Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
- URL: http://arxiv.org/abs/2404.16698v3
- Date: Wed, 10 Jul 2024 08:34:06 GMT
- Title: Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
- Authors: Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner, Bernhard Schölkopf, Mrinmaya Sachan, Rada Mihalcea,
- Abstract summary: GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs)
We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%.
We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
- Score: 101.17919953243107
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: As AI systems pervade human life, ensuring that large language models (LLMs) make safe decisions remains a significant challenge. We introduce the Governance of the Commons Simulation (GovSim), a generative simulation platform designed to study strategic interactions and cooperative decision-making in LLMs. In GovSim, a society of AI agents must collectively balance exploiting a common resource with sustaining it for future use. This environment enables the study of how ethical considerations, strategic planning, and negotiation skills impact cooperative outcomes. We develop an LLM-based agent architecture and test it with the leading open and closed LLMs. We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%. Ablations reveal that successful multi-agent communication between agents is critical for achieving cooperation in these cases. Furthermore, our analyses show that the failure to achieve sustainable cooperation in most LLMs stems from their inability to formulate and analyze hypotheses about the long-term effects of their actions on the equilibrium of the group. Finally, we show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability. Taken together, GovSim enables us to study the mechanisms that underlie sustainable self-government with specificity and scale. We open source the full suite of our research results, including the simulation environment, agent prompts, and a comprehensive web interface.
Related papers
- AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios [38.878966229688054]
We introduce AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios.
Drawing on Dramaturgical Theory, AgentSense employs a bottom-up approach to create 1,225 diverse social scenarios constructed from extensive scripts.
We evaluate LLM-driven agents through multi-turn interactions, emphasizing both goal completion and implicit reasoning.
arXiv Detail & Related papers (2024-10-25T07:04:16Z) - Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts [20.8288955218712]
We propose a framework where a principal guides an agent in a Markov Decision Process (MDP) using a series of contracts.
We present and analyze a meta-algorithm that iteratively optimize the policies of the principal and agent.
We then scale our algorithm with deep Q-learning and analyze its convergence in the presence of approximation error.
arXiv Detail & Related papers (2024-07-25T14:28:58Z) - Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory [8.80864059602965]
Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale.
Our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time.
We analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security.
arXiv Detail & Related papers (2024-06-20T14:42:58Z) - 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) - MetaAgents: Simulating Interactions of Human Behaviors for LLM-based
Task-oriented Coordination via Collaborative Generative Agents [27.911816995891726]
We introduce collaborative generative agents, endowing LLM-based Agents with consistent behavior patterns and task-solving abilities.
We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills.
Our work provides valuable insights into the role and evolution of Large Language Models in task-oriented social simulations.
arXiv Detail & Related papers (2023-10-10T10:17:58Z) - Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View [60.80731090755224]
This paper probes the collaboration mechanisms among contemporary NLP systems by practical experiments with theoretical insights.
We fabricate four unique societies' comprised of LLM agents, where each agent is characterized by a specific trait' (easy-going or overconfident) and engages in collaboration with a distinct thinking pattern' (debate or reflection)
Our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring social psychology theories.
arXiv Detail & Related papers (2023-10-03T15:05:52Z) - The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI)
We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents.
We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
arXiv Detail & Related papers (2023-09-14T17:12:03Z) - Building Cooperative Embodied Agents Modularly with Large Language
Models [104.57849816689559]
We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments.
We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework.
Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
arXiv Detail & Related papers (2023-07-05T17:59:27Z) - ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in
Multi-Agent Simulations [110.72725220033983]
Epsilon-Robust Multi-Agent Simulation (ERMAS) is a framework for learning AI policies that are robust to such multiagent sim-to-real gaps.
ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations.
In particular, ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations.
arXiv Detail & Related papers (2021-06-10T04:32:20Z)
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.