Evolution of honesty in higher-order social networks

TL;DR

This paper investigates how the structure of higher-order social networks influences the evolution of honesty and deception, revealing complex dynamics and conditions under which moral behavior persists.

Contribution

It introduces a model of group interactions among receivers in social networks, extending sender-receiver games to higher-order interactions, and analyzes their impact on honesty evolution.

Findings

Honesty can persist despite temptation to lie under certain conditions.

Moral strategies can dominate even when lies benefit the receiver at a cost to the sender.

Results are robust across simulations with real-world hypergraph data.

Abstract

Sender-receiver games are simple models of information transmission that provide a formalism to study the evolution of honest signaling and deception between a sender and a receiver. In many practical scenarios, lies often affect groups of receivers, which inevitably entangles the payoffs of individuals to the payoffs of other agents in their group, and this makes the formalism of pairwise sender-receiver games inapt for where it might be useful the most. We therefore introduce group interactions among receivers, and study how their interconnectedness in higher-order social networks affects the evolution of lying. We observe a number of counterintuitive results that are rooted in the complexity of the underlying evolutionary dynamics, which has thus far remained hidden in the realm of pairwise interactions. We find conditions for honesty to persist even when there is a temptation to lie, and we observe the prevalence of moral strategy profiles even when lies favor the receiver at a cost to the sender. We confirm the robustness of our results by further performing simulations on hypergraphs created from real-world data using the SocioPatterns database. Altogether, our results provide persuasive evidence that moral behaviour may evolve on higher-order social networks, at least as long as individuals interact in groups that are small compared to the size of the network.