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Decision Pattern – “Highly Accurate Ballpark Estimate”
A bad way to make decisions is to ignore the accuracy of your underlying data, and continue to finesse the analytical model that calculates values to 4 decimal places.
Shortcut Decision Pattern
The following two equivalence rules of boolean logic are frequently used by scientists and engineers.
- P and False = False
- P or True = True
The obvious reason these are important is because they obviate the computation of “P”, which may be significant saving in computation time. This is of significant import in software optimization (removing unnecessary branches of code), and more specifically in pattern matching algorithms, such as Rete.
An interesting use of these shortcut is in enterprise decision making involving human interaction (such as management debates). Say you are deciding whether to launch a new line of shaver tooth brushes. You have decided that you will launch this line of products, if (i) there are enough people with less time in the morning, or (ii) there are enough people who shave multiple times a day and brush multiple times a day, or (iii) you need to spend the cash to reduce tax liability. This would be written in shortcut notation as:
launchNewProdLine = enoughLateRisers() || multipleShaverBrushers() || needSpendCash()
In algorithmic/software terms, each condition would be evaluated in sequence until the first condition evaluated to true. The rest of the terms would not be evaluated. However, in people terms, the way these decisions are laid out, all terms are usually evaluated. The justification usually is that they are “important as well” and “have implications”, but the way I think about it, I think it is mostly just curiosity (Do we really have so much income that we should try to reduce tax liability? Really? Wow!)
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