Collective Problem Solving: Functionality beyond the Individual

Following a non-reductionist approach to the explanation of higher functionality observed in collective problem solvers, a simple agent-based model is used to “solve” a sequential problem a maze. Larger collectives of the individual agents are observed in the simulations to locate a minimal path, even though the agents are non-interacting, have no global perception of the maze and use rules that do not include logic for finding a shorter path. The convergence to an optimal path is argued to be a demonstration of both an emergent problem formulation and emergent problem solution. Furthermore, many of the dynamics and properties of cooperating collectives are observed: performance of the collective greater than that of the average individual, reduced performance with less diversity, ability to function in the presence of extreme noise and information loss, improved collective performance with established individual problem solvers, path sensitivity to individual contributions but limited sensitivity of group performance, and others. The implications of the results to the formation of self-organizing knowledge and decision making systems are discussed.