Tractable Identification of Strategic Network Formation Models with Unobserved Heterogeneity

2026-03-09

AI Summary

This study addresses the identification challenge arising from the coexistence of strategic interdependence in link formation and unobserved individual heterogeneity in strategic network formation models. By employing a “bounding-by-c” approach, the authors treat endogenous covariates as random variables and leverage monotonicity constraints together with subnetwork structures—such as tetrads, triads, and weighted cycles—to construct identification conditions that eliminate or partially difference out individual fixed effects. This work establishes, for the first time, point or partial identification of structural parameters while simultaneously preserving strategic interactions and unobserved heterogeneity. The authors derive a complete system of identification constraints and demonstrate through simulations that the proposed method yields informative bounds for the structural parameters.

Technology Category

• Reasoning under Uncertainty: Causality
• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Structured Learning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

We develop a tractable identification approach for strategic network formation models with both strategic link interdependence and individual unobserved heterogeneity (fixed effects). The key challenge is that endogenous network statistics (e.g. number of common friends) enter the link formation equation, while the mapping from model primitives to equilibrium network structure is generally intractable. Our approach sidesteps this difficulty using a ``bounding-by-$c$''technique that treats endogenous covariates as random variables and exploits monotonicity restrictions to obtain identifying information. We derive a system of identifying restrictions based on subnetwork configurations: tetrad-based restrictions that completely eliminate all individual fixed effects, triad-based restrictions that partially difference out fixed effects, and general weighted cycle-based restrictions, along with point identification results. Preliminary simulations show that our approach can deliver informative bounds on the structural parameters.

Problem

• strategic network formation
• unobserved heterogeneity
• identification
• endogenous network statistics
• fixed effects

Innovation

• strategic network formation
• unobserved heterogeneity
• tractable identification
• bounding-by-c
• endogenous network statistics