Integrating behavioral experimental findings into dynamical models to inform social change interventions
For researchers and practitioners in social influence and behavior change, this work provides a method to combine individual decision-making and network propagation, addressing a long-standing gap.
The paper integrates behavioral experimental findings with dynamical models to improve seeding policies for large-scale adoption of behaviors or products. It shows that state-of-the-art seeding methods are suboptimal if they neglect individual-level behavioral drivers, and proposes a method to correct this.
Addressing global challenges -- from public health to climate change -- often involves stimulating the large-scale adoption of new products or behaviors. Research traditions that focus on individual decision making suggest that achieving this objective requires better identifying the drivers of individual adoption choices. On the other hand, computational approaches rooted in complexity science focus on maximizing the propagation of a given product or behavior throughout social networks of interconnected adopters. The integration of these two perspectives -- although advocated by several research communities -- has remained elusive so far. Here we show how achieving this integration could inform seeding policies to facilitate the large-scale adoption of a given behavior or product. Drawing on complex contagion and discrete choice theories, we propose a method to estimate individual-level thresholds to adoption, and validate its predictive power in two choice experiments. By integrating the estimated thresholds into computational simulations, we show that state-of-the-art seeding methods for social influence maximization might be suboptimal if they neglect individual-level behavioral drivers, which can be corrected through the proposed experimental method.