An Agent-Based Behavioral Change Model with Behavioral Intervention Control Techniques

Human behavior is complicated and can be difficult to predict. In this paper we use an agent-based model to predict behavioral change. This model is based on sociological and psychological theories such as reasoned action, conformity, internal biases, and outcome feedback. This model is used to simulate and evaluate the effectiveness of different behavioral intervention control techniques. A sufficient criteria is developed for allowing an external source of influence (i.e. a control agent) to be able to change agents' behaviors. Control techniques investigated via simulation are as follows: “one to all”, “influencer”, “rotating”, “targeted belief”, “targeted behavior”, “targeted low bias”, and a “targeted high bias” approach. From the simulations the “targeted belief”, “targeted behavior”, and “rotating” approaches show the most promise with a large increase in agent behavior. The “targeted bias” simulations indicate that targeting individuals with low bias is more effective than those with high bias, knowledge of which can be used in further studies.