Collective decision making by embodied neural agents

TL;DR

This paper explores how simple neural dynamics and sensorimotor coordination among embodied agents influence collective decision-making, highlighting the importance of intra- and inter-agent coupling and environmental factors.

Contribution

It introduces a minimal neural dynamics model for embodied agents and demonstrates how coordination dynamics affect collective decisions in a multi-agent environment.

Findings

Success depends on intra-, inter-agent, and environment coupling.

Environmental factors influence decision difficulty.

Coordination dynamics impact collective behavior.

Abstract

Collective decision making using simple social interactions has been studied in many types of multi-agent systems, including robot swarms and human social networks. However, existing multi-agent studies have rarely modeled the neural dynamics that underlie sensorimotor coordination in embodied biological agents. In this study, we investigated collective decisions that resulted from sensorimotor coordination among agents with simple neural dynamics. We equipped our agents with a model of minimal neural dynamics based on the coordination dynamics framework, and embedded them in an environment with a stimulus gradient. In our single-agent setup, the decision between two stimulus sources depends solely on the coordination of the agent's neural dynamics with its environment. In our multi-agent setup, that same decision also depends on the sensorimotor coordination between agents, via their simple social interactions. Our results show that the success of collective decisions depended on a balance of intra-agent, inter-agent, and agent-environment coupling, and we use these results to identify the influences of environmental factors on decision difficulty. More generally, our results demonstrate the impact of intra- and inter-brain coordination dynamics on collective behavior, can contribute to existing knowledge on the functional role of inter-agent synchrony, and are relevant to ongoing developments in neuro-AI and self-organized multi-agent systems.