Information exchange, meaning and redundancy generation in anticipatory systems: self-organization of expectations -- the case of Covid-19

TL;DR

This paper explores how information exchange and meaning generation influence the self-organization and evolution of complex systems, applying a Triple Helix model to Covid-19 propagation to link information dynamics with observable infection patterns.

Contribution

It introduces a novel information-based model of system evolution, integrating meaning and redundancy generation, and applies it to infectious disease dynamics, specifically Covid-19.

Findings

Model predictions align with observed Covid-19 infection data.

Information processing and meaning generation influence system evolution.

Triple Helix model can be adapted beyond innovation to epidemiology.

Abstract

When studying the evolution of complex systems one refers to model representations comprising various descriptive parameters. There is hardly research where system evolution is described on the base of information flows in the system. The paper focuses on the link between the dynamics of information and system evolution. Information, exchanged between different system's parts, before being processed is first provided with meaning by the system. Meanings are generated from the perspective of hindsight, i.e. against the arrow of time. The same information can be differently interpreted by different system's parts (i,e,provided with different meanings) so that the number of options for possible system development is proliferated. Some options eventually turn into observable system states. So that system evolutionary dynamics can be considered as due to information processing within the system. This process is considered here in a model representation. The model under study is Triple Helix (TH) model, which was earlier used to describe interactions between university, industry and government to foster innovations. In TH model the system is comprised of three interacting parts where each part process information ina different way. The model is not limited to the sphere of innovation and can be used in a broader perspective. Here TH is conceptualized in the framework of three compertment model used to describe infectious disease. The paper demonstrates how the dynamics of information and meaning can be incorporated in the description of Covid-19 infectious propagation. The results show correspondence of model predictions with observable infection dynamics.