Policy Prescience: Predictive Modeling of Technology Diffusion in a Changing Policy Context
*Names in bold indicate Presenter
Solar PV adoption decision-making is complex. Both economic aspects (e.g. prices & rebates) and informational aspects (e.g. information exchanges among individual decision-makers) each play a large role in the structure and evolution of diffusion. Forming ex ante expectations of the impact of policy changes requires predictive models that account for both economic and informational aspects of the decision-making context. We compare two approaches that incorporate these aspects in predictive modeling of solar PV diffusion, each with embedded tradeoffs: a top-down, equation-based approach that deals with information flows and individual decision-making in the aggregate and a bottom-up, generative approach that models them explicitly. We exploit the variation across these approaches to investigate how tradeoffs in predictive modeling methods impact the prediction of future solar PV diffusion when the adoption decision-making context experiences a large shift. By comparing the two approaches on a common footing, we contribute to an understanding of the role model choice plays in determining policy recommendations.