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分类依据与纠错

AI 根据所提供的论文材料归类:摘要以信息网络上的战略互动及行动的局部随机依赖为核心。

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代码、数据与补充材料

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关注这篇论文的作者
Nathan Canen
Jacob Schwartz
Kyungchul Song

观察邻居的众多个体之间的局部互动估计

研究问题
当研究者只观察大型网络中的部分个体,而个体也不了解完整信息网络时,如何建立可计算的互动模型并进行推断?
方法与思路
作者让个体基于已观察的邻居类型和简单策略信念采取线性最佳反应,推导局部收益相互依赖与行动随机依赖的联系。
材料陈述的贡献
摘要称给出信息网络与信念条件,使最佳反应具有显式形式,并支持无需观察所有玩家或掌握精确抽样过程的渐近推断。
阅读关注点
应核对信息网络和信念的限制、局部依赖如何控制,以及部分网络观测下参数识别与渐近推断所需的抽样条件。
阅读边界
所给 Crossref 摘要未列出具体网络和信念条件;不需精确抽样过程不代表任意抽样均有效,也不推断刊本变化。

原文摘要

In various economic environments, people observe other people with whom they strategically interact. We can model such information‐sharing relations as an information network, and the strategic interactions as a game on the network. When any two agents in the network are connected either directly or indirectly in a large network, empirical modeling using an equilibrium approach can be cumbersome, since the testable implications from an equilibrium generally involve all the players of the game, whereas a researcher's data set may contain only a fraction of these players in practice. This paper develops a tractable empirical model of linear interactions where each agent, after observing part of his neighbors' types, not knowing the full information network, uses best responses that are linear in his and other players' types that he observes, based on simple beliefs about the other players' strategies. We provide conditions on information networks and beliefs such that the best responses take an explicit form with multiple intuitive features. Furthermore, the best responses reveal how local payoff interdependence among agents is translated into local stochastic dependence of their actions, allowing the econometrician to perform asymptotic inference without having to observe all the players in the game or having to know the precise sampling process.

笔记与分组
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