Promoting Cooperation in the Public Goods Game using Artificial Intelligent Agents
- URL: http://arxiv.org/abs/2412.05450v1
- Date: Fri, 06 Dec 2024 22:16:21 GMT
- Title: Promoting Cooperation in the Public Goods Game using Artificial Intelligent Agents
- Authors: Arend Hintze, Christoph Adami,
- Abstract summary: Using a computational evolutionary model, we find that only when AI agents mimic player behavior does the critical synergy threshold for cooperation decrease.<n>This suggests that we can leverage AI to promote collective well-being in societal dilemmas by designing AI agents to mimic human players.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The tragedy of the commons illustrates a fundamental social dilemma where individual rational actions lead to collectively undesired outcomes, threatening the sustainability of shared resources. Strategies to escape this dilemma, however, are in short supply. In this study, we explore how artificial intelligence (AI) agents can be leveraged to enhance cooperation in public goods games, moving beyond traditional regulatory approaches to using AI as facilitators of cooperation. We investigate three scenarios: (1) Mandatory Cooperation Policy for AI Agents, where AI agents are institutionally mandated always to cooperate; (2) Player-Controlled Agent Cooperation Policy, where players evolve control over AI agents' likelihood to cooperate; and (3) Agents Mimic Players, where AI agents copy the behavior of players. Using a computational evolutionary model with a population of agents playing public goods games, we find that only when AI agents mimic player behavior does the critical synergy threshold for cooperation decrease, effectively resolving the dilemma. This suggests that we can leverage AI to promote collective well-being in societal dilemmas by designing AI agents to mimic human players.
Related papers
- FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory [51.96049148869987]
We present FAIRGAME, a Framework for AI Agents Bias Recognition using Game Theory.
We describe its implementation and usage, and we employ it to uncover biased outcomes in popular games among AI agents.
Overall, FAIRGAME allows users to reliably and easily simulate their desired games and scenarios.
arXiv Detail & Related papers (2025-04-19T15:29:04Z) - Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents [61.132523071109354]
This paper investigates the interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios.
Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" stances than pure game-theoretic agents.
arXiv Detail & Related papers (2025-04-11T15:41:21Z) - Superintelligence Strategy: Expert Version [64.7113737051525]
Destabilizing AI developments could raise the odds of great-power conflict.
Superintelligence -- AI vastly better than humans at nearly all cognitive tasks -- is now anticipated by AI researchers.
We introduce the concept of Mutual Assured AI Malfunction.
arXiv Detail & Related papers (2025-03-07T17:53:24Z) - Responsible AI Agents [17.712990593093316]
Companies such as OpenAI, Google, Microsoft, and Salesforce promise their AI Agents will go from generating passive text to executing tasks.
The potential power of AI Agents has fueled legal scholars' fears that AI Agents will enable rogue commerce, human manipulation, rampant defamation, and intellectual property harms.
This Article addresses the concerns around AI Agents head on.
It shows that core aspects of how one piece of software interacts with another creates ways to discipline AI Agents so that rogue, undesired actions are unlikely.
arXiv Detail & Related papers (2025-02-25T16:49:06Z) - Human-AI Collaboration in Real-World Complex Environment with
Reinforcement Learning [8.465957423148657]
We show that learning from humans is effective and that human-AI collaboration outperforms human-controlled and fully autonomous AI agents.
We develop a user interface to allow humans to assist AI agents effectively.
arXiv Detail & Related papers (2023-12-23T04:27:24Z) - 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) - ProAgent: Building Proactive Cooperative Agents with Large Language
Models [89.53040828210945]
ProAgent is a novel framework that harnesses large language models to create proactive agents.
ProAgent can analyze the present state, and infer the intentions of teammates from observations.
ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
arXiv Detail & Related papers (2023-08-22T10:36:56Z) - Discriminatory or Samaritan -- which AI is needed for humanity? An
Evolutionary Game Theory Analysis of Hybrid Human-AI populations [0.5308606035361203]
We study how different forms of AI influence the evolution of cooperation in a human population playing the one-shot Prisoner's Dilemma game.
We found that Samaritan AI agents that help everyone unconditionally, including defectors, can promote higher levels of cooperation in humans than Discriminatory AIs.
arXiv Detail & Related papers (2023-06-30T15:56:26Z) - On Avoiding Power-Seeking by Artificial Intelligence [93.9264437334683]
We do not know how to align a very intelligent AI agent's behavior with human interests.
I investigate whether we can build smart AI agents which have limited impact on the world, and which do not autonomously seek power.
arXiv Detail & Related papers (2022-06-23T16:56:21Z) - Cooperative Artificial Intelligence [0.0]
We argue that there is a need for research on the intersection between game theory and artificial intelligence.
We discuss the problem of how an external agent can promote cooperation between artificial learners.
We show that the resulting cooperative outcome is stable in certain games even if the planning agent is turned off.
arXiv Detail & Related papers (2022-02-20T16:50:37Z) - Adversarial Attacks in Cooperative AI [0.0]
Single-agent reinforcement learning algorithms in a multi-agent environment are inadequate for fostering cooperation.
Recent work in adversarial machine learning shows that models can be easily deceived into making incorrect decisions.
Cooperative AI might introduce new weaknesses not investigated in previous machine learning research.
arXiv Detail & Related papers (2021-11-29T07:34:12Z)
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.