{'fraction_non_alphanumeric': 0.011946591707659873, 'fraction_numerical': 0.0, 'mean_word_length': 5.56221198156682, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Effects of interaction history and network topology on rate of convention emergence

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 researches. 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 studing 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. The reward metric is the majority rule, thus the emerging convention becomes self propagating in the society. Agents are proportionally rewarded based upon their conformity to the majority action when interacting with another agent. Another research question to be answered is what effect does the history based reward function have on convergence time in different topologies. We also investigate the effects of history size, agent population size and neighborhood size proving their effects by agent-based experimentation.