The Impact of Incomplete Information on Network Formation with Heterogeneous Agents

TL;DR

This paper introduces an agent-based network formation model under uncertainty that explores how incomplete information and biased beliefs influence segregation and network structures, extending traditional complete information models.

Contribution

It presents a novel model that incorporates beliefs and biases, demonstrating their impact on network formation and segregation, which was not addressed in prior complete information frameworks.

Findings

Biased beliefs significantly drive segregation in networks.

The model can generate networks similar to complete information models.

Biases lead to homophilous equilibria regardless of observable attributes.

Abstract

We propose an agent-based network formation model under uncertainty with the objective of relaxing the common assumption of complete information, calling attention to the role beliefs may play in segregation. We demonstrate that our model is capable of generating a set of networks that encompasses those of a complete information model. Further, we show that by allowing agents to be biased toward each other based on observable attributes, our model is able to generate homophilous equilibria with preferences that are indifferent to these attributes. We accompany our theoretical results with a simulation-based investigation of the relationship between beliefs and segregation and show that biased beliefs are an important driver of segregation under incomplete information.