"Our view of forecasting rests on the following notions. First, that most predictions and forecasts contain an irreducible intuitive component. Second, that the intuitive predictions of knowledgeable individuals contain much useful information. Third, that these intuitive judgments are often biased in a predictable manner. Hence, the problem is not whether to accept intuitive predictions at face value or to reject them, but rather how they can be debiased and improved."Usually as I read stuff, especially conceptual stuff, I try to relate it to something with which I am familiar. The article did a good job of providing understandable examples, but for me, what resonated was trying to predict how long something, particularly engineering-related, will take to repair. When something breaks--and it's inevitable that something *will,* every operational planner knows that there's "real estimate" and "engineering estimate" for repairs.
Real time is what it actually takes to fix whatever is broken, and is never actually known until the piece of equipment is fixed. Engineering time is the EO/EPOs best estimate for how long it's going to take.
"...the element of uncertainty is typically underestimated in risky decisions. The elimination of overconfidence is therefore an important objective in an attempt to improve the quality of the intuitive judgments that serve decision making."Under extreme duress and lots of nagging on my part, a first-rate, highly skilled, extremely talented EO shared with me his engineering time algorithm: 2*estimated repair time + 20 percent. So if he thought it would actually take an hour to say, fix the fuel leak on the small boat, he'd tell me that the small boat would be FMC in about two and a half hours, give or take. That way he and his engineers looked like rock stars when it was done in an hour and a half, and they still had plenty of time to thwart the annoying gremlin trickery that is inherent to engineering repairs.
Of course, I always tried to reverse engineer his engineering time to get the real time...usually only ended up annoying the hell out of both of us.
"A probability distribution that is conditioned on restrictive assumptions reflects only part of the existing uncertainty regarding the quantity, and is therefore likely to yield too many surprises."Somehow, though, I was never able to effectively apply the same theory to predicting how long it would take to launch the small boat. Like, *never.* I would always underestimate it, and we'd be late (guaranteed to aggravate me), or overestimate it, and the boat crew would have to haul a mile, usually upswell, to get to the boarding target, arriving thoroughly soaked and more tired than they needed to be (and I always knew it was my fault). I think my "restrictive assumption" was that it would either take 15 minutes to launch the small boat, or 30 minutes (mostly because my brain thinks most easily in quarter-hour increments), when actually it takes, on average, 22 minutes to get the boat in the water and boat crew and boarding team loaded. It's really hard to take the Plan of the Day seriously when it says, "0938 - Set Boat Lowering Detail," for a 1000 arrival time.
"In many problems of prediction and estimation, available information is limited, incomplete, and unreliable. If people derive almost as much confidence from poor data as from good data, they are likely to produce overly narrow confidence intervals when their information is of inferior quality."I guess my point is that I like what the authors did with the article in trying to break down the nature of uncertainty in planning. I'm poking gentle fun at it because they take it so seriously, and turn it all scientific and statistical. But, in the end, they're right...the important thing about predictions is honestly recognizing where they are weak, and trying, despite ourselves, to compensate for those weaknesses.