网络阈值博弈
Network Threshold Games
AI总结：
本文提出网络变换吸收阈值异质性，用k-核刻画网络阈值博弈的均衡，并证明线性二次偏好下序贯博弈具有唯一子博弈完美均衡。
摘要：
本文提出了一种新的视角来研究在阈值异质情况下网络上的阈值博弈。在这类博弈中，每个智能体有两个可选行动，并且当且仅当足够多的邻居选择行动1时，该智能体才偏好行动1。我们提出了一种网络变换，将阈值的异质性“吸收”进网络结构中。这使得我们能够通过变换后网络的k-核——一种被广泛研究的网络凝聚性度量——来刻画均衡。我们的模型也是当行动为0或1时Ballester等人（2006）经典模型的直接类比。此外，我们的二元行动版本展现出显著的稳定性性质。当智能体具有线性二次偏好时，无论智能体以何种顺序行动，变换后网络的k-核都能刻画该博弈序贯行动版本的唯一子博弈完美均衡。
英文摘要：
This paper proposes a new lens for studying threshold games played on networks when the thresholds are heterogeneous. These are games where agents have two possible actions, and prefer action 1 if and only if enough of their neighbours choose action 1. We propose a transformation of the network that `absorbs' the heterogeneity in thresholds into the network. This allows us to characterise equilibria in terms of the k-core -- a well-studied measure of network cohesiveness -- of the transformed network. Our model is also the direct analogy to the workhorse model of Ballester et al. (2006) when actions are 0 or 1. Further, our binary action version exhibits a remarkable stability property. When agents have linear-quadratic preferences, the k-core of the transformed network characterises the unique subgame perfect equilibrium of a sequential move version of the game -- no matter what order agents move in.
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。
