Paul Saffo's tools and hints about forecast
The last issue of Harvard Business Review features an insightful article by Paul Saffo about efficient forecast. Although the title refers to "6 rules" for efficient forecast, the article actually provides the reader with two "thinking tools" and a set of highly relevant heuristics about how do forecasting. The premise here is that forecasting is in no way about predicting the future but instead to identify the full range of possibilities to take meaningful actions in the present. The two tools described by Saffo are the "cone of uncertainty" and "S-curves".
A cone of uncertainty is a tool meant to help the decision maker exercise strategic judgment by delineating possibilities that extend out from a particular moment or event. At first, defining the cone corresponds to set its breadth: the measure of overall uncertainty. Saffo describes that it is important to define it broadly at start to "maximize your capacity to generate hypotheses about outcomes and eventual responses". Defining the edge is also worthwhile since it enable to distinguish "between the highly improbable and the wildly impossible". Then the point is to fill the cone with external factors to consider, inside the cone there would be factors such as the possible emergence of competing technologies or consumer characteristics (preference) and at the edge of the cone would be wild cards (surprising events such as war, terrorist attack), which are what define the edge of the cone. While the neck of the cone depicts the key speculation, the end shows the possible outcomes.
See the example Saffo gave (about robots):
(Cone as defined by Paul Saffo / HBR)
The other thinking tool presented here is the S-curve (as exemplified by Moore's law). As described previously in this blog, S-curves (power laws) are meant to model the way change happens: it starts slowly and incrementally till an inflection point where it explodes, eventually reaching a plateau. Forecasting is about finding S-curved patterns before the inflection point (left of the curve). Moreover, Saffo highlights the fractal nature of s-curves: they are composed of small s-curves; which means that finding a S-curve can lead to suspect a larger/more important one in the background. He also gives few hints:
" the left-hand part of the S curve is much longer than most people imagine (Television took 20 years, plus time out for a war, to go from invention in the 1930s to takeoff in the early 1950s) (...) having identified the origins and shape of the left-hand side of the S curve, you are always safer betting that events will unfold slowly than concluding that a sudden shift is in the wind. (...) Once an inflection point arrives, people commonly underestimate the speed with which change will occur. (...) expect the opportunities to be very different from those the majority predicts, for even the most expected futures tend to arrive in utterly unexpected ways (...) The leading-edge line of an emerging S curve is like a string hanging down from the future, and the odd event you can’t get out of your mind could be a weak signal of a distant industry-disrupting S curve just starting to gain momentum. (...) The best way for forecasters to spot an emerging S curve is to become attuned to things that don’t fit, things people can’t classify or will even reject."
Then Saffo, through his 4 other rules, give a compelling list of heuristics that I will only quote below:
" just as we dislike uncertainty, we shy away from failures and anomalies. But if you want to look for the thing that’s going to come whistling in out of nowhere in the next years and change your business, look for interesting failures—smart ideas that seem to have gone nowhere. (...) One of the biggest mistakes a forecaster—or a decision maker—can make is to overrely on one piece of seemingly strong information because it happens to reinforce the conclusion he or she has already reached. (...) lots of interlocking weak information is vastly more trustworthy than a point or two of strong information. (...) Good forecasting is a process of strong opinions, weakly held. If you must forecast, then forecast often—and be the first one to prove yourself wrong. (...) our historical rearview mirror is an extraordinarily powerful forecasting tool. (...) The problem with history is that our love of certainty and continuity often causes us to draw the wrong conclusions. The recent past is rarely a reliable indicator of the future (...) You must look for the turns, not the straightaways, and thus you must peer far enough into the past to identify patterns. It’s been written that “history doesn’t repeat itself, but sometimes it rhymes.” The effective forecaster looks to history to find the rhymes, not the identical events. (...) [look for] deep, unchanging consumer desires and ultimately, to the sorrow of many a start-up, unchanging laws of economics. (...) Be skeptical about apparent changes, and avoid making an immediate forecast—or at least don’t take any one forecast too seriously. The incoming future will wash up plenty more indicators on your beach, sooner than you think. "
Why do I blog this? I quite like articles like this because it gives a lot of hints, which resonates with past readings/meetings (see here and there). Currently trying to integrate all these tools and approaches into some more personal approach, I am particularly interested in "find what doesn't fit" or "spot rhythms" hints. I wish he had insisted more on the outcome of such work (story, scenario, decision) and - above all - on what is difficult or hard: how find variables? underlying issues? The part that I am mostly interested in concerns the "data" that could be employed to describe cones or S-curves.
Last, some less organized quotes that I liked: "There is a tendency to overestimate the short term and to underestimate the long term" Roy Amara "Whether a specific forecast actually turns out to be accurate is only part of the picture - even a broken clock is right twice a day" Paul Saffo "Son, never mistake a clear view for a short distance" "The future's already arrived, it's just not evenly distributed" William Gibson "Forecasting is nothing more (nor less) than the systematic and disciplined application of common sense"