Batched Adaptive Network Formation
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AI 根据所提供的论文材料归类：摘要直接研究带潜在类型和成对互补性的网络形成，通过自适应匹配优化连接总产出，明确对应网络经济计量方向。
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分批自适应网络形成
- 研究问题
- 作者研究如何随数据逐批积累学习个体潜在类型及类型互补性，并通过自适应网络形成提高所有连接权重之和。
- 方法与思路
- 作者结合加权随机区块模型与分批多臂老虎机，以变分估计形成用于贝叶斯更新的近似正态信号，再通过最大权重匹配选择下一批网络。
- 材料陈述的贡献
- 摘要称，建立分批局部渐近正态性及极大似然与变分估计的渐近等价，模拟显示产出改善、参数估计变准，并适应变化的个体池。
- 阅读关注点
- 应核对潜在类型和互补性的识别、分批渐近框架、变分等价的条件、匹配约束，以及探索与利用如何影响学习和网络产出。
- 阅读边界
- 依据 arXiv 预印本摘要，同行评审或期刊接收未核实；摘要未给出遗憾界或非平稳理论，相关模拟不能证明一般非平稳有效性。
原文摘要
Networks are central to many economic and organizational applications, including workplace team formation, social platform recommendations, and classroom friendship development. In these settings, networks are modeled as graphs, with agents as nodes, agent pairs as edges, and edge weights capturing pairwise production or interaction outcomes. This paper develops an adaptive, or \textit{online}, policy that learns to form increasingly effective networks as data accumulates over time, progressively improving total network output measured by the sum of edge weights. Our approach builds on the weighted stochastic block model (WSBM), which captures agents' unobservable heterogeneity through discrete latent types and models their complementarities in a flexible, nonparametric manner. We frame the online network formation problem as a non-standard \textit{batched multi-armed bandit}, where each type pair corresponds to an arm, and pairwise reward depends on type complementarity. This strikes a balance between exploration -- learning latent types and complementarities -- and exploitation -- forming high-weighted networks. We establish two key results: a \textit{batched local asymptotic normality} result for the WSBM and an asymptotic equivalence between maximum likelihood and variational estimates of the intractable likelihood. Together, they provide a theoretical foundation for treating variational estimates as normal signals, enabling principled Bayesian updating across batches. The resulting posteriors are then incorporated into a tailored maximum-weight matching problem to determine the policy for the next batch. Simulations show that our algorithm substantially improves outcomes within a few batches, yields increasingly accurate parameter estimates, and remains effective even in nonstationary settings with evolving agent pools.