Capturing collective conflict dynamics with sparse social circuits

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.

💡 Research Summary

This paper discusses a set of computational techniques called Inductive Game Theory for extracting strategic decision-making rules from time series data and constructing probabilistic social circuits. The authors propose an inductive approach to reconstructing the edges by connecting component individuals and groups with strategies within a game context. They demonstrate this method using conflict behavior among pigtailed macaques, identifying significant patterns in individual-level decision-making that can be mapped to collective behaviors at the group level.

The paper extends previous work on Inductive Game Theory by more efficiently searching through possible strategy spaces by grouping individuals into socially relevant sets. This approach allows for a more efficient and parsimonious specification of underlying interactions between components. Additionally, the authors discuss how they reduce the dimensionality of these circuits using coarse-graining or compression techniques to build cognitive effective theories for collective behavior.

The demonstration with pigtailed macaques provides insights into how individual-level behaviors can be linked to macroscopic features of a system that were not initially specified in constructing the initial circuit. This research highlights advancements in extracting strategic decision-making rules from time series data, which holds potential applications across various social contexts and could lead to deeper understandings of collective behavior dynamics.