Integrating behavioural experimental findings into dynamical models to inform social change interventions

Addressing global challenges often involves stimulating the large-scale adoption of new products or behaviours. Research traditions that focus on individual decision-making suggest that achieving this objective requires identifying the drivers of individual discrete adoption choices. However, computational approaches rooted in complexity science focus on maximizing the propagation of a given product or behaviour throughout social networks of interconnected adopters. Here, by integrating discrete-choice modelling into the complex contagion theory, we propose a method to estimate individual-level thresholds to adoption. We validate the predictive power of this approach in two choice experiments. By integrating the estimated thresholds into computational simulations, we show that state-of-the-art seeding policies for initiating large-scale behavioural change might be suboptimal if they neglect individual-level behavioural drivers, which can be corrected through the proposed experimental method.