Information Synergy Maximizes the Growth Rate of Heterogeneous Groups

TL;DR

This paper develops a general theory showing that groups maximizing information synergy through diverse signals and optimal cooperation strategies significantly enhance their growth in complex environments.

Contribution

It introduces a statistical framework for understanding how heterogeneous agents benefit from information pooling and synergy, advancing the prediction of cooperative group dynamics.

Findings

Groups with diverse signals maximize collective information.

Optimal cooperation strategies depend on information synergy.

Heterogeneous groups outperform homogeneous ones in growth rate.

Abstract

Collective action and group formation are fundamental behaviors among both organisms cooperating to maximize their fitness, and people forming socioeconomic organizations. Researchers have extensively explored social interaction structures via game theory and homophilic linkages, such as kin selection and scalar stress, to understand emergent cooperation in complex systems. However, we still lack a general theory capable of predicting how agents benefit from heterogeneous preferences, joint information, or skill complementarities in statistical environments. Here, we derive general statistical dynamics for the origin of cooperation based on the management of resources and pooled information. Specifically, we show how groups that optimally combine complementary agent knowledge about resources in statistical environments maximize their growth rate. We show that these advantages are quantified by the information synergy embedded in the conditional probability of environmental states given agents' signals, such that groups with a greater diversity of signals maximize their collective information. It follows that, when constraints are placed on group formation, agents must intelligently select with whom they cooperate to maximize the synergy available to their own signal. Our results show how the general properties of information underlie the optimal collective formation and dynamics of groups of heterogeneous agents across social and biological phenomena.