Is the Fermi Paradox due to the Flaw of Averages?
Where is everybody? — Enrico Fermi The omnipresence of uncertainty is part of why making predictions and decisions is so …
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Voluntary vs. mandatory testing for naval crew selection
As COVID-19 testing becomes a routine part of doing business, some operations face the question of whether testing should be …
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The Suppression Triangle for COVID-19: How soon can we end the lockdown?
The COVID-19 pandemic has given many people a crash course in the frightening implications of rapid exponential growth in cases …
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Suppression strategy and updated forecast for US deaths from COVID-19 Coronavirus in 2020
(Posted 23-Mar-2020) Two weeks ago in the article “Estimating US Deaths from COVID-19 Coronavirus in 2020” [C20b], I developed a …
How social isolation impacts COVID-19 spread in the US: A Markov model approach
On Monday of this week, the Imperial College COVID-19 Response Team released an influential report that forecasts how social isolation …
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Estimating US Deaths from COVID-19 Coronavirus in 2020
We all are uncertain about how the future will unfold. This is a primary reason why decision making in business, public policy and our personal lives is so hard. As the COVID-19 Coronavirus pandemic continues to unfold [W20], we are all confronted numerous new decisions that hinge a lot on how bad this is going to get. And these are hard, because the uncertainty and the stakes are so big.
Failure To Communicate: Why big data analytics projects fail and how to save them
If you are like most practitioners of analytics, you went into the field because you are good with numbers, and you want to help people improve the quality of decisions on important issues. But many of us spend too much time struggling to organize poorly structured data and debugging complex spreadsheets or code and too little time engaging directly with our clients to help them clarify their objectives and decisions, brainstorm better decision options, and explore, visualize and understand the results. Without this kind of interaction, the analysis may fail to address the issues they really care about. Even if it does, clients may not develop the confidence to rely on the insights, and you end up frustrated that your hard work fails to be properly appreciated.
Read the article at Orms Today Magazine, Dec 2019
Book Review: Toma de decisiones bajo incertidumbre
This just-released Spanish-language textbook from Professor Jorge E Muro Arbulú has now taken its spot as my favorite textbook on …
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Testing hypotheses about causation
In 2002, I developed a statistical framework for testing whether your data provides statistically significant support for the hypothesis that A causes B. I published only one conference paper with some colleagues on the idea before moving on to other things, but I’ve always felt the idea was pretty novel and interesting.
How the strange Cauchy distribution proved useful
On Tuesday I had an interesting exchange with Jorge Muro Arbulú, a professor in Peru, about the Cauchy distribution, which …
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