Self-adaptation in a network of social drivers: using random boolean networks

Abstract

One of the major research directions in adaptive and self-organizing systems is dedicated to learning how to coordinate decisions and actions. Also, it is important to understand whether individual agents' decisions can lead to globally optimal or at least acceptable solutions. Our long term approach aims at studying the effect of several types of strategies for self-organization of agents in complex systems. The present paper addresses simulation of agents' decision-making regarding route choice when random boolean networks are used as a formalism for mapping information coming from other agents into the decision-making process of each agent. It is thus assumed that these agents are part of a social network (for example acquaintances or work colleagues). Hence, part of the information necessary to decide can be provided by these acquaintances (small-world), or by route guidance systems. With this approach we target a system that adapts dynamically to changes in the environment, which, in this case, involves other adaptive decision-makers, a challenging endeavor. We compare our results to similar ones reported in the literature. Results show that the use of a relatively low number of boolean functions and few information from acquaintances leads the system to an equilibrium.