Modeling the emergence of signal-based collective behavior

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Collective behaviors is found everywhere in nature, from birds flocking, fish schooling, ants foraging, fireflies synchronizing, to humans self-organizing in societies. Not only are all these examples of entities entering a higher-level of organization, but the way they reach this state is most interestingly done without any leader or central control. Instead, emergent swarming patterns are based on simple, local, individual decision making. Identifying the minimal features of biologically-inspired interacting agents leading to the emergence of such behavior is fundamental to our understanding of collective behavior, for many disciplines of research ranging from physics to chemistry, to biology, to artificial intelligence.

The literature of the last 20 years is saturated with mathematical models of swarming behavior that rely on a third-person controller following static rules of interaction, or imposed fixed leaders within the group. But few people seem to take a minimalistic modelling approach, simulating what emerges from agents uniquely exchanging simple signals between each other. I decided we could do better. I therefore coded up a computer simulation of agents navigating in a 3D world, guided by artificial neural networks. In the virtual environment, in order to survive, each agent must look for a resource it cannot detect directly. Instead, each agent senses signals produced by other agents in the neighborhood and processes them through its own artificial neural network and can modify its velocity and its own signal accordingly.

After a few dozens of generations, the simulation produces agents which collectively move together like a natural swarm. But the most surprising was how these dynamics allowed agents to be more efficient at reaching and sticking to resource areas, while still being unable to detect them directly. Once the collective behavior emerges and spreads to the whole population, it naturally leads to a genetic drift of the agents’ genotypes. I would like to suggest that this model presents dynamics favorable to the study of relaxed selection, which would be a natural next step for the simulations to develop into. This piece of work on emergent collective strategies for uninformed search in complex systems is an example of simulation-based approach, paving the way for future research on the origins of signaling and efficient collective patterns in evolution.

Computational modeling offers the significant advantage that the information flows between individuals — in an information theoretic sense — can be thoroughly computed within the simulation. These measures can typically lead to compelling insights on which properties lead to transitions to the next level of organization: namely the emergence of a cognitive swarm or, in a more general way, the transition from a population of solitary individuals to a collective society.

Link to the publication: http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0152756

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