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progress. For example, Grubler and Gritsevskii [109] used a simple optimization model with endogenous technological change represented by a traditional log-linear experience curve, but added uncertainty in the learning rate, represented by a lognormal distribution function around the mean value. They showed that when the rate of learning was certain (i.e., perfect foresight), the optimal solution was to invest heavily and early in the “winning” technology. Barreto and Klaassen [59] found similar results. However, when learning rates were uncertain (as in the real world), the optimal solution also became less certain. As a result, there were broader investments in a portfolio of technologies, with slower diffusion and market entry of any particular technology. Messner et al. [110] also incorporated uncertainties in future technology performance and found that it tended to spread risk over a larger number of options to cope with uncertainties in technology development paths.

Over the longer term, continued research into the underlying factors that govern or influence technological innovations may yield improved models that can reliably forecast the implications of proposed energy and environmental policy measures. In the meantime, more concerted efforts are needed to explore, understand and display the consequences of uncertainties in current formulations of technology experience curves (or other models) used to project the future cost of technology in energy-economic modeling and policy analysis.


We gratefully acknowledge the contributions of our colleagues Profs. Margaret Taylor (University of California, Berkeley) and David Hounshell (Carnegie Mellon University) to an earlier version of this paper (available at <>), as well as to several of the references cited below, which were invaluable to the foundations of this paper.


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