We introduce a simulation environment to facilitate research into emergent collective behaviour, with a focus on replicating the dynamics of ant colonies. By leveraging real-world data, the environment simulates a target ant trail that a controllable agent must learn to replicate, using sensory data observed by the target ant. This work aims to contribute to the neuroevolution of models for collective behaviour, focusing on evolving neural architectures that encode domain-specific behaviours in the network topology. By evolving models that can be modified and studied in a controlled environment, we can uncover the necessary conditions required for collective behaviours to emerge. We hope this environment will be useful to those studying the role of interactions in emergent behaviour within collective systems.

This study applies the information-theoretic measure of Non- Trivial Information Closure (NTIC) to quantify the autonomy of individual ants within a colony. We calculate the degree to which an ant’s future behavior is determined by its own past states versus being influenced by its local environment. Results show that individual ants exhibit consistent levels of autonomy across different timescales. This suggests that ant behavior reflects a non-trivial processing of both internal and external information, rather than being a simple reflexive response to stimuli. The approach demonstrates the utility of NTIC as a metric for assessing autonomy in complex biological systems. These findings lay the groundwork for future studies of autonomy and information processing in swarms.