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de Neufville + Scholtes D R A F T September 30, 20091999-2004, one could see an average drop in traffic of about 2% a year. This is a direct result ofthe burst of the dot.com bubble and the concurrent 2001 terrorist attacks. However, choosing avery long period, such as 50 years may cover several trends. Indeed, air traffic in the UnitedStates and Boston enjoyed a spurt of growth in the 1980s, following the national deregulation ofairlines, the overall lowering of prices and increased competition. That spurt has since dissipatedand we might best exclude it from any analysis of the current trends. Following that logic, theappropriate period for projecting might start somewhere in the 1990s. But when? Which years ofdata should we include in order to establish a trend?The years selected for establishing a trend can easily change the long-term projectiondrastically. This is because slight changes in the pointer multiply into substantial differences whenprojected far. The example in the Box 2.6, based on data at a major airport, demonstrates howslight modifications in the range of historical data chosen can change the 10-year forecast by20%. Trends are not obvious. They are in the eye of the beholder.[Box 2.6 about here]What drives the trend? We might reasonably ask: what drives the trends? What factors increasethe future development? What might encourage users to want to use new communicationservices? Or hybrid cars? Or any of the products and services our systems might deliver?The process of determining the drivers of future forecasts is also highly ambiguous. Thisis because it is rarely possible to separate out the factors that determine the change in anythingin which we might be interested. As it turns out, most possible explanatory variables follow similarpatterns, and it is almost impossible to unscramble their separate effects.The pattern of change of most developments can often be reasonably described for alimited time by some annual rate of growth, call it r percent a year. This means that, starting froma current level L, the future level after T years is:Future level, L T = L (1 + r) T ~ L e rTThis pattern is called exponential growth. Moore’s Law describing the doubling of computercapacity every two years is one example of this phenomenon. Exponential changes cansometimes last a long time; Moore’s description of change held for over 4 decades since the1960s. However, there are clearly limits to such patterns. In general, they last for some limitedtime. The sales of new products, for example, frequently grow exponentially during early stages,and then slow down as markets become saturated.Exponential change generally applies not only to the trend of the factor that we want toforecast – but also frequently to several of the other factors that might drive or otherwise influencethe factor of interest. For example, the factors influencing the increased use of cell phones mightbe the steady drop in price for equivalent functionality, the increase in population, changes inoverall wealth -- all of which might reasonably experience exponential change, at least for a while.Part 1: Chapters 1 to 3 <strong>Page</strong> 30 of 69
de Neufville + Scholtes D R A F T September 30, 2009Because an exponential process describes the rate of change of many possible drivers,we cannot untangle their effects. Technically, the potential drivers are likely to be highlycorrelated. The consequence of this high correlation is that it is not possible to have much – if any– statistical confidence in any of the effects of the drivers. Box 2.7 describes examples of thisphenomenon. From a practical point of view, we simply cannot have much confidence in theresults of analysis trying to determine the causes of most trends we would like to project.[Box 2.7 about here]Recommended procedureRecognize uncertainty. The starting point for good design is the fundamental reality that wecannot predict the future precisely. Our best thinking, our most thoughtful analyses cannotovercome the unpleasant facts that make forecasts unreliable. Interpreting the historical record isambiguous at best. And even though trends may persist over the short-term, trend-breakersroutinely occur over the long-term horizon for systems design.We must recognize the great uncertainties that inevitably surround any forecasts. As theexamples illustrate, the realistic range of possibilities is often as wide as a factor of two from amedium estimate for innovative, large-scale projects. The range of possibilities is large even formature, well-established activities; witness the 10% deviation from forecast for air traffic forBoston after only 5 years. We should direct an important part of our forecasting effort towarddeveloping a reasonable understanding of our uncertainties. What is the range of possibilities wemay have to deal with over the life of the system?We need to think carefully about the range of possibilities. Sometimes we can reasonablyassume that their distribution is symmetric about the forecast of the most likely future, forexample “plus or minus 20 percent”. However, the distribution is often significantly skewed. Forexample, the most likely long-term price of copper is probably in the range of US$ 1 per pound.Its lowest price will be well above zero, a drop of perhaps US$ 0.50 – but its highest price couldwell be as high as US$ 4, as has already happened. The distribution of copper prices is thusskewed from the most likely forecast – perhaps only minus 50%, but possibly plus 300%.A good way to develop an understanding of the size and the shape of the range ofuncertainties is to look at previous experience in the same or a similar area. The historical recordfor the product or service often gives a good view of the situation. Thus, a range for the price ofcopper is easily available from records of past prices. Figures 2.5 and 2.6 respectively showprices over the last 5 years for example, and back a century. 6 This kind of information gives arealistic perspective on the range of possibilities. For completely new situations, for which there isno historical record, an effective approach can be to look at situations that appear comparable.We might ask, for example, about the track record of predictions for new electronic technologies,when thinking of a new system in that field. We might look at previous experience predicting thePart 1: Chapters 1 to 3 <strong>Page</strong> 31 of 69
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