Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma?

• URL: http://arxiv.org/abs/2406.13605v1
• Date: Wed, 19 Jun 2024 14:51:14 GMT
• Title: Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma?
• Authors: Nicoló Fontana, Francesco Pierri, Luca Maria Aiello,
• Abstract summary: We study the cooperative behavior of Llama2 when playing the Iterated Prisoner's Dilemma against random adversaries displaying various levels of hostility. We find that Llama2 tends not to initiate defection but it adopts a cautious approach towards cooperation. In comparison to prior research on human participants, Llama2 exhibits a greater inclination towards cooperative behavior.
• Score: 0.1474723404975345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The behavior of Large Language Models (LLMs) as artificial social agents is largely unexplored, and we still lack extensive evidence of how these agents react to simple social stimuli. Testing the behavior of AI agents in classic Game Theory experiments provides a promising theoretical framework for evaluating the norms and values of these agents in archetypal social situations. In this work, we investigate the cooperative behavior of Llama2 when playing the Iterated Prisoner's Dilemma against random adversaries displaying various levels of hostility. We introduce a systematic methodology to evaluate an LLM's comprehension of the game's rules and its capability to parse historical gameplay logs for decision-making. We conducted simulations of games lasting for 100 rounds, and analyzed the LLM's decisions in terms of dimensions defined in behavioral economics literature. We find that Llama2 tends not to initiate defection but it adopts a cautious approach towards cooperation, sharply shifting towards a behavior that is both forgiving and non-retaliatory only when the opponent reduces its rate of defection below 30%. In comparison to prior research on human participants, Llama2 exhibits a greater inclination towards cooperative behavior. Our systematic approach to the study of LLMs in game theoretical scenarios is a step towards using these simulations to inform practices of LLM auditing and alignment.

Related papers

• Can Machines Think Like Humans? A Behavioral Evaluation of LLM-Agents in Dictator Games [7.504095239018173]
  Large Language Model (LLM)-based agents increasingly undertake real-world tasks and engage with human society. This study investigates how different personas and experimental framings affect these AI agents' altruistic behavior. Despite being trained on extensive human-generated data, these AI agents cannot accurately predict human decisions.
  arXiv  Detail & Related papers  (2024-10-28T17:47:41Z)
• Toward Optimal LLM Alignments Using Two-Player Games [86.39338084862324]
  In this paper, we investigate alignment through the lens of two-agent games, involving iterative interactions between an adversarial and a defensive agent. We theoretically demonstrate that this iterative reinforcement learning optimization converges to a Nash Equilibrium for the game induced by the agents. Experimental results in safety scenarios demonstrate that learning in such a competitive environment not only fully trains agents but also leads to policies with enhanced generalization capabilities for both adversarial and defensive agents.
  arXiv  Detail & Related papers  (2024-06-16T15:24:50Z)
• Human vs. Machine: Behavioral Differences Between Expert Humans and Language Models in Wargame Simulations [1.6108153271585284]
  We show that large language models (LLMs) behave differently compared to humans in high-stakes military decision-making scenarios. Our results motivate policymakers to be cautious before granting autonomy or following AI-based strategy recommendations.
  arXiv  Detail & Related papers  (2024-03-06T02:23:32Z)
• GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations [87.99872683336395]
  Large Language Models (LLMs) are integrated into critical real-world applications. This paper evaluates LLMs' reasoning abilities in competitive environments. We first propose GTBench, a language-driven environment composing 10 widely recognized tasks.
  arXiv  Detail & Related papers  (2024-02-19T18:23:36Z)
• LLM-driven Imitation of Subrational Behavior : Illusion or Reality? [3.2365468114603937]
  Existing work highlights the ability of Large Language Models to address complex reasoning tasks and mimic human communication. We propose to investigate the use of LLMs to generate synthetic human demonstrations, which are then used to learn subrational agent policies. We experimentally evaluate the ability of our framework to model sub-rationality through four simple scenarios.
  arXiv  Detail & Related papers  (2024-02-13T19:46:39Z)
• Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis [16.285154752969717]
  This study systematically analyzes Large Language Models (LLMs) in the context of game theory. Experiments indicate that even the current state-of-the-art LLM exhibits substantial disparities compared to humans in game theory.
  arXiv  Detail & Related papers  (2023-12-09T07:33:26Z)
• ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents [77.34720446306419]
  Alympics is a systematic simulation framework utilizing Large Language Model (LLM) agents for game theory research. Alympics creates a versatile platform for studying complex game theory problems.
  arXiv  Detail & Related papers  (2023-11-06T16:03:46Z)
• MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks [49.60689355674541]
  A rich literature in cognitive science has studied people's causal and moral intuitions. This work has revealed a number of factors that systematically influence people's judgments. We test whether large language models (LLMs) make causal and moral judgments about text-based scenarios that align with human participants.
  arXiv  Detail & Related papers  (2023-10-30T15:57:32Z)
• LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay [55.12945794835791]
  Using Avalon as a testbed, we employ system prompts to guide LLM agents in gameplay. We propose a novel framework, tailored for Avalon, features a multi-agent system facilitating efficient communication and interaction. Results affirm the framework's effectiveness in creating adaptive agents and suggest LLM-based agents' potential in navigating dynamic social interactions.
  arXiv  Detail & Related papers  (2023-10-23T14:35:26Z)
• The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games? [0.0]
  We investigated the capability of the GPT-3.5 large language model (LLM) to operationalize natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior. We used a prompt to describe the task environment using a similar protocol to that used in experimental psychology studies with human subjects. Our results provide evidence that LLMs can, to some extent, translate natural language descriptions of different cooperative stances into corresponding descriptions of appropriate task behaviour.
  arXiv  Detail & Related papers  (2023-05-13T17:23:16Z)
• Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values [68.8204255655161]
  This study proposes a novel approach to explain cooperative strategies in multiagent RL using Shapley values. Results could have implications for non-discriminatory decision making, ethical and responsible AI-derived decisions or policy making under fairness constraints.
  arXiv  Detail & Related papers  (2021-10-04T10:28:57Z)

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.