Two-Step Estimation of a Strategic Network Formation Model with Clustering
This paper explores strategic network formation under incomplete information using data from a single large network. We allow the utility function to be nonseparable in an individual's link choices to capture the spillover effects from friends in common. In a network with n individuals, an individual with a nonseparable utility function chooses between 2^{n-1} overlapping portfolios of links. We develop a novel approach that applies the Legendre transform to the utility function so that the optimal link choices can be represented as a sequence of correlated binary choices. The link dependence that results from the preference for friends in common is captured by an auxiliary variable introduced by the Legendre transform. We propose a two-step estimator that is consistent and asymptotically normal. We also derive a limiting approximation of the game as n grows large that simplifies the computation in large networks. We apply these methods to favor exchange networks in rural India and find that the direction of support from a mutual link matters in facilitating favor provision.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringVocal Bursts Valence PredictionSimilar Papers 제목 키워드 기반
A3S: A General Active Clustering Method with Pairwise Constraints
Active clustering aims to boost the clustering performance by integrating human-annotated pairwise constraints through strategic querying. Conventional approaches with semi-supervised clustering schemes encounter high qu…
ClusteringStrategic Federated Learning: Application to Smart Meter Data Clustering
Federated learning (FL) involves several clients that share with a fusion center (FC), the model each client has trained with its own data. Conventional FL, which can be interpreted as an estimation or distortion-based a…
ClusteringFederated LearningSchedulingSynthStrategy: Extracting and Formalizing Latent Strategic Insights from LLMs in Organic Chemistry
Modern computer-assisted synthesis planning (CASP) systems show promises at generating chemically valid reaction steps but struggle to incorporate strategic considerations such as convergent assembly, protecting group mi…
Regularized Non-negative Spectral Embedding for Clustering
Spectral Clustering is a popular technique to split data points into groups, especially for complex datasets. The algorithms in the Spectral Clustering family typically consist of multiple separate stages (such as simila…
ClusteringHandling missing data in model-based clustering
Gaussian Mixture models (GMMs) are a powerful tool for clustering, classification and density estimation when clustering structures are embedded in the data. The presence of missing values can largely impact the GMMs est…
ClusteringData AugmentationDensity EstimationGeneral Classification+3