One of the battles in trying to produce real analysis is our hardwiring. We naturally have an attraction toward repeating sequential and palindromic numbers or events that extend beyond what we consciously realize. This is our inherent hardwiring that comes down to our basic species survival instinct. Animals hear just a sound and scurry. They did not actually see a threat; they simply assume everything is a threat. Even an animal will connect a pattern and realize that humans are not a threat when offering food. This squirrel has learned to hang out in a theme park where he can trust humans.
Our instincts come from our survival instincts as well. Our brains are constantly working to make sense of the information we do not even realize is taking place. Recognizing patterns is critical to our ability to learn from past experiences and anticipate challenges as well as threats. We learn in this manner just as the squirrel learns through repetition to trust humans offering food in a theme park.
Essentially, our brain is wired to form associations and then complete the pattern. We are pattern recognition machines. Some people keep forecasting a crash because that is what they have experienced like the squirrel. Others predict that whatever trend is in motion will continue for they cannot comprehend how things change rapidly.
Apophenia is the human tendency to perceive meaningful patterns within random data. Scientists define this as “unmotivated seeing of connections” accompanied by a “specific experience of an abnormal meaningfulness.” The first use of the term is attributed to Klaus Conrad. Pareidolia is a visual form of apophenia, such as when people believe they see Jesus’ face in a piece of burnt toast (don’t worry, scientists say it’s perfectly normal).
One of the most well-known examples of apophenia is the gambler’s fallacy as the call it: wrongly believing that after tossing heads 10 times in a row, the probability of tossing heads again is no longer 50 percent. Of course, the odds for each flip of the coin is always the same. Yet, we comprehend that there is some higher overriding pattern. Then in 2008, Michael Shermer coined the word “patternicity”, defining it as “the tendency to find meaningful patterns in meaningless noise.” But is this really true? Perhaps we assume there is noise so we are incapable to observing complex patterns that make the flipping of the coin subject to two worlds of patterns.
Strangely enough, those in the field of studying the mind may be wrong and it appears are falling into the trap that they may be the victims of our own observations. By trying to be too detached, they prevent themselves from seeing the patterns within what they ASSUME is random noise. They are wrong because there is a higher order that they do not understand.
The father of chaos theory is Edward Lorenz. (1917–2008) who was an American mathematician and meteorologist. Lorenz was certainly THE pioneer in chaos theory. A professor at MIT, Lorenz was the first to recognize what is now called chaotic behavior in the mathematical modeling of weather systems.
During the 1950s, Lorenz 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. It was like trying to measure the circumference of a circle with a straight edge ruler. His work on the topic culminated in the publication of his 1963 paper “Deterministic Non-periodic Flow” in the Journal of the Atmospheric Sciences, and with it, the foundation of chaos theory. During the early 1960s, Lorenz had access to early computers. He was running what he thought would be random numbers and began to observe that there was a duality of a hidden repetitive nature. He graphed the numbers that were derived from his study of convection rolls in the atmosphere. What emerged has been perhaps one of the most important discoveries in modern time.
This illustration of the Lorenz Strange Attractor is incredibly important and was first reported in 1963. Lorenz’s discovery of a strange attractor was made during an attempt to create a model of weather patterns. The actual experiment was an attempt to model atmospheric dynamics of the planet. It involved a truncated model of the Navier-Stokes equations. It is a visual example of a non-linear dynamic system corresponding to the long-term behavior in a cyclical manner that reveals a hidden order we cannot otherwise observe.
The Lorenz Strange Attractor is a three-dimensional dynamical system that exhibits chaotic flow. Noted for its interesting shape revolving around two invisible strange points in space-time, we call them strange attractors. The map shows how the state of a dynamical system with three variables of a three-dimensional system evolves over the fourth dimension (time) in a complex, yet non-repeating pattern. In other words, here is a visualization of duality – what appears to be randomness (chaos) simultaneously has a broader clear pattern of order. The same identical structure appears in light where it is both a waveform and particle, as we see in the economy where we retain our individuality, yet at the same time, we are part of a broader collective pattern. This is the very essence of the invisible hand – or in Lorenz terms, a strange attractor.
Lorenz also discovered in 1969 that very minor differences in a dynamic nonlinear system, which would include the economy, could trigger vast and often unsuspected drastic results. These observations ultimately led him to formulate what became known as the term butterfly effect in 1969. Very tiny changes in what appeared to be minor data at the outset had a ripple effect throughout the entire system and created substantially different outcomes. This term grew out of an academic paper he presented in 1972 entitled: “Predictability: Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?”
What Lorenz established is that complexity may produce the appearance of chaos, yet there is a higher order most cannot see. Computers are free from bias, and as such, they are producing a revelation that there are indeed patterns within the noise.
So, when we look at economic forecasting you will notice that the typical analysis projects the same trend which is in motion will stay in motion. They cannot see the big event which appears like a rogue wave out of what seems to be a normal sea.
Then there are those who keep seeing a crash simply because a market remains high, as in the Dow, or that the dollar must crash because the U.S. has an $18 trillion debt. They fear the rogue wave, yet are clueless how to forecast when they arrive.
There is something much deeper than these superficial observations. Unfortunately, it may require a computer to help us see beyond our own hardwiring and prejudices. We cannot see what we do not understand.