Modeling and analyzing users’ behavioral strategies with co-evolutionary process

Abstract Social networking services (SNSs) are constantly used by a large number of people with various motivations and intentions depending on their social relationships purposes, thus, resulting in diverse strategies posting/consuming content SNSs. Therefore, it is important to understand the differences individual network locations surroundings. For this purpose, using game-theoretical model users called agents proposing co-evolutionary algorithm multiple-world genetic evolve strategy for each user, we investigated compared results artificial networks those Facebook ego network. From our experiments, found that did not select free rider strategy, which means just reading articles comments posted other users, network, although usually cost-effective appeared networks. We also who mainly comment articles/comments rarely post own appear but do connecting nearest-neighbor networks, think kind user actually exists real-world Our experimental simulation revealed friends was crucial factor identify users’ SNSs through analysis impact reward