Agent-Based Modeling of Complex Social Interactions

Traditional wisdom that assumed benefit maximization often neglected the role of social interactions (e.g., norms) in human decision-making. Studies have shown that interactions among people are important in human decision-making, such as environmentally significant behaviors. We study complex social interactions for enrollment in China’s Grain-to-Green program, the largest payments for ecosystem services program in the developing world. We use an agent-based model to allow for individuals’ enrollment decision that can be affected by norms and circumstances formed by decisions of other community members.