Emergence of collective memories

TL;DR

This paper explores how individual memories of historical events form collective memories through social communication, using network analysis and agent-based modeling to understand the dynamics and structure of shared histories.

Contribution

It introduces a network-based approach combined with agent-based modeling to study the emergence and convergence of collective memories from individual memory webs.

Findings

Memory webs exhibit specific degree distributions and community structures.

Communication can lead to convergence of individual memories into collective memories.

The number of distinct memory groups may be limited by population size.

Abstract

We understand the dynamics of the world around us as by associating pairs of events, where one event has some influence on the other. These pairs of events can be aggregated into a web of memories representing our understanding of an episode of history. The events and the associations between them need not be directly experienced-they can also be acquired by communication. In this paper we take a network approach to study the dynamics of memories of history. First we investigate the network structure of a data set consisting of reported events by several individuals and how associations connect them. We focus our measurement on degree distributions, degree correlations, cycles (which represent inconsistencies as they would break the time ordering) and community structure. We proceed to model effects of communication using an agent-based model. We investigate the conditions for the memory webs of different individuals to converge to collective memories, how groups where the individuals have similar memories (but different from other groups) can form. Our work outlines how the cognitive representation of memories and social structure can co-evolve as a contagious process. We generate some testable hypotheses including that the number of groups is limited as a function of the total population size.