内生网络形成中的策略性谈判
Strategic Negotiations in Endogenous Network Formation
AI总结：
本文研究内生网络形成中任意子集智能体可策略性歪曲信念的问题，提出求解全部纳什均衡的高效算法，并设计新智能体学习策略性信息的算法，在模拟与国际贸易数据上验证了多策略性智能体带来的复杂效用变化。
摘要：
在网络形成博弈中，智能体相互建立连边以最大化自身效用。每个智能体的效用取决于其私有信念以及它在网络中的连边。策略性智能体可能歪曲自身信念，以获得更有利的网络结果。该领域以往大多数研究只考虑诚实智能体或单一策略性智能体。本文提出一个允许任意子集智能体具有策略性的模型。我们给出一种高效算法，用于在纳什均衡存在时找出全部均衡集合，并在不存在时给出不存在性证明。我们还表明，当存在多个策略性智能体时，与所有智能体都诚实的情形相比，它们的效用可能上升也可能下降。智能体间相关性的微小变化即可引发这种转变。相比之下，文献中探讨的较简单的单一策略性智能体设定缺乏此类复杂模式。最后，我们开发了一种算法，使新加入的智能体能够学习策略性行为所需的信息。即使（未知的）策略性智能体偏离纳什最优策略，我们的算法仍然有效。我们在模拟网络和一个国际贸易真实数据集上验证了这些结果。
英文摘要：
In network formation games, agents form edges with each other to maximize their utility. Each agent's utility depends on its private beliefs and its edges in the network. Strategic agents can misrepresent their beliefs to get a better resulting network. Most prior works in this area consider honest agents or a single strategic agent. Instead, we propose a model where any subset of agents can be strategic. We provide an efficient algorithm for finding the set of Nash equilibria, if any exist, and certify their nonexistence otherwise. We also show that when several strategic agents are present, their utilities can increase or decrease compared to when they are all honest. Small changes in the inter-agent correlations can cause such shifts. In contrast, the simpler one-strategic-agent setting explored in the literature lacks such complex patterns. Finally, we develop an algorithm by which new agents can learn the information needed for strategic behavior. Our algorithm works even when the (unknown) strategic agents deviate from the Nash-optimal strategies. We verify these results on both simulated networks and a real-world dataset on international trade.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
