Paradoxes in the co-evolution of contagions and institutions

TL;DR

This paper explores how local institutions influence epidemic spread, revealing paradoxes where increased transmission can reduce outbreaks and slower institutional responses can sometimes be beneficial, highlighting complex co-evolutionary dynamics.

Contribution

It introduces a novel model combining institutional dynamics with epidemic spread, uncovering paradoxical effects and complex co-evolutionary behaviors not captured by traditional models.

Findings

Higher transmission rates can lead to smaller outbreaks.

Slower institutional adaptation can sometimes reduce outbreak size.

Perceived threat levels influence policy investments and endemicity.

Abstract

Epidemic models study the spread of an undesired agent through a population, be it infectious diseases through a country, misinformation in online social media, or pests infesting a region. In combating these epidemics, we rely neither on global top-down interventions, nor solely on individual adaptations. Instead, interventions most commonly come from local institutions such as public health departments, moderation teams on social media platforms, or other forms of group governance. Classic models, which are often individual or agent-based, are ill-suited to capture local adaptations. We leverage recent development of institutional dynamics based on cultural group selection to study how groups can attempt local control of an epidemic by taking inspiration from the successes and failures of other groups. Incorporating these institutional changes into the epidemic dynamics reveals paradoxes: a higher transmission rate can result in smaller outbreaks and decreasing the speed of institutional adaptation generally reduces outbreak size. When groups perceive a contagion as more worrisome, they can invest in improved policies and, if they maintain these policies long enough to have impact, lead to a reduction in endemicity. By looking at the interplay between the speed of institutions and the transmission rate of the contagions, we find rich co-evolutionary dynamics that reflect the complexity of known biological and social contagions.