A Behavioral Micro-foundation for Cross-sectional Network Models
For researchers using cross-sectional network models, this provides a theoretical grounding linking static models to underlying behavioral processes, though the contribution is incremental as it builds on prior work.
The paper develops a behavioral micro-foundation for cross-sectional network models using a continuous-time stochastic choice mechanism, showing that equilibrium behavior can be expressed in exponential family form, enabling preference estimation with existing methods. The approach is illustrated on friendship networks and phase transitions in small groups.
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.