Since the conclusion of World War II, policymakers in the U.S. have maintained a broad and firm consensus regarding the critical role that government-supported innovation plays in that nation’s welfare. For a number of reasons, however, the pillars of American innovation are under threat at a time when urgent challenges require government to make large, near-term investments in innovative technologies. The size of these investments and the uncertainty inherent in the process deter private funding and render government support essential.
There are large uncertainties about which innovation management strategies perform best when it comes to government-supported technology development. In the case of energy, Richard Lester of MIT and David Hart of George Mason have noted, “today there is no agreed framework for federal involvement in these activities. This is one of the most serious gaps in the current U.S. energy innovation system–especially for large-scale technologies where public risk and cost-sharing at the demonstration stage is unavoidable”. We are extending the methods of applied machine learning and quantitative decision science to investigate how best to structure technology development programs supported by the U.S. government. Program outcomes are driven not only by technical risk and cost, but also by organizational behavior. Therefore, we adopt a pandisciplinary approach to assessing the impact on program outcomes of different innovation management strategies, as embodied within different organizational structures.