Emergence is a property often claimed to apply to complex systems on multiple levels of organization: individual behavior emerges from underlying neural activity and social patterns – from constituent behaviors of the individuals. Furthermore, the emergent level is typically characterized as possessing autonomy from the lower-level phenomena and as exerting downward causation on them. In this study, we investigate such a multi-level emergence in the context of a single simple task. We evolve agents controlled by a small neural network to travel information. We then compute measures of emergence stemming from an approach known as Integrated Information Decomposition. Results are presented for both the final behavior and the evolutionary changes that led to it.

We evolve artificial agents to perform a simple tracking task in three conditions: one individual (Isolated Condition) and two joint action conditions with division of labor. The joint conditions differ by whether two agents switch complementary roles during the task (Generalist Condition) or always play the same role (Specialist Condition). At the end of evolutionary runs we calculate the agents’ neural complexity using Tononi-Sporns-Edelman (TSE) complexity measure which relates to Integrated Information Theory (IIT). We show that (1) division of labor with specialization leads to a level of neural complexity comparable to the complexity of performing the same task alone, and that (2) both are lower than neural complexity when performing the task jointly with role switching. We further consider viewing collaborating agents as a single extended system and calculate its joint neural complexity. We demonstrate that contrary to our predictions, the same pattern of results, i.e., Generalists’ complexity being higher than Specialists’, holds also in this conceptualization.