.96738882568224]
  This paper investigates the over-competition in multi-agent debate, where agents under extreme pressure exhibit unreliable, harmful behaviors.<n>To study this phenomenon, we propose HATE, a novel experimental framework that simulates debates under a zero-sum competition arena.
  arXiv  Detail & Related papers  (2025-09-30T11:44:47Z)
• Can an Individual Manipulate the Collective Decisions of Multi-Agents? [53.01767232004823]
  M-Spoiler is a framework that simulates agent interactions within a multi-agent system to generate adversarial samples.<n>M-Spoiler introduces a stubborn agent that actively aids in optimizing adversarial samples.<n>Our findings confirm the risks posed by the knowledge of an individual agent in multi-agent systems.
  arXiv  Detail & Related papers  (2025-09-20T01:54:20Z)
• When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems [78.04679174291329]
  We introduce a proof-of-concept to simulate the risks of malicious multi-agent systems (MAS)<n>We apply this framework to two high-risk fields: misinformation spread and e-commerce fraud.<n>Our findings show that decentralized systems are more effective at carrying out malicious actions than centralized ones.
  arXiv  Detail & Related papers  (2025-07-19T15:17:30Z)
• Emergence of human-like polarization among large language model agents [61.622596148368906]
  We simulate a networked system involving thousands of large language model agents, discovering their social interactions, result in human-like polarization.<n>Similarities between humans and LLM agents raise concerns about their capacity to amplify societal polarization, but also hold the potential to serve as a valuable testbed for identifying plausible strategies to mitigate it.
  arXiv  Detail & Related papers  (2025-01-09T11:45:05Z)
• LMAgent: A Large-scale Multimodal Agents Society for Multi-user Simulation [66.52371505566815]
  Large language models (LLMs)-based AI agents have made significant progress, enabling them to achieve human-like intelligence.<n>We present LMAgent, a very large-scale and multimodal agents society based on multimodal LLMs.<n>In LMAgent, besides chatting with friends, the agents can autonomously browse, purchase, and review products, even perform live streaming e-commerce.
  arXiv  Detail & Related papers  (2024-12-12T12:47:09Z)
• Multi-Agents are Social Groups: Investigating Social Influence of Multiple Agents in Human-Agent Interactions [7.421573539569854]
  We investigate whether a group of AI agents can create social pressure on users to agree with them. We found that conversing with multiple agents increased the social pressure felt by participants. Our study shows the potential advantages of multi-agent systems over single-agent platforms in causing opinion change.
  arXiv  Detail & Related papers  (2024-11-07T10:00:46Z)
• SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning [58.84311336011451]
  We propose a novel gradient-based state representation for multi-agent reinforcement learning. We employ denoising score matching to learn the social gradient fields (SocialGFs) from offline samples. In practice, we integrate SocialGFs into the widely used multi-agent reinforcement learning algorithms, e.g., MAPPO.
  arXiv  Detail & Related papers  (2024-05-03T04:12:19Z)
• Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning [3.3751859064985483]
  We show that network rewiring facilitates mutual cooperation even when one agent always offers cooperation. We also find that ostracism alone is not sufficient to make cooperation emerge. Our findings provide insights into the conditions and mechanisms necessary for the emergence of cooperation.
  arXiv  Detail & Related papers  (2023-10-06T23:18:55Z)
• AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors [93.38830440346783]
  We propose a multi-agent framework framework that can collaboratively adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that framework framework can effectively deploy multi-agent groups that outperform a single agent. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups.
  arXiv  Detail & Related papers  (2023-08-21T16:47:11Z)
• Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems [2.4555276449137042]
  Problems of cooperation are omnipresent within human society. As the use of AI becomes more pervasive throughout society, the need for socially intelligent agents is becoming increasingly evident. This paper presents a comprehensive analysis and evaluation of the behaviors and learning dynamics associated with direct punishment, third-party punishment, partner selection, and reputation.
  arXiv  Detail & Related papers  (2023-01-19T19:33:54Z)
• Cooperation and Reputation Dynamics with Reinforcement Learning [6.219565750197311]
  We show how reputations can be used as a way to establish trust and cooperation. We propose two mechanisms to alleviate convergence to undesirable equilibria. We show how our results relate to the literature in Evolutionary Game Theory.
  arXiv  Detail & Related papers  (2021-02-15T12:48:56Z)
• Modelling Cooperation in Network Games with Spatio-Temporal Complexity [11.665246332943058]
  We study the emergence of self-organized cooperation in complex gridworld domains. Using multi-agent deep reinforcement learning, we simulate an agent society for a variety of plausible mechanisms. Our methods have implications for mechanism design in both human and artificial agent systems.
  arXiv  Detail & Related papers  (2021-02-13T12:04:52Z)
• Multi-Agent Interactions Modeling with Correlated Policies [53.38338964628494]
  In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework. We develop a Decentralized Adrial Imitation Learning algorithm with Correlated policies (CoDAIL) Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators.
  arXiv  Detail & Related papers  (2020-01-04T17:31:53Z)

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.