A Behavioral Social Learning Model for Studying the Dynamics of Forecast
Adoption
Abstract
Drought forecasts, particularly at seasonal scales, offer great
potential for managing climate risk in water resources and agricultural
systems. In this context, the importance of assessing the economic value
of such forecasts and determining whether a decision-maker should adopt
them cannot be overstated. Value-assessment studies often, however,
ignore the dynamic aspects of forecast adoption, despite evidence from
field-based studies suggesting that farmers’ forecast-adoption behavior
fits the general framework of innovation diffusion, i.e. that forecast
adoption is a dynamic learning process that takes place over time. In
this study, we develop an agent-based model of drought forecast adoption
to study the role played by heterogeneous economic and behavioral
factors (i.e. risk aversion, wealth, learning rates), forecast
characteristics (i.e. accuracy), and the social network structure (i.e.
inter- and intra-county ties, change agents, self-reliance) in the
process of forecast adoption and diffusion. We consider two learning
mechanisms: learning by doing, represented by a reinforcement-learning
mechanism, and learning from others, represented by a DeGroot-style
opinion-aggregation model. Results show that, when social interactions
between agents occur, forecast adoption follows a typical S-shaped
diffusion curve. By contrast, when agents rely only on their own
experience, the adoption pattern is close to linear. Our numerical
experiment shows additionally that forecasts are never adopted if
forecast accuracy drops below 65 percent. Finally, the proposed model
also provides a flexible tool with which to test the effectiveness of
extension targeting strategies in facilitating the diffusion of
forecasts.