研究智能体模拟对博弈中社会福利的影响，发现混合策略模拟未必有益。
Game Theory with Simulation in the Presence of Unpredictable Randomisation
- 通过支付固定成本模拟对手混合策略以优化自身决策
- 混合策略模拟在部分博弈中无法提升双方收益，且决策依赖时无效
- 信任可调节或需隐私保护时，模拟可改善社会福祉
AI代理在某些方面具有可预测性，这与传统代理不同。我们探讨如何利用这种可预测性提升社会福利。在博弈论框架下，一个代理可通过支付固定成本模拟另一个代理以学习其混合策略。负面结果表明，与纯策略模拟不同，允许混合策略模拟在所有“广义信任博弈”中不再保证双方收益提升。事实上，当被模拟者的行为可依赖模拟者行为时，混合策略模拟无益。此外，判断某博弈中模拟是否引入帕累托改进的纳什均衡是NP难问题。正面结果表明，当模拟者可调节信任水平、双方面临信任与协调双重挑战，或维持一定隐私对合作至关重要时，混合策略模拟可提升社会福利。
原文摘要 · Abstract (English)
AI agents will be predictable in certain ways that traditional agents are not. Where and how can we leverage this predictability in order to improve social welfare? We study this question in a game-theoretic setting where one agent can pay a fixed cost to simulate the other in order to learn its mixed strategy. As a negative result, we prove that, in contrast to prior work on pure-strategy simulation, enabling mixed-strategy simulation may no longer lead to improved outcomes for both players in all so-called "generalised trust games". In fact, mixed-strategy simulation does not help in any game where the simulatee's action can depend on that of the simulator. We also show that, in general, deciding whether simulation introduces Pareto-improving Nash equilibria in a given game is NP-hard. As positive results, we establish that mixed-strategy simulation can improve social welfare if the simulator has the option to scale their level of trust, if the players face challenges with both trust and coordination, or if maintaining some level of privacy is essential for enabling cooperation.
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