A Weighted Decision List

Sometimes I’m analytical to a fault.

Take the traditional pros-cons list. I like the idea, in that listing out the goods and bads of a singular item should make for a more quantified, analytical position. Its criticized, however, for setting up false dichotomies and its role as a rationalization tool to decisions that may have already been made.

My usage of the pros-cons list has been to enable me to choose between multiple options that all seem pretty good to great. At its core, the technique helps compare disparate options. By converting everything into an identical unit of measurement, theoretically, the biggest total should win. These comparisons come up often enough, for big enough decisions (e.g., choosing a job offer, buying a house, buying a car, etc.) that I don’t feel comfortable relying on just gut feelings.

In a way, making these lists is akin to comparing spec sheets: there’s one column per entrant, and each row contributes an attribute/category. For instance, for figuring out which home to buy, rows may be labeled: price, walkability of neighborhood, number of bedrooms and bathrooms, age of the property, etc.

To figure out a winner among like applicants, a naive technique would be to decide a winner per row, then pick whichever option won the most rows; in pros-cons lists, this is akin to choosing whichever side has more points. The major flaw with this method is that it assumes all attributes are equal, when inevitably some characteristics matter a lot more than others. For the example of a home purchase, total square footage and age of the home and number of bedrooms have varying degrees of relevance to different buyers.

My take on this system adds implicit weights by assigning ranges corresponding to the importance of items. Important areas can be rated 1-10, while less crucial ones range 1-5 or 1-3, and nice-to-haves are 0-1. In theory, the different ranges of numbers should allow for a more precise summation; it attempts to account for tradeoffs between a few major positives versus very minor positives.

I employed this with my recent car purchase. Initially I had settled on the newly announced Tesla Model X, but I started having doubts after a bit more research, and it was enough that I began to systemically analyze the alternatives. This was me going through the exercise:

It turns out that the new Volvo XC90 just edges out the Model X!

It’s important to note that the process and end results are entirely subjective; the numbers only have meaning to the person creating these lists. It’s tool that tries to resolve the meandering internal debates that sound like “well, there’s this great feature, but there’s this other downside, but how much do I care about this wholly different area…”

Your mileage will definitely vary.

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