Feedback dynamics in matching networks drive behavioral differentiation despite overlapping objectives

2026-06-30

AI Summary

This study addresses the puzzling prevalence of selective asymmetry in two-sided social networks, even when agents on both sides exhibit nearly identical preference distributions. The authors propose a repeated one-to-one matching model in which individuals dynamically adjust their acceptance thresholds based on observed match success rates. Through analytical derivation and numerical simulation, they demonstrate for the first time that endogenous feedback within the matching process can spontaneously generate a stable equilibrium characterized by pronounced selectivity asymmetry—where one side becomes highly selective while the other accepts almost all proposals—even when initial target compatibility distributions are virtually indistinguishable. This unique equilibrium emerges robustly across contexts such as online dating, labor markets, and housing platforms, yielding several empirically testable predictions.

Abstract

Many bipartite social networks exhibit pronounced asymmetries in selectivity and matching opportunities: members of one side can afford to be highly selective, while members of the opposite side are forced to accept less desirable matches. While it is natural to try to explain this asymmetry in terms of the intrinsic characteristics of the two sides or other exogenous factors, here we show that such asymmetries can also emerge endogenously through a feedback process generated by the matching process itself: as one side becomes more selective, the other side is pushed to be less selective due to reduced matching opportunities, and vice versa. We develop a model in which individuals repeatedly form one-to-one matches across two groups and adapt their selectivity to achieve a target matching rate. Using both analytic and numerical methods, we show that when encounters are sufficiently frequent, the unique equilibrium is for one group to be highly selective and the other non-selective. This qualitative outcome holds even for heterogeneous groups with overlapping, almost indistinguishable distributions of target matching rates. The model makes several testable predictions, and it provides a mechanism for behavioral differentiation in repeated matching environments, with applications ranging from online dating to hiring and housing markets.

Problem

• matching networks
• behavioral differentiation
• selectivity asymmetry
• feedback dynamics
• bipartite social networks

Innovation

• feedback dynamics
• matching networks
• behavioral differentiation
• endogenous asymmetry
• selectivity adaptation