Disentangling material, social, and cognitive determinants of human behavior and beliefs

Abstract

Abstract In social interactions, human decision-making, attitudes, and beliefs about others coevolve. Their dynamics are affected by cost-benefit considerations, cognitive processes (such as cognitive dissonance, social projecting, and logic constraints), and social influences by peers (via descriptive and injunctive social norms) and by authorities (e.g., educational, cultural, religious, political, administrative, individual or group, real or fictitious). Here we attempt to disentangle some of this complexity by using an integrative mathematical modeling and a 35-day online behavioral experiment. We utilize data from a Common Pool Resources experiment with or without messaging promoting a group-beneficial level of resource extraction. We directly estimate the weights of different factors in decision-making and beliefs dynamics. We show that personal norms and conformity with expected peers’ actions have the largest impact on decision-making while material benefits and normative expectations have smaller effects. Individuals behaving prosocially are characterized by higher weights of personal norms while antisocial types are more affected by conformity. Messaging greatly decreases the weight of personal norms while simultaneously increases the weight of conformity. It also markedly influences personal norms and normative expectations. Both cognitive and social factors are important in the dynamics of beliefs. Between-individual variation is present in all measured characteristics and notably impacts observed group behavior. At the same time, gender differences are small. We argue that one can hardly understand social behavior without understanding the dynamics of personal beliefs and beliefs about others and that cognitive, social, and material factors all play important roles in these processes. Our results have implications for understanding and predicting social processes triggered by certain shocks (e.g., social unrest, a pandemic, or a natural disaster) and for designing policy interventions aiming to change behavior (e.g., actions aimed at environment protection or climate change mitigation).