Efficiency of the Emergence of Consensus in Complex Networks - assessing force influence

Interactions between agents in multi-agent systems have an inherent potential for conflict and must be coordinated. The adhesion to uniform behaviour is one way of coordinating actions: social conventions and lexicons are good examples of coordinating systems, where uniformity promotes shared expectations of behaviour and shared meanings. This paper deals with the emergence of a uniform collective choice, a consensus, inside a population of artificial agents. The efficiency of the formation of a uniform shared decision or choice is an important issue. The nature of interactions and also the nature of society configurations may promote or inhibit consensual emergence. We study the efficiency of uniform decision formation along two dimensions: rules of interaction and network topologies. We compare two different interaction behaviours: the well know and used Highest Cumulative Reward, which is a kind of reinforcement learning behaviour, against a recent behaviour named Recruitment based on Force. We also compare those interaction behaviours along five types of social link networks: fully connected, regular, random, scale-free and small-world.