An Evolutionary Analysis of Narrative Selection

TL;DR

This paper examines how different narrative selection strategies influence belief updating and population dynamics within an evolutionary framework, revealing conformist advantages and the impact of uncertainty on belief extremity.

Contribution

It introduces an evolutionary model incorporating heterogeneous narrative selection criteria and analyzes their effects on belief evolution and population composition.

Findings

Conformist agents have an evolutionary advantage across all scenarios.

Agents with mild beliefs perform better under high uncertainty.

Agents with extreme beliefs perform better when uncertainty is low.

Abstract

We study the performance of different methods for processing information, incorporating narrative selection within an evolutionary model. All agents update their beliefs according to Bayes' Rule, but some strategically choose the narrative they use in updating according to heterogeneous criteria. We simulate the endogenous composition of the population, considering different laws of motion for the underlying state of the world. We find that conformists -- that is, agents that choose the narrative to conform to the average belief in the population -- have an evolutionary advantage over other agents across all specifications. The survival chances of the remaining types depend on the uncertainty regarding the state of the world. Agents who tend to develop mild beliefs perform better when the uncertainty is high, whereas agents who tend to develop extreme beliefs perform better when the uncertainty is low.