Capturing collective conflict dynamics with sparse social circuits

TL;DR

This paper introduces Inductive Game Theory techniques to extract decision-making rules from time series data, constructing social circuits that link individual strategies to group-level conflict dynamics in macaques.

Contribution

It presents an efficient, parsimonious method for reconstructing social interaction circuits and mapping individual behaviors to collective conflict patterns.

Findings

Identified significant decision-making patterns in macaque conflict behavior.

Constructed probabilistic social circuits linking individual strategies to group dynamics.

Reduced circuit complexity through coarse-graining to develop effective theories.

Abstract

We discuss a set of computational techniques, called Inductive Game Theory, for extracting strategic decision-making rules from time series data and constructing probabilistic social circuits. We construct these circuits by connecting component individuals and groups with strategies in a game and propose an inductive approach to reconstructing the edges. We demonstrate this approach with conflict behavior in a society of pigtailed macaques by identifying significant patterns in decision-making by individuals. With the constructed circuit, we then capture macroscopic features of the system that were not specified in the construction of the initial circuit, providing a mapping between individual level behaviors to collective behaviors over the scale of the group. We extend on previous work in Inductive Game Theory by more efficiently searching the space of possible strategies by grouping individuals into socially relevant sets to produce a more efficient, parsimonious specification of the underlying interactions between components. We discuss how we reduce the dimensionality of these circuits using coarse-graining or compression to build cognitive effective theories for collective behavior.