Monday, February 20, 2012

Explain or predict: simulation

Some time ago, when I presented the "explain or predict" work, my colleague Avi Gal asked where simulation falls. Simulation is a key method in operations research, as well as in statistics. A related question arose in my mind when thinking of Scott Nestler's distinction between descriptive/predictive/prescriptive analytics. Scott defines prescriptive analytics as "what should happen in the future? (optimization, simulation)".

So where does simulation fall? Does it fall in a completely different goal category, or can it be part of the explain/predict/describe framework?

My opinion is that simulation, like other data analytics techniques, does not define a goal in itself but is rather a tool to achieve one of the explain/predict/describe goals. When the purpose is to test causal hypotheses, simulation can be used to study what-if the causal effect was true, by simulating data from the "causally-true" hypothesis and comparing it to data from "causally-false" scenarios. In predictive and forecasting tasks, where the purpose is to predict new or future data, simulation can be used to generate predictions. It can also be used to evaluate the robustness of predictions under different scenarios (that would have been very useful in recent years economic forecasts!). In descriptive tasks, where the purpose is to approximate data and quantify relationships, simulation can be used to check the sensitivity of the quantified effects to various model assumptions.

On a related note, Scott challenged me on a post from two years ago where I stated that the term data mining used by operations research (OR) does not really mean data mining. I still hold that view, although I believe that the terminology has now changed: INFORMS now uses the term Analytics in place of data mining. This term is indeed a much better choice, as it is an umbrella term covering a variety of data analytics methods, including data mining, statistical models and OR methods. David Hardoon, Principal Analytics at SAS Singapore, has shown me several terrific applications that combine methods from these different toolkits. As in many cases, combining methods from different disciplines is often the best way to add value.
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