Friday, September 1, 2017 at 12:15pm
Understanding human behavior is critical for several disciplines that need to account for supply and demand dynamics. Choice modeling aims at representing the cognitive process of economic decisions and is widely used in transportation systems analysis, health, environmental and energy economics, marketing, industrial organization, operations research, and urban planning. Choice modeling is used in practice to forecast demand under differing pricing and marketing strategies and to determine how much consumers are willing to pay for product/service improvements. In transportation engineering, these models allow researchers, firms, and policy-makers to predict demand for new alternatives and infrastructure, to analyze the market impact of firm decisions, to set pricing strategies, to prioritize research and development decisions as well as to perform cost-benefit analyses of infrastructure projects. This talk will overview the microeconometric foundations of discrete choice analysis and the development of the models in terms of flexibility gains and computational complexity. The design, use, and value of controlled choice experiments will also be discussed. Examples from transportation and energy will highlight the use of choice modeling to engineer solutions that are not only technically sound and environmentally sustainable but also attractive enough to be adopted by society in general.
Ricardo A. Daziano (PI), Ph.D. in Economics (Université Laval, 2010), is an Associate Professor in the School of Civil and Environmental Engineering at Cornell. His research focuses on engineering decision making, specifically on theoretical and applied econometrics of consumer behavior and discrete choice models applied to technological innovation in transportation and energy efficiency. Conventional methods in discrete choice modeling treat forecasts as deterministic, but Daziano’s research aims to overcome this limitation by deriving robust, computationally efficient statistical inference methods for policy-oriented analysis. In addition, his work in choice modeling has a unique focus on surplus-improving decisions for technology development and on finding a path for conversion to sustainable energy sources. The empirical research interests of Daziano’s group include the study of pro-environmental preferences toward ultra-low-emission vehicles, modeling the adoption of sustainable travel behavior, estimating willingness-to-pay for renewable energy, and forecasting consumers’ response to environmentally-friendly energy sources.