BivariateOutlierPeer into the crystal ball.  It will tell you the future.  This was what the principal researcher(PR) wanted.  To be more precise PR was the customer and he wanted a formula that would take a few simple measurements and return a yes or a no.  Simple enough, but it didn’t make sense.

The PR wanted a simple  formula with a few inputs that spit out a yes, plant a marsh or a no, do not plant a marsh.

Why a marsh?  To slow down erosion, because it is cheaper than building something bigger and more expensive.

Why not a marsh?  It will not work and will waste time, effort and money.

So the question was, “will the marsh be effective at erosion control?, yes or no”.  The PR’s customers were managers that didn’t like a mushy maybe.

Shorelines are really messy.  High tide could be miles from low tide, or high tide could be right on top of low tide.  Sometimes a no is really simple, if you are on a shore and the waves are large and crashing loud and this is the norm and not something unusual, most likely the marsh will not root and grow(look at all of the qualifiers, even the simple can be complicated).  If the shoreline is a vertical cliff, not much place for a marsh.

So we took the data, and ran it through multivariate regression analysis  and spit out a formula.  The magic formula spit out a number, but the number needed interpretation.  If it was in this range, it was a yes, if it was outside the range it was a no.  There were a lot of weak yeses and medium yeses that became nos.

Almost any strong yes could be invalidated by big events, like the big hurricane party that nature throws with lots of storm surge, tsunamis, or big floods.  Of course the bigger, stronger, more expensive engineering solutions fail when nature throws a fit as well.

I thought that the formula added a layer of obfuscation rather than provide a simple decision tool.  The PR  knew the answer to the yes-ness and the no-ness without the formula, he just wanted the scientific coolness factor that formulas conferred.  The PR wanted a formula that hid the uncertainty in the decision, because his customers were risk averse.

Maybe the formula made sense as a tool to avoid decision paralysis.

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Of course this formula could have been a candidate for a  fuzzy math  solution, but fuzzy math in all its glory hadn’t been born yet.

fuzzyshorelinebearNowadays texas hold em players who are accustomed to probability calculations may be more comfortable with results that are more probabilistic than exact, when making decisions.  Then again, how many people are comfortable with probability, and how many people misuse or misunderstand probability.

Forty years later there is no evidence that the formula is useful or used.

Was all this worthwhile? Yes/no/maybe/don’t know.