Impact of Attitude and Bounded Rationality on Collective Behavioral Transitions

A team of researchers from KTH Royal Institute of Technology and Nanyang Technological University has developed a dynamic agent-based modeling framework that brings the Theory of Planned Behavior (TPB) into the computational age. Led by Chen Song, the team integrated TPB's core components—attitude, subjective norms, and perceived behavioral control—with a novel behavior-to-attitude feedback mechanism. This allows the model to simulate how populations collectively transition between behaviors over time, such as adopting a beneficial health practice or rejecting a harmful social norm.

The model's key finding is that collective behavioral transitions can be effectively controlled by tuning just two parameters: the strength of attitude influence on behavior and the degree of bounded rationality in agents' decision-making. By adjusting these, the framework can predict whether a population will shift toward or away from a given behavior. This work, accepted for presentation at the 23rd IFAC World Congress, provides the first quantitative, dynamic extension of TPB, enabling researchers to simulate interventions and identify leverage points for driving large-scale behavioral change.