Causal Entropy, Control and Leadership Dynamics

TL;DR

This paper introduces a model of collective animal behavior based on Future State Maximisation, showing how preferences and alignment influence swarm cohesion and fragmentation, and identifying optimal coupling for group stability.

Contribution

It combines FSM with classical flocking rules to model decentralized coordination and identifies conditions affecting swarm cohesion and fragmentation.

Findings

Biased agents can cause swarm fragmentation depending on preference strength and difference.

An optimal coupling strength exists that maximizes flock cohesion.

FSM effectively models intelligent collective behavior when integrated with classical rules.

Abstract

Collective motion in animal groups provide examples of emergent, decentralised coordination. Here, we examine a bottom-up model of collective behavior based on Future State Maximisation (FSM). In this model agents seek to maximise the diversity of their future visual states over a finite time horizon. We further assume that a subset of agents have a directional bias, e.g. towards different destinations. We observe swarm fragmentation on increasing (i) the strength of these preferences, or (ii) the difference in preferred directions, or (iii) the number of biased agents. Depending on these factors, biased agents can leave the swarm alone, leaving behind all other agents, or they can entrain some fraction of the group to leave with them. We further study the role of a classical nearest-neighbor alignment term on cohesion. Notably, we identify the existence of an finite, optimal coupling strength that suppresses fragmentation and maximises the flock cohesion. Our results demonstrate that FSM can be successfully combined with classical flocking rules, offering a flexible framework for modeling intelligent collective systems.