A Game Theory Approach for Assisting Humans in Online Information-Sharing

Contemporary information-sharing environments such as Facebook offer a wide range of social and practical benefits. These environments, however, may also lead to privacy and security violations. Moreover, there is usually a trade-off between the benefits gained and the accompanying costs. Due to the uncertain nature of the information-sharing environment and the lack of technological literacy, the layperson user often fails miserably in balancing this trade-off. In this paper, we use game theory concepts to formally model this problem as a "game", in which the players are the users and the pay-off function is a combination of the benefits and costs of the information-sharing process. We introduce a novel theoretical framework called Online Information-Sharing Assistance (OISA) to evaluate the interactive nature of the information-sharing trade-off problem. Using these theoretical foundations, we develop a set of AI agents that attempt to calculate a strategy for balancing this trade-off. Finally, as a proof of concept, we conduct an empirical study in a simulated Facebook environment in which human participants compete against OISA-based AI agents, showing that significantly higher utility can be achieved using OISA.