Endogenous Network Structures with Precision and Dimension Choices

TL;DR

This paper develops a model where agents' choices of signal precision and learning dimensions determine the endogenous network structure, revealing sublinear optimal precision and the importance of influence distribution for social learning efficiency.

Contribution

It introduces a framework where network structures are endogenously formed through agents' signal and dimension choices, linking individual decisions to social network outcomes.

Findings

Optimal precision is sublinear in stationary influence.

Individual optimal choices are worse than social optimum by a factor of n^{1/3}.

A kernel distance defines influence distribution, favoring equal influence for efficient learning.

Abstract

This paper presents a social learning model where the network structure is endogenously determined by signal precision and dimension choices. Agents not only choose the precision of their signals and what dimension of the state to learn about, but these decisions directly determine the underlying network structure on which social learning occurs. We show that under a fixed network structure, the optimal precision choice is sublinear in the agent's stationary influence in the network, and this individually optimal choice is worse than the socially optimal choice by a factor of $n^{1/3}$. Under a dynamic network structure, we specify the network by defining a kernel distance between agents, which then determines how much weight agents place on one another. Agents choose dimensions to learn about such that their choice minimizes the squared sum of influences of all agents: a network with equally distributed influence across agents is ideal.