With New Year’s now in the rear view mirror, I’ve been speculating on what kind of resolutions the folks on Wall Street made this week.  This year’s dismal performances in the markets leave a lot of room for good intentions, and I suspect one common theme of these resolutions was “better tools.”   Last quarter, in the laying of blame for the credit failures that caused the financial system to buckle, there were many aspersions cast about the models used by credit rating agencies for risk assessment and decision evaluation in investing, and there were even some fingers pointing directly at Monte Carlo software and the other computational tools for risk analysis.  

Sharper tools may help protect future investors making decisions under the usual levels of uncertainty, but the fact is that shiny new tools are easier to develop than flawless judgment.   And the judgment of the model creator is a huge factor in the reliability of the model: “garbage in, garbage out,” as is famously said about any kind of computer output.  

The judgment of the modeler is particularly crucial when it comes to the handling of probabilities. Therefore, RESOLVED: my first entries in 2009 will be devoted to the role and specification of probability.

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  1. An analysis is only as good as the model used to prepare it. I am involved with project risk analysis for oil and gas projects. If one of my models is not constructed to reflect specific risks events for that project (cost and schedule), then it is a wasted effort. I have seen too many people put more effort in learning the mechanics of running @Risk and other monte carlo software packages than learning the process of constructing models to reflect the risk associated with projects.

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