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| PLEASE CLICK ON THE ABOVE IMAGE FOR HIGHER RESOLUTION AND SIZE |
NOTE: An inflection point is the point on a curved mathematical function where the curve changes direction or concavity. This transition or direction change is easy to see on a graph of the mathematical function; the inflection point is the place at which the curve or function just begins to turn downward or upward; the curve changes from concave upward to concave downward or vice versa. You can find the inflection point of this curve by using calculus. The inflection point occurs where the second derivative of the function equals zero or doesn’t exist, as long as the function is continuous at that point and as long as the concavity is different on both sides of the point. Read more: How to Find Inflection Points | eHow.com http://www.ehow.com/how_6180264_inflection-points.html#ixzz2OypNMgxC
Major events throughout history are not random, although they may seem so. This illusion of randomness or of chaos is merely attributable to the limitations of our perception. For example, if a certain event (the passing of a comet close to Earth, a change in the Earth's electromagnetic poles, revolutionary upheavals of governments, major economic catastrophes) only occurs once every hundred years, it might appear to one individual (with a limited life) as a fluke or aberration.
But if that phenomenon were charted by several successive scientists (try saying that three times fast), they would be able to predict the next time the event would most likely occur, because their collective perspective of time, i.e., several consecutive lifetimes, in conducting the study would be increased. The total perception of the event has a very low frequency of occurrence, which makes it look random during a single lifetime, but given a multiple lifetime view (a longer span of observation), a pattern or wave could be discerned and the occurrence of the event predicted.
It is much easier to see a pattern of a higher frequency, like the sunrise and sunset, which have a much shorter wavelength or periodicity.
The trick, as referenced in the image and in the text further above is knowing precisely when we are at an inflection point. If we cannot do this by using calculus (I slept through my calculus course in college), we try to approximate using a kind of simplified cause and effect analysis, or a special Tipping Point (with thanks to Author Malcolm Gladwell), which is actually called Tolerance Measurement for our purposes. These two approaches become especially important if we cannot be certain of the waveform variables (frequency, amplitude, or second order variations of these variables, i.e., "how much each of the variables is varying," which makes sense but sounds a bit silly).
Cause And Effect:
This is a method where the inflection point is predicted by an historically demonstrated cause and effect relationship. Simply put, when "x" event happens, "y" event will follow. For example, if you were to see me drunkenly attempting to break into your car, you could expect (after you'd called the police on your cellphone) the police to show up and arrest me. While the correlation between cause and effect is close to 100%, the mystery is the "when" -- When will the police arrive. But correlative analysis is, if not perfect, a wonderful tool, especially in shorter term forecasting and prediction.
Tolerance Measurement ("Tipping Point"):
This is a method which is also used which relates to cause and effect but has much more to do with a kind of applied stress analysis. For example, I can know (or at least approximate): how much water I can put into a balloon before it bursts; how many sips I can take out of my grand papa's brandy bottle before he'll notice and confront me; how many times I can use a mechanical part before it breaks and requires replacement; how many persons I can stuff into an elevator car before at least one will become so claustrophobic that he or she will wriggle out of the car for some fresh air -- this is a kind of stress test.
As a university undergraduate, I used to constantly calculate how well I would have to do one the remaining exams to be able to counterbalance the ones which I had taken and on which I'd done poorly. The best example may be how much water pressure can a dam take before it will give out and break.
The good thing about Tolerance Management is that we have an approximation of the "when" of an occurrence. We can test it. A government worries (some of the more sentient employees who show up for work and care about such things) about how much it can tax its citizenry before there will be an outrage of violence and non-compliance on the part of the abused taxpayers.
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I use all three of these tools, i.e., Wave Analysis, Cause And Effect Analysis And Tolerance Measurement Analysis in constructing some of my forecasts. I add to these certain aspects of displacement potential and an intuitive analysis of the growing disparity, worldwide, in the power of (and dependence on) technology versus a flat-line growth in the evolution of Human attitudes and propensities to come up with a basic forecast. When I have finished, I establish an approximate time frame with significant built-in parameters for the time of occurrence and the intensity or significance of the event.
Lastly, I study what other futurists, prognosticators, fortune tellers and professional analysts and prediction experts are forecasting, each of which group uses different tools and techniques, and I crudely measure how many of their forecasts are similar to mine. This is a fairly good litmus test for me -- and it doesn't make me a follower or a mere reproter of predictions by others.
What I derive from looking at a composite chart of the predictions obtained by all of these different sages is an intuitive measure of 1) the likelihood [probability] that my analysis is supported, and is likely to come true, and 2) a good range of the time (parameters) when these things will come true. I call this Convergent Content Analysis, and it serves me incredibly well. It is a fine tuning apparatus which I use in adjusting my future vision. Being pedestrian about it, it is simply that I believe that if many persons reach the same conclusions by many different means, the likelihood of an event at a certain time increases.
You now know all of Douglas E. Castle's forecasting and prediction secrets used in creating content for The Global Futurist Blog. If you try them at home, be certain that you do so only under adult supervision. Yes, I'm grinning.
Thank you, as always for reading me, and for sharing my articles with associates and colleagues using those magnificent (and occasionally annoying) social media tools.
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