Decision Modeling, that is, not that other kind of modeling (I don’t have the appropriate assets for that other kind of modeling). Class last week had a very interesting lesson embedded in it, and we’ll be learning more this week about that, I think. It’s was about weighted averages, and the hidden logical fallacy buried within them. It’s this: when we apply a “weight” to a weighted average in which we are assigning relative importance to various attributes of a decision we are trying to measure, we are implicitly assigning a constant exchange rate between the various attributes. The weight basically means this: I would trade this much of attribute X in order to gain this much of attribute Y.
Most people, the professor contended, don’t think that deeply about what the weights they are using mean. Further, he suggested, most of us don’t really have a constant trade-off that we’re willing to make. The more we have of something, often, the more of it we’re willing to trade to get something we have less of. As the relative difference between the two diminishes, so to does the amount we’re willing to trade. Weighted averages don’t take this fact into consideration. Whether there’s an answer to this problem-solving conundrum, I don’t yet know.
For this week, there’s a chance I may be late or non-existant with a few of the normal daily updates. I haven’t missed a day in over two months, but there are a number of deadlines and other higher-priority tasks hitting this week that will require my more immediate attention than this blog. So, if I miss a day or two this week, my sincerest apologies, in advance. It is only because it is unavoidable, if so.