A Platform For Assessing LLMs' Collaborative Behavior And Ability To Manage Shared Resources Is Now Available!
3 main points
✔️ Designed GOVSIM (Governance of the Commons Simulation), a simulation platform designed to evaluate the cooperative behavior of LLMs and their ability to manage shared resources
✔️ Using GOVSIM, a large-scale comparison of 15 different LLMs in Conducted experiments
✔️ Only two types of experiments achieved sustainable results
Cooperate or Collapse: Emergence of Sustainability Behaviors in a Society of LLM Agents
written by Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner, Bernhard Scholkopf, Mrinmaya Sachan, Rada Mihalcea
Submitted on 25 Apr 2024
Comments: Published on arxiv.
Subjects: Computation and Language (cs.CL)
code：
The images used in this article are from the paper, the introductory slides, or were created based on them.
Introduction
Recentadvances in Large Language Models (LLMs) not only rival but in some cases surpass human capabilities in a variety of tasks.
While these models are being integrated into complex agent systems, especially recently, as LLMs become more central to such systems, the ability of LLMs to operate safely and reliably, especially in collaborative contexts, is necessary.
Research on LLM in cooperative behavior is still in its infancy, with most existing studies focusing on constrained scenarios such as board games, and some efforts have been made for single-agent LLM, but have failed to address the following issues.
- Insufficient understanding of how LLMs understand and maintain cooperative norms
- How the LLM handles interactions within the simulation and maximizes rewards is not understood
- The potential use of LLM as a simulation platform for psychological and economic theory has not been fully explored
To solve these problems, we designed GOVSIM (Governance of the Commons Simulation), a simulation platform for evaluating the cooperative behavior of LLMs and their ability to manage shared resources, and conducted a large-scale comparison experiment using 15 types of LLMs.The paperinvestigates the performance of LLM models in cooperative strategies by conducting large-scale comparative experiments with 15 different LLMs.
GOVSIM (Goveranance of the Commons Simulation)
The simulation platform designed in this paper, GOVSIM (Governance of the Commons Simulation), consists of two components : the Environment, which manages the dynamics of the simulation , and the Agent, which interacts with the simulation in a given environment. The simulation platform designed in this paper, GOVSIM (Governance of the Commons Simulation), consists of two components : the Environment , which manages the simulation dynamics, and the Agent, which interacts with the simulation in a given environment.
Environment
The environment designed in GOVSIM includes a multi-agent and partially observable framework, each with multiple rounds consisting of different phases.
An overview of each round is shown in the figure below.
This section includes the following phases
- Strategy: Agents reflect on the past, plan future actions and develop strategies
- Harvesting: Agents collect resources and determine the amount of resources to harvest
- Discussion: Agents gather in a town hall for a discussion, followed by a group discussion with all participants
During the Discussion phase, agents gather in a virtual environment to discuss, where only the agent called moderator has the ability to disclose the amount each agent harvested in the previous cycle.
Enabling this feature increases transparency and accountability among participants; in contrast, not enabling it allows us to investigate the dynamics of trust and deception among agents.
Agent
While the Agent in GOVSIM uses the architecture described in existing studies, it is designed to fit more goal-oriented tasks, in contrast to the original framework's emphasis on human simulation of everyday activities.
In addition, while the original framework limited agent interaction to one-on-one, GOVSIM extends the conversation module to allow the moderator to manage the interaction.
This allows for a more dynamic and interactive discussion, as direct questions are answered by the target agent, while more general statements can be solicited from any participant.
Based on the previous commentary, an example prompt in a simulation in which agents share a fish population would be as follows
Experiment
To demonstrate the effectiveness of GOVSIM, a large scale comparative experiment was conducted in this paper using the following 15 different LLM models.
- Closed-weights models: GPT-3.5, GPT-4, Mistral Medium, Mistral Large, Claude-3 Haiku, Claude-3 Sonnet, Claude-3 Opus
- Open-weights models: Llama-2 7B, Llama-2 13B, Llama-2 70B, Mistral 7B, Mistral 8x7B, Qwen 72B, DBRX, Command R+
This experiment investigated the ability of LLM agents to maintain fish populations in the lake and reach equilibrium between resource use and maintenance of fish populations.
The results of the simulation are shown in the graph below.
The vertical axis of the graph represents the number of fish maintained and the horizontal axis represents time. The results confirm that GPT-4 and Claude-3 Opus (green line) successfully maintained the shared resource of fish for a long period of time, while the other models (red line) failed to maintain the resource and ran out of fish in June. The red line shows that the other model (red line) fails to maintain the stock and runs out of fish in June.
Details of this result are shown in the table below.
Thus,it is clear that apoorly performing model struggles to grasp the complexity of the simulation andconsumes shared resources more quickly.
Summary
How was it?In this article, wedesigned GOVSIM (Governance of the Commons Simulation), a simulation platform for evaluating LLMs' cooperative behavior and ability to manage shared resources, and described a paper that investigated LLMs' performance in cooperative strategies by conducting a large-scale comparison experiment using 15 different LLM models. The paperinvestigated the performance of LLMs in a cooperative strategy by conducting large scale comparative experiments with 15 different LLM models.
Thecomparative experiments conducted in this paperrevealed thatonly two of the 15 LLMs used (GPT-4 and Claude-3 Opus) were able to achieve sustainable results,indicating a significant gap in the ability ofLLM modelsto manage shared resources.
On the other hand, the experiment also mentions the problem of simplistic scenarios of resource sharing, whereas managing shared resources in the real world involves more complex dynamics, such as the variety of resource types and broader stakeholders.
The author of this paper responds by saying, "In the future, extending the simulations to include this complexity will allow for a more detailed understanding of the cooperative behavior of LLM models," and we look forward to future developments.
Details of the simulation platform and experimental results presented here can be found in this paper for those interested.
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