In his introduction, the author explains that everybody uses forecasts to take decisions , not only corporations, but also individuals (the forecasted growth of your income will help to decide which house to buy, the weather forecast to decide whether you’ll spend the weekend at the coast, etc.). Hence, being successful only depends on the selection of forecasts you base your decision on.
That’s an appealing starting point since it assumes that you can use existing forecasts to build your strategy on, rather than build your own, and secondly that choosing the right forecasts is a matter of sound judgment rather than specific skills or wisdom.
By drawing our attention to every bias and misconception that might occur in a forecast, Adam Gordon prepares us to make better judgments of which trends/forecasts to take seriously and act upon. And, as his book shows, there are plenty of considerations to take into account (I only comment on those that I was interested in or disagreed with, the book contains a lot more):
1. Categorization of forecasts
When looking at a forecast, one can better ask himself what purpose the forecaster has with the study. The important distinction is between ‘future aligning’ (are we taking the right measures to act upon a trend) and ‘future influencing’ (can we turn this trend into our profit) reports. Basically, the latter ones are about ‘lobbying’ towards a specific direction, and hence are often biased.
I’ve often experienced myself that, when confronted with the same data, people can have very different interpretations and conclusions. As Gordon rightfully points out, the interpretation often unravels which real purposes people have with the forecast.
2. Quality of the information
I work in an industry where even the size of the market is not and cannot be known accurately (I won’t go into details of why that is in this article, but if there’s interest I can develop this in a next article). So, to me, knowing the assumptions that forecasters make is more interesting than the figures they come up with. This is a point that Gordon misses in this chapter I think (that assumptions can be more revealing than the data).
But he is right in pointing out the things that can go wrong when using data in forecasts: incompleteness; taken out of context; ‘overhyped’, out of date. It’s sometimes the only data one might have (it often happens in my professional activities), but still you should never be blind about the exact nature of the data you use.
3. Zeitgeist
This is the chapter that was most inspiring to me. A walk through the philosophers Foucault and Kuhn brings us to the concept of ‘paradigms’, patterns or structures that shape our mind’s ability to understand our environment and that are often prone to influences from our cultural belongings or the period we live in.
9/11 was in a way an example of this. While all the elements existed to predict such an attack, there was a lack of ‘paradigm’ that took into account the possibility of a globalized terrorist force that would eventually proceed with it. Hence it wasn’t ‘expected’, while it could have been –and most probably is from now on.
This is a kind of a ‘zeitgeist’-bias: we are limited by what our times consider as likely or not. No doubt none of the forecasts made hundred years ago would have considered a globalized, instantly connected world. But even closer to us: the end of the Second World War, in conjunction with an increase in technological innovations, led people to believe my generation would be spending holidays on the moon, or have robots doing all of the domestic activities for us. The oil crisis in the Seventies made all forecasts dim and gloomy. The fall of the iron wall in the 80s changed the paradigm again, which led to the 90’s believing in never-ending growth in wealth and stability.
What are the chances of us being limited in our forecasting activities? Will global warming look like a non-event fifty years from now? Will our children look back pitifully at our expectations about the ageing population, the emerging power of the emerging countries, or even the ever increasing connectedness of things?
Perhaps... There’s very few ways to know. But we should take this into account in our forecasting. And this accounts as well for all the experts in the field, which we sometimes survey in a Delphi-study in order to know more about the future. The consensus that is reached is most likely a reflection of our ‘Zeitgeist’.
4. Technology
Two chapters of the book are dedicated to drivers and inhibitors of trends. Gordon rightfully points out that it is the perceived ‘utility’ of the ultimate beneficiary of a trend, which will make it happen or not. If there’s no incremental value to something, it will not be adopted; hence it will not become a (lasting) trend.
While this is not applicable to all trends (ageing population is hardly something people can adopt or not), it is certainly applicable to one of the major drivers of new trends: technology.
Technology can create (not only drive) new trends in itself, and Gordon somehow fails to present a full scope of this –but probably this would take another book- but as he rightfully points out the availability of technology in itself is not a driver, it is the perceived utility of the technology that will.
“The future is never just about what is possible to do. It is always about the choices people and the market will make from among what we can do” (Adam Gordon).
5. System forecasting
Another interesting approach is to investigate the ‘systems’ in which trends occur. Looking at the causes and effects of all the influencing elements help s indeed to discover tipping points of trends, although Gordon states that this method doesn’t help to show drivers and inhibitors of these trends.
This systemic analysis would’ve deserved more thought –or a separate book altogether. There are probably more models to be found other than the reinforcing or balancing loops, and I’m certain there would be ways to show the drivers and inhibitors in such a systemic modeling. Food for thought.
But all in all this is a valuable reading. Gordon develops the caveats with using forecasts in a very complete and lively fashion, enabling his readers to be more critical when using them and to select the most useful ones in their own strategy.
The only weakness of the book lies in the lack of positive examples of good forecasts. Gordon excels at criticizing trend predictions and forecasts, but is very careful in developing his own…
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