Exploring the Dimensions of Convention Emergence in Multiagent Systems

Social conventions are useful self-sustaining protocols for groups to coordinate behavior without a centralized entity enforcing coordination. The emergence of such conventions in different multi agent network topologies has been investigated by several researchers, although exploring only specific cases of the convention emergence process. In this work we will provide multi-dimensional analysis of several factors that we believe determines the process of convention emergence, such as: the size of agents memory, the population size and structure, the learning approach taken by agents, the amount of players in the interactions, or the convention search space dimension. Although we will perform an exhaustive study of different network structures, we are concerned that different topologies will affect the emergence in different ways. Therefore, the main research question in this work is comparing and studying effects of different topologies on the emergence of social conventions. While others have investigated memory for learning algorithms, the effects of memory on the reward have not been investigated thoroughly. We propose a reward metric that is derived directly from the history of the interacting agents. Another research question to be answered is what effect does the history based reward function and the learning approach have on convergence time in different topologies. Experimental results show that all the factors analyzed affect differently the convention emergence process, being such information very useful for policy-makers when designing self-regulated systems.