Higher-order adaptive behaviors outperform pairwise strategies in mitigating contagion dynamics

TL;DR

This paper demonstrates that adaptive behaviors based on higher-order group interactions are more effective and socially efficient in controlling contagion spread than those based on pairwise interactions, through simulations and analytic models.

Contribution

It introduces a comparison of higher-order versus pairwise adaptive behaviors in contagion dynamics, highlighting the superior effectiveness of higher-order information-based strategies.

Findings

Higher-order adaptive behaviors better limit contagion spread.

They reduce social costs by decreasing interaction intensity.

Heterogeneous risk perception emerges from higher-order information.

Abstract

When exposed to a contagion phenomenon, individuals may respond to the perceived risk of infection by adopting behavioral changes, aiming to reduce their exposure or their risk of infecting others. The social cost of such adaptive behaviors and their impact on the contagion dynamics have been investigated in pairwise networks, with binary interactions driving both contagion and risk perception. However, contagion and adaptive mechanisms can also be driven by group (higher-order) interactions. Here, we consider several adaptive behaviors triggered by awareness of risk perceived through higher-order and pairwise interactions, and we compare their impact on pairwise and higher-order contagion processes. By numerical simulations and a mean-field analytic approach, we show that adaptive behaviors driven by higher-order information are more effective in limiting the spread of a contagion, than similar mechanisms based on pairwise information. Meanwhile, they also entail a lower social cost, measured as the reduction of the intensity of interactions in the population. Indeed, adaptive mechanisms based on higher-order information lead to a heterogeneous risk perception within the population, producing a higher alert on nodes with large hyperdegree (i.e., participating in many groups), on their neighborhoods, and on large groups. This in turn prevents the spreading process to exploit the properties of these nodes and groups, which tend to drive and sustain the dynamics in the absence of adaptive behaviors.