On the Empirical Relevance of the Transient in Opinion Models

TL;DR

This paper empirically tests an opinion exchange model by relating transient opinion profiles to real election data, demonstrating statistical agreement and emphasizing the importance of transient dynamics in opinion models.

Contribution

It introduces an empirical confirmation experiment linking opinion model transients to real election data, validating the model's relevance.

Findings

Transient opinion profiles show statistical agreement with real election data.

The model's dynamics can predict electoral performance during transient states.

Empirical validation emphasizes the importance of transient phases in opinion models.

Abstract

While the number and variety of models to explain opinion exchange dynamics is huge, attempts to justify the model results using empirical data are relatively rare. As linking to real data is essential for establishing model credibility, this Letter develops a empirical confirmation experiment by which an opinion model is related to real election data. The model is based on a representation of opinions as a vector of $k$ bits. Individuals interact according to the principle that similarity leads to interaction and interaction leads to still more similarity. In the comparison to real data we concentrate on the transient opinion profiles that form during the dynamic process. An artificial election procedure is introduced which allows to relate transient opinion configurations to the electoral performance of candidates for which data is available. The election procedure based on the well--established principle of proximity voting is repeatedly performed during the transient period and remarkable statistical agreement with the empirical data is observed.