Asa B. Palley
Assistant Professor of Operations and Decision Technologies
The Kelley School of Business at Indiana University
a<mylastname>@indiana.edu
Faculty Profile at Kelley
I am an Assistant Professor of Operations and Decision Technologies at the Kelley School of Business at Indiana University. I received a Ph.D. in Decision Sciences at the Fuqua School of Business at Duke University, where I also completed a Certificate in College Teaching. Previously, I earned an A.B. from Bowdoin College, an M.S. in Applied Mathematics and Scientific Computation from the University of Maryland at College Park, and an M.S. in Mathematics from Carnegie Mellon University.
My research uses tools from the fields of decision analysis, operations research, and judgment and decision making to generate prescriptive approaches 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. A decision maker may 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 distribution accuracy. I use analytical mathematical models to derive new methodology for obtaining probability distributions and use laboratory experiments and archival data to test the effectiveness of these approaches. Secondary interests include learning in sequential decision problems, carbon pricing and investment in renewable generation and storage capacity, 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 generate prescriptive approaches 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. A decision maker may 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 distribution accuracy. I use analytical mathematical models to derive new methodology for obtaining probability distributions and use laboratory experiments and archival data to test the effectiveness of these approaches. Secondary interests include learning in sequential decision problems, carbon pricing and investment in renewable generation and storage capacity, 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.