Abstract for: How People Respond to Policies: Integrating Behavioural and Decision-Making Theories into Systems Thinking Models for Sustainability Transformations

Understanding how individuals and groups respond to transformative policies is essential for sustainability. Traditional policy analyses assume rational actors, yet human decision-making is complex, non-linear, and shaped by cognition, social norms, and institutions. Behavioural public policy modelling seeks to integrate decision-making and behavioural change processes into ex-ante assessments, enabling policies to be evaluated for both effectiveness and adoption likelihood. This paper reviews decision-making, behavioural, and behavioural change theories from psychology, behavioural economics, and social sciences including their origin. It then demonstrates exemplary pathways to integrate these theories into systems models for policy evaluation: (i) qualitative systemic decision-making models, (ii) embedding behavioural insights into socio-technical, socio-ecological, and socio-ecological-technological models, and (iii) quantitative System Dynamics simulations for time-resolved dynamics. Systemic decision-making models integrating multiple behavioural theories reveal the complex interplay of cognitive, social, and economic factors influencing policy target groups. Embedding these insights into STS, SES, and SETS models allows proper representation of social components in mixed systems. Quantitative System Dynamics simulations further operationalise soft variables, enabling exploration of feedbacks and emergent dynamics. Together, these approaches provide holistic insights into behavioural responses under transformative policies. Qualitative CLDs capture complex behavioural dynamics, while quantitative simulations explore emergent effects over time. Challenges include operationalising soft variables, accounting for bounded rationality, and managing uncertainty. Combining both approaches bridges fragmented theories, offering policymakers behaviourally grounded insights and guiding the design of effective interventions for sustainability transformations. AI-based language assistance (more details in manuscript)