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Crunchtime: When Events Outrun Plan B
Crunchtime: When Events Outrun Plan B
Not only will events outrun Plan B, they’ll also outrun Plans C and D.
We all know what Plan B is: our pre-planned response to the emergence of risk. Plan B is for risks that can be anticipated, regular but unpredictable events such tornadoes, earthquakes, hurricanes, etc. In the human sphere, risks that can be anticipated include temporary loss of a job, stock market down turns, recession, disruption of energy supplies, etc.
Hidden in most Plan B’s are a host of assumptions that all the systems running in the background of the economy will remain stable. Even if electrical and cell-phone service go down, for example, we assume the outage will be temporary. We assume delivery of energy and food will resume shortly, we assume medical care will be available somewhere nearby, roadways will soon be cleared and so on.
In other words, we assume emergencies will be short-lived and that these non-linear events will leave the rest of our social and economic orders as fully intact linear systems, that is, predictable because the outputs (results) will continue to be proportional to the inputs.
If one road crew can clear five roads of debris, then if ten roads are blocked, we reckon adding another crew will generate a proportional result: two crews will clear all ten blocked roads. This is a linear system and response.
But if for some reason the second crew can’t clear even a single road, and adding a third crew also fails to make progress, the situation becomes non-linear: increasing inputs doesn’t generate proportional outputs.
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Chaos of Weather Record Heat Wave Coming – Fraud of Linear Predictions
Chaos of Weather Record Heat Wave Coming – Fraud of Linear Predictions
This weekend the forecasts are calling for a blistering, dangerous heat wave which is poised to scorch the southeastern U.S. over the Memorial Day weekend. I warned that our computer was showing a sharp increase in volatility in the weather. The winters would spike to new record lows and then the summers would spike to scorching highs. Once again, this is not my personal opinion. If you simply input the data this is what comes up – patterns.
The pro-Global Warming crowd love to send me hate-mail arguing I am not a meteorologist. I reply, neither is Al Gore – the father of Global Warming nonsense. There is far more hidden order to weather and collecting data since only 1850, manipulating it to pretend there is a linear trend and then claim the world will end in 12 years like AOC, begs a lot of question. Why do we need social programs? Stop all taxes including Social Security and let everyone enjoy life while they have it. Kids should drop out of school and enjoy life rather than get degrees they will never use. Why investigate Trump if there will be no presidency in 12 years? Nobody really believes stopping air traffic and driving cars will save the world in 12 years.
The linear analysis of weather used by the Global Warming crowd is complete nonsense and it threatens our way of life along with our future. During the 1950s, Edward N. Lorenz (1917-2008) observed that there was a cyclical non-linear nature to weather, yet the field relied upon linear statistical models in meteorology to do weather forecasting. Lorenz became the father of Chaos Theory. He was an American mathematician and meteorologist. Lorenz was certainly THE pioneer in Chaos Theory.
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Keep It Simple And Complex, Stupid
Keep It Simple And Complex, Stupid
My last post supporting the use of nonlinear models (“You Do Need A Weatherman”) generated some thoughtful responses, mainly along the lines of this post by Ari Andricopoulos entitled “A View on the Economic Model Debate from a Non-economist (but someone who builds models for a living)”. The basic argument is that a full nonlinear model of any significant economic process would be too complicated, and that it was better therefore to stick with tractable linear models, while keeping in mind that the real world is nonlinear:
I build models with data for a living, and I am acutely aware of the problems with using non-linear models to make any sort of accurate predictions – even with huge volumes of data to calibrate it with.
It is not that the systems are linear. They are hugely complex. My problem is that they are too complex to model even with non-linear models. My belief is that linear models do have to be used but with a full understanding of the non-linearity of real life. Also, the whole building of macro-models from first principles, based on ‘rational’ agents, is a complete joke of a way to design a model that is supposed to be used in the real world.
While these points have some validity (especially Ari’s jibe at “rational agent” models), this criticism approaches complex systems from the wrong end—the “complicated” as opposed to “complex” end. A core lesson from complex systems analysis (dating right from its first discovery by Poincare back in 1899, and manifest in the first simulation of a complex system by Lorenz in 1963) is that a simple system can demonstrate complex behaviour. And a simple complex system—yes, I know that sounds like an oxymoron, but bear with me—can tell you most of what you need to know about a complicated complex system.
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