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

TL;DR

This paper combines behavioral experiments with complex contagion models to better predict and optimize large-scale social change interventions.

Contribution

It introduces a method to estimate individual adoption thresholds by integrating choice modeling into contagion theory, improving intervention strategies.

Findings

Estimated individual thresholds improve prediction of behavioral adoption.

Integrating thresholds into simulations enhances the effectiveness of seeding policies.

Neglecting behavioral drivers can lead to suboptimal social change strategies.

Abstract

Addressing global challenges 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 identifying the drivers of individual discrete 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. Here, by integrating discrete choice modeling 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 behavioral change might be suboptimal if they neglect individual-level behavioral drivers, which can be corrected through the proposed experimental method.