Suppose we roll two dice. I cannot know the sum of the two upper faces when they come to rest. However, if I say that it is most likely to be seven and that they will, more likely than not, total between 5 and 9, inclusive, I will have a superior knowledge of futurity over the person who believes that the total will just as likely be 12 as any other total. That person may, in any specific case, be correct - the total may be 12. However, whatever methodology they are using to gain their view of the future, if used consistently, it will prove very disappointing. Because the dice might, indeed, total 12, we cannot claim precise knowledge of the future when we state that that the total will most likely be 7. However, by understanding the underlying mechanisms that create the future, we can claim to have a superior knowledge of futurity.
How does one do that? I use a five step process which I describe below. It may seem formulaic. It is not. Rather I am presenting a complex process as a formula. If followed with reasonable judgment, it is actually quite robust. As you consider it you will realize that it is an extremely difficult formula to follow, in practice.
Rather than a set of unrelated trends, the proper analogical model for how the present becomes the future is a system of endogenously related time series equations. Such systems are frequently characterized by sudden discontinuities, similar to those studied in the mathematics of catastrophe theory. Only with such a model can one correctly anticipate the transformations inherent in the emergence of a global Information Age civilization.
By 2100 A.D. everyone will understand that there was an Information Revolution. Everyone will understand that the revolution was not just about new technologies, but, like the Neolithic and Industrial Revolutions before it, was also a comprehensive rewrite of the cultural frameworks and social institutions of civilization.
- A scientific breakthrough takes place
- It leads to new technological opportunities
- These are capitalized upon by enterprises
- The products and services introduced change the culture
- The cultural changes lead to changes in prevailing mores, demographics and politics
This endogenous connection can have profound results. For example, consider the graph below of three unrelated trends. It represents how most pundits, professors, including Futurists, and politicians view the future. It is inherent in the Megatrends series of books. Even though Ray Kurzweil, in 'The Singularity is Near' presents his trends in log scales, he also creates the impression of trends operating more or less independent of each other.
Now, in the graph below, I kept the same trend for each equation. However, I modified the value at t(n) by by adding a percentage of the t(n-1) value of one of the other trends and subtracting a percentage of the t(n-1) value of the other trend.
You will note that the trends become more complicated. The dark blue trend line remains a relatively straightforward exponential curve, but it grows substantially more quickly. The Magenta trend rather than decreasing at the rate of 5% per period, remains relatively constant until period 9 when it suddenly begins to grow exponentially. Lastly, the yellow trend line which grew at a moderate exponential rate before now grows for a period, apparently becoming asymptotic in the low forties, but then around period 8 begins to decline exponentially.
There are two important lessons to learn from this. First, no matter how well researched and supported a future trend may be, if it doesn't consider what is happening to other, related trends, it is not at all trustworthy. Neither in rate of change nor even in the direction (sign) of change. Second, this is a visual example of a global transformation. In this case, up to period 4 everything seems rather calm. There is an aura of normalcy. Then, after period 4, to around period 11, trends do unexpected, even dramatic, things. This portion of the graph can be considered a period of discontinuity, what I am calling here, A Transformation. After period 11, although the trends are different, they once again become more predictable.
Lastly, there is a general assumption that tomorrow will be like today only more so. To clarify this, let's consider population, natural resources and Gross World Product as the trend lines yellow, magenta, and dark blue, respectively. We then say, in the future, population and GWP will increase, as is happening today and the amount of natural resources will decline as we 'use them up.' That is a 'tomorrow is like today only more so' viewpoint. However, we see that the second graph would cause us to conclude that population, after growing for some time will begin to start decreasing. Rather than natural resources decreasing, they remain constant for a period of time and then begin to grow rapidly. Lastly, while GWP does continue to grow, it grows faster than we imagine reaching a value 40% higher than the trend line by period 15.
It is worth noting that prior to period 4 and after period 11, the future was like today only more so. However, today, we are at the equivalent of about period 5 and for us, at this time, the future is not like today only more so. This is another, conceptual way, to describe The Transformation. It explains why the synthesis that I have created of these twelve major events lead to a superior knowledge of futurity, substantially different than what is predicted by virtually anyone else.
A basic tenet of Future Studies is the idea that technological advancements, market acceptance, demographic shifts, etc. generally follow some type of sigmoid function. Although it is generally not the best descriptor, we generally think in terms of logistic curves, often called S-curves. They are advantageous because they are described by the relatively simple equation P(t)=1/(1+e^-t)
These sigmoid functions so commonly describe actual events because they fit a general scenario. You will note that the first half of the curve is growing in a manner similar to an exponential growth curve. This happens due to what Ray Kurzweil calls 'the law of accelerating returns.' While a valid description of early events, at some point in time the more familiar law of diminishing returns takes over and the curve begins to ever more slowly approach an asymptote.
When logistic functions are used to describe an endogenously related system of equations, the results can be spectacular. They are extremely sensitive to small changes in the endogenous relationships. Here I explore one such model. Remember, all three equations, if not affected by one another would look like a logistic function. However, when each is modified at t by a function of the values of the other functions at t-1, none of the resulting graphs even remotely resemble a logistic curve.
If you place a piece of paper on the right side of the graph with the left hand rule at 21, you will be looking at trends that, while interesting, portend nothing alarming in the near future. However, as is clearly evident, a dramatic event takes place at about 23. What we can see is that as the analogical model becomes an ever more accurate reflection of reality, the potential for periods of extreme discontinuities becomes ever greater.
There is another type of equation that is well worth considering when creating one's analogical model. It is becoming extraordinarily important with regard to how information is disseminated, especially on the Internet. Imagine that you send an e-mail to 100 people. Because the e-mail is of interest, some of those 100 recipients forward the e-mail to others, who forward the e-mail to others, etc. This also works well with teh concept of 'share.'
We can then think of the ultimate size of the audience as the sum of the series 100* r^t. where r = replication rate and t is the generation of replication. A replication rate of 50% would result in the series 100+50+25+12.5... = 200. Now let's consider a series of e-mails where each e-mail experiences a replication rate 0.2% higher than the previous one. Below is a graph of the result. As r --> 100%, the sum --> ∞.
If you place a piece of paper over the right hand side of the graph with the left hand rule at .7, there would be nothing to suggest to you the behavior of the function when t >.9. This is relevant primarily to the creation of Internet communities that have a high degree of interest within a specific range of topics.
In contrast, the mainstream press is dominated by several visions of the future that clearly are driven by ideological comfort or unrestrained flights of fancy.