Designing Incentives to Maximize the Adoption of Rooftop Solar Technology

Household level rooftop solar technology adoption is rising in many regions, driven by a multitude of factors, including falling prices and incentives such as tax breaks. It has also been shown in recent research that peer effects have an important role in the spread of solar adoption. This leads to a natural problem of how to design incentives to maximize adoption in such a model. While this is an instance of an "influence maximization" problem, prior results from the influence maximization literature cannot be used directly. In this work, we extend prior results from the literature on the use of submodularity to obtain a greedy approximation. We use this new result to do optimal "seed set" selection for a highly detailed, datadriven, agent-based model of household rooftop solar adoption.