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Asa Benjamin Palley Assistant Professor Operations and Decision Technologies Kelley School of Business, Indiana University Faculty Profile at Kelley |
I am an Assistant Professor of Operations and Decision Technologies at the Kelley School of Business at Indiana University. I also serve as an Associate Editor at Management Science. I received a Ph.D. in Decision Sciences from the Fuqua School of Business at Duke University, where I additionally completed a Certificate in College Teaching.
My research uses tools from the fields of decision analysis, operations research, and judgment and decision making to develop prescriptive methods to help individuals and organizations make better decisions. A critical step in many decision problems is the estimation and quantification of uncertainty about important variables. Often, decision makers rely on personal or expert judgments (which may be subjective and/or driven by a formal quantitative model) to form such assessments. My primary stream of work thus far aims to improve these probability distributions by studying how they are obtained, how they can be appropriately adjusted, and how the availability of multiple experts can be leveraged to increase their accuracy. I use analytical mathematical models to derive new methodology for obtaining estimates of these uncertainties and use laboratory experiments and archival data to test the effectiveness of these approaches. Secondary research interests include carbon pricing and investment in renewable electricity generation and storage capacity, learning in sequential decision problems, and the application of decision analysis to public policy questions. My work has been published in the journals Management Science, Experimental Economics, and Risk Analysis.
My research uses tools from the fields of decision analysis, operations research, and judgment and decision making to develop prescriptive methods to help individuals and organizations make better decisions. A critical step in many decision problems is the estimation and quantification of uncertainty about important variables. Often, decision makers rely on personal or expert judgments (which may be subjective and/or driven by a formal quantitative model) to form such assessments. My primary stream of work thus far aims to improve these probability distributions by studying how they are obtained, how they can be appropriately adjusted, and how the availability of multiple experts can be leveraged to increase their accuracy. I use analytical mathematical models to derive new methodology for obtaining estimates of these uncertainties and use laboratory experiments and archival data to test the effectiveness of these approaches. Secondary research interests include carbon pricing and investment in renewable electricity generation and storage capacity, learning in sequential decision problems, and the application of decision analysis to public policy questions. My work has been published in the journals Management Science, Experimental Economics, and Risk Analysis.