Stephen C. Hora’s paper titled “Median Aggregation of Distribution Functions" won the Decision Analysis 2013 Special Recognition Award. This award is given annually for the paper most worthy of recognition published in the Decision Analysis Journal.
The criteria for the paper most worthy of special recognition:
1) the paper is foundationally based on decision analysis
2) the paper makes an important contribution to theory and/or practice
3) the paper is broadly interesting and influential to a wide portion of decision analysis community
Dr. Hora partnered with the Office of Policy/Strategy, Planning, Analysis and Risk (SPAR) at the U.S. Department of Homeland Security (Benjamin R. Fransen, Natasha Hawkins, Irving Susel) to write the paper.
When multiple redundant probabilistic judgments are obtained from subject matter experts, it is common practice to aggregate their differing views into a single probability or distribution. Although many methods have been proposed for mathematical aggregation, no single procedure has gained universal acceptance. The most widely used procedure is simple arithmetic averaging, which has both desirable and undesirable properties. Here we propose an alternative for aggregating distribution functions that is based on the median cumulative probabilities at fixed values of the variable. It is shown that aggregating cumulative probabilities by medians is equivalent, under certain conditions, to aggregating quantiles. Moreover, the median aggregate has better calibration than mean aggregation of probabilities when the experts are independent and well calibrated and produces sharper aggregate distributions for well-calibrated and independent experts when they report a common location-scale distribution. We also compare median aggregation to mean aggregation of quantiles.
Stephen C. Hora, Benjamin R. Fransen, Natasha Hawkins, Irving Susel (2013) Median Aggregation of Distribution Functions. Decision Analysis 10(4):279-291. http://dx.doi.org/10.1287/deca.2013.0282