Understanding Uncertainty Through Simulation with
Foreword
by Peter L. Bernstein,
Author of Against the Gods The Remarkable Story of Risk
Understanding Uncertainty Through Simulation with |
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Foreword |
“The only certainty is that nothing is certain.” So said the Roman scholar, Pliny the Elder. And some 2000 years later, it’s a safe bet he would still be right. Many people, when faced with an uncertainty, like future sales, or interest rate, succumb to the temptation of replacing the uncertain number in question with a single average value. I call this the flaw of averages, and it is a fallacy as fundamental as the belief that the earth is flat (see my article in the San Jose Mercury News). Roughly speaking, the flaw of averages states that:
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A
well known example involves the statistician who drowned while fording a
river that was, on average, only three feet deep. But in real life, the flaw of averages gums up seemingly well-laid plans on a regular basis. Fortunately, today’s computers can alleviate this problem through a technique known as Monte Carlo simulation. The name was applied during its use in the Manhattan atomic bomb project, but this simple idea has been around for centuries, just waiting for the PC to make it practical for everyday use. If you think of a spreadsheet model as a ladder that you are about to climb on, then Monte Carlo simulation shakes the ladder with random forces to make sure that it is stable under a wide range of scenarios. XLSim has been designed to provide a simple but powerful introduction to Monte Carlo simulation for Excel spreadsheet users. The emphasis has been on ease of use rather than on features. The Command Reference contains information on installation, commands and limitations of XLSim, including a discussion of more powerful simulation software available from other vendors. The first four sections of this document describe how simulation may guide us to make better decisions under uncertainty. No prior knowledge of statistics is assumed. There is, in fact, some evidence that this may actually be harmful (see Reference [3]). |
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