A Behavioral Micro-foundation for Cross-sectional Network Models

2026-05-04

AI Summary

This study establishes a microfoundational link between cross-sectional network models and their underlying generative behavioral mechanisms. Building on a continuous-time stochastic choice framework, it proposes a general modeling approach that accommodates non-network actors and multilateral relational constraints, yielding an exponential-family representation under equilibrium conditions to facilitate individual preference estimation. The key innovation lies in the natural decomposition of graph potential into a preference component reflecting agent utilities and an entropy component encoding tie-formation rules, thereby unifying behavioral and statistical network modeling paradigms. The framework is successfully applied to analyze friendship networks in professional organizations and to model structural phase transitions in small groups, demonstrating its empirical validity and broad applicability.

Abstract

Models for cross-sectional network data have become increasingly well-developed in recent decades, and are widely used. This has led to a growing interest in the connection between such cross-sectional models and the behavioral processes from which the corresponding networks were presumably generated. Here, we build on prior work in this area to present a behavioral micro-foundation for cross-sectional network models, based on a continuous time stochastic choice mechanism, that can accommodate highly general classes of cases (including agents who are not themselves in the network, and multilateral edge control). As we show, the equilibrium behavior of this process under appropriate conditions can be expressed in exponential family form, allowing estimation of individual preferences using existing methods; the graph potential separates naturally into a preference-based term reflecting agent utilities, and an entropic term reflecting the rules of tie formation. We illustrate our approach via an analysis of friendship in a professional organization, and modeling of phase transitions in the structure of small groups.

Problem

• cross-sectional network models
• behavioral micro-foundation
• network formation
• stochastic choice
• exponential family

Innovation

• behavioral micro-foundation
• cross-sectional network models
• continuous-time stochastic choice
• exponential family
• graph potential