Abstract:

Social conventions are useful self-sustaining protocols for groups to
coordinate behavior without a centralized entity enforcing
coordination. We perform an in-depth study of different network
structures, to compare and evaluate the effects of different network
topologies on the success and rate of emergence of social
conventions. While others have investigated memory for learning
algorithms, the effects of memory or history of past activities on the
reward received by interacting agents have not been adequately
investigated. We propose a reward metric that takes into
consideration the past action choices of the interacting agents. The
research question to be answered is what effect does the history based
reward function and the learning approach have on convergence time to
conventions in different topologies. We experimentally investigate the
effects of history size, agent population size and neighborhood size
or the emergence of social conventions.

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