Efficiency of emergent constraint satisfaction in small-world and random agent networks

Abstract

In this paper, we study the efficiency of emergent constraint satisfaction in small-world and random agent networks. We find that emergent constraint satisfaction in a small-world network is less efficient than in some other networks (e.g., regular networks). Further, we find that this finding holds in almost all random networks. Based on these observations, we study the relationship between the efficiency of emergent constraint satisfaction and the randomness of the corresponding network from which the constraint satisfaction problem is generated.