Statistical inference in social networks: how sampling bias and uncertainty shape decisions

TL;DR

This paper explores how sampling bias and uncertainty influence individuals' expectations and decision-making in social networks, affecting equilibrium outcomes in strategic interactions.

Contribution

It introduces a model analyzing the impact of sampling bias and uncertainty on social inference and equilibrium behavior, considering agent sophistication.

Findings

Sampling bias from the friendship paradox affects perceptions of social connectedness.

Uncertainty from small samples influences expectations and strategic decisions.

Agent sophistication moderates the effects of sampling bias on equilibrium outcomes.

Abstract

We investigate how individuals form expectations about population behavior using statistical inference based on observations of their social relations. Misperceptions about others' connectedness and behavior arise from sampling bias stemming from the friendship paradox and uncertainty from small samples. In a game where actions are strategic complements, we characterize the equilibrium and analyze equilibrium behavior. We allow for agent sophistication to account for the sampling bias and demonstrate how sophistication affects the equilibrium. We show how population behavior depends on both sources of misperceptions and illustrate when sampling uncertainty plays a critical role compared to sampling bias.