The advent of Large Language Models (LLMs) presents a novel opportunity to build high-fidelity agent-based models for simulating complex social systems. However, the behavior of these LLM-based agents in game-theoretic network games remains surprisingly unexplored. In this work, we introduce "NetworkGames," a novel simulation framework designed to investigate how network topology and agent personality jointly shape the evolution of cooperation in network games. We instantiate a population of LLM agents, each endowed with a distinct personality from the MBTI taxonomy, and situate them in various network structures (e.g., small-world and scale-free). Through extensive simulations of the Iterated Prisoner's Dilemma, we first establish a baseline dyadic interaction matrix, revealing nuanced cooperative preferences between all 16 personality pairs. We then demonstrate that macro-level cooperative outcomes are not predictable from dyadic interactions alone; they are co-determined by the network's connectivity and the spatial distribution of personalities. For instance, we find that small-world networks are detrimental to cooperation, while strategically placing pro-social personalities in hub positions within scale-free networks can significantly promote cooperative behavior. Our findings offer significant implications for designing healthier online social environments and forecasting collective behavior. We open-source our framework to foster further research in network game simulations.

翻译：大型语言模型（LLMs）的出现为构建高保真度的基于智能体的模型以模拟复杂社会系统提供了新的机遇。然而，这些基于LLM的智能体在博弈论网络博弈中的行为仍鲜有研究。本文提出“NetworkGames”——一个新颖的仿真框架，旨在探究网络拓扑结构与智能体人格如何共同影响网络博弈中合作行为的演化。我们实例化了一组LLM智能体，每个智能体被赋予MBTI人格分类中的一种独特人格，并将其置于多种网络结构（如小世界网络与无标度网络）中。通过对迭代囚徒困境进行大量仿真，我们首先建立了基准的双向交互矩阵，揭示了全部16种人格组合间微妙的合作偏好。随后我们证明，宏观层面的合作结果无法仅从双向互动中预测；它们同时受网络连接性与人格空间分布的共同决定。例如，我们发现小世界网络不利于合作，而在无标度网络中将亲社会人格策略性地置于枢纽位置则能显著促进合作行为。本研究对设计更健康的在线社交环境及预测集体行为具有重要启示。我们开源了该框架以推动网络博弈仿真领域的进一步研究。