  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.
