When Models Interact with their Subjects: The Dynamics of Model Aware Systems

TL;DR

This paper investigates how models that influence their subjects can lead to dynamic behaviors like convergence or oscillation, with implications for understanding scientific and social systems.

Contribution

It introduces two models of model aware systems, analyzing their dynamics and comparing simulations with real-world data, revealing universal behaviors.

Findings

Model aware systems can exhibit convergent or oscillatory dynamics.

Universal 1/f noise observed in system behaviors.

Simulation results align with empirical publication data.

Abstract

A scientific model need not be a passive and static descriptor of its subject. If the subject is affected by the model, the model must be updated to explain its affected subject. In this study, two models regarding the dynamics of model aware systems are presented. The first explores the behavior of "prediction seeking" (PSP) and "prediction avoiding" (PAP) populations under the influence of a model that describes them. The second explores the publishing behavior of a group of experimentalists coupled to a model by means of confirmation bias. It is found that model aware systems can exhibit convergent random or oscillatory behavior and display universal 1/f noise. A numerical simulation of the physical experimentalists is compared with actual publications of neutron life time and {\Lambda} mass measurements and is in good quantitative agreement.