There are several “discoveries” that I have made over the years. Some of these discoveries seem to be original. Other discoveries involve uncovering some largely unknown works that provide insight into the nature of living systems. The net result is a different view of the world.
I am in the process of “taking stock” of what I have learned over the past several decades. I think about many of these things on a daily basis, but have not attempted an organized inventory. Part of this process has been to look at the topics I have studied. I have been building web pages to highlight some of the main discoveries. I will try to organize the basic concepts here.
A Chronological List
- Change is a zig-zag process
- The world is normal
- Zig-zag fluctuation is universal
- Zig-zag fluctuation is constant
- Brains are neuro-chemical computers
- Traits are density functions of states
- Many scientific paradigms are flawed
- A proposed solution to the age crime curve puzzle
- Latent traits are normally distributed
- The propensity-opportunity-threshold model
- The nonlinear problem with rates as points on a cumulative distribution curve
- Crime rates may be related to adult to child ratios 20 years previously
- Health is a normally distributed latent trait
- A proposed solution to the Pareto puzzle
A Conceptual Organization
I have tried to organize my discoveries in a logical fashion so that you have the concepts needed to understand discoveries farther down the list when you understand the higher level discoveries. These can be grouped into individual and population level discoveries.
One might think of the study of the physics of living systems as similar to the study of gasses. At the individual level, each gas molecule is bouncing around at a different speed and direction from the others. At the population level, we can study the properties of groups of gas molecules like heat, pressure, volume, etc.
With the physics of gasses, we can deduce population level phenomena from the individual level and vice-versa. The physics of living systems provides the same reversibility in conceptual models. It could be argued that any theory of living systems should be reversible.
Individual Level Discoveries
I spent my initial years studying life at the individual level. I discovered that life has its ups and downs. Creating meaningful change is hard. As I began to study others, I found the same pattern. People are always changing. Eventually, I began to realize that this was a universal phenomenon. All living systems are constantly in a state of flux. Usually, the pattern hovers around some set point, but that can change at a moment’s notice. As I began to look for answers, I found the science of complex systems. From there, I realized that individual organisms function through high dimensional chaos. I have been scratching the surface in trying to understand how high dimensional chaos functions and what implications this provides for science.
High Dimensional Chaos
The discovery that “high dimensional chaos” is fundamental to understanding the nature of living systems did not simply burst into consciousness. It took me many decades before I came to realize that living systems run on chaos. Specifically, living systems use “high dimensional chaos” to operate with sufficient preparedness to deal with almost any conceivable eventuality.
From my observations, it would seem that most people don’t understand this concept. They set out to change themselves or others with no understanding that perturbing a chaotic systems can have a wide range of effects. People try to control chaos. The verbalization of this idea gives you a sense of the futility of this endeavor. You can’t control chaotic systems.
Once you understand high dimensional chaos, understanding living systems becomes much easier. You can begin to work within a chaotic framework. A push in one direction with create a response that may be in the direction you want or may be in the opposite direction. By studying the results of any attempt to create change, one gets a sense of what to try next.
Individual Differences
Individuals vary between each other and also within themselves from moment to moment. We are never the same individual at two different moments in time. I will argue that this is true for every type of organism, but especially true for humans. As proof, I will suggest an in-depth study of Walter Freeman’s work on neurodynamics, which he developed through the study of electroencephalogram (EEG) outputs. The most important facet of his discoveries, from the physics of living system’s perspective, is that brains are constantly reconfiguring themselves chaotically in every instant. We are never the same person twice.
The implications of this discovery are profound. The study of group tendencies might result in meaningful knowledge, but if we truly want to understand individual behaviors and outcomes, we need to sturdy each individual over time through time series analysis. This is so far removed from the current practice of making before and after measurements and inferring change as to constitute a completely new scientific method. It is an axiom of studied of change that you cannot measure change at two time points.
Population Level Discoveries
My work at the population level began with an attempt to find the solution to the age crime curve puzzle. There is a persistent pattern in crime rates where crime rates start at zero for infants, climb to a peak at about age 18, and then fall to near zero for aged adults. This pattern is impossible to explain using current scientific methods. I found the solution to the age crime curve puzzle and wrote an online book called “The Criminological Puzzle” that explains what I found. I discovered that one needs to look at cumulative distribution functions and human developmental trajectories in order to understand the age crime curve.
As I went on to work as a data scientist in healthcare and marketing, I found that the same patterns I observed in age-crime data were present in both heath data and marketing data. These discoveries led to a new way of thinking about population level data. I was eventually able to come up with a model that explains the Pareto principle. It does not appear that anyone was even looking for an explanation, so this science appears to be far in advance of anything currently in progress.