The article I co-authored with Doug Samuelson in Analytics Magazine just came out with the fall issue. “Analysis Placebos: The Difference Between Perceived and Real Benefits of Risk Analysis and Decision Models.” explains why many popular analysis methods and models may have entirely illusory benefits. See the online version of the article at http://viewer.zmags.com/publication/2d674a63#/2d674a63/15
by Douglas Hubbard and Douglas Samuelson
Analytics, 10/28/2009
The article I coauthored with Doug Samuelson in Analytics Magazine just came out with the fall issue. “Analysis Placebos: The Difference Between Perceived and Real Benefits of Risk Analysis and Decision Models.” explains why many popular analysis methods and models may have entirely illusory benefits. [view article]
See the Events page on www.hubbardresearch.com. The webinars in August in September did very well and demand continues to grow. There are several webinars of different lengths, topics and times that are sure to fit your needs.
October 9 article in OR/MS Today by Douglas Hubbard and Douglas Samuelson
“Modeling Without Measurements: How the Decision Analysis Culture’s Lack of Empiricism Reduces Its Effectiveness”
See this article at http://www.lionhrtpub.com/orms/orms-10-09/frrisk.html.
In this article my co-author and I point out a general lack of willingness to measure the actual effectiveness of many quantitative models. Just as doctors are often the worst patients, quants are often the last to measure their own performance or the performance of the models they create. We argue that this leads to the unquestioned and continued use of many models that are deeply flawed. We discuss several sources of those problems and what to do about them.
by Douglas Hubbard and Douglas Samuelson OR/MS
Today, 10/09/2009
In this article my coauthor and I point out a general lack of willingness to measure the actual effectiveness of many quantitative models. Just as doctors are often the worst patients, quants are often the last to measure their own performance or the performance of the models they create. We argue that this leads to the unquestioned and continued use of many models that are deeply flawed. We discuss several sources of those problems and what to do about them. [view article]
