The methodology for identifying factors contributing to the acceptance of behavioral change-led policies: the case of stay-at-home requests during the COVID-19 pandemic in Japan

In the wake of the COVID-19 pandemic, evidence-based policymaking (EBPM) has garnered attention in Japan. However, when applying EBPM to policies aimed at achieving the common good in society, uncertainty arises if the mechanisms through which policies bring about effects are unclear. To elucidate these mechanisms and ensure that policies function effectively as institutions, we proposed a new model incorporating cognitive mechanisms of how individuals perceive others and pre-evaluate policy acceptability. This model combines the conceptual model of the institutional ecosystem with cognitive hierarchy theory. We applied this model to Japan’s stay-at-home requests during the 2020 COVID-19 pandemic. Consequently, we could reproduce human movement data from all 47 prefectures in Japan. We propose a hybrid method combining agent-based simulation with linear regression analysis using real-world data to enhance our understanding of the mechanisms for policy effects and institutionalization related to accepting policies that require behavioral change. Using this hybrid method, we found that the depth of thinking regarding others was one of the factors influencing behavior in response to stay-at-home requests. These findings suggest the need to consider regional variations in cognitive mechanisms concerning others when predicting behavior toward policies requiring behavioral change.