I will be suggesting that many existing scientific paradigms are flawed because they are based upon assumptions that conflict with “the facts of life.” There are three basic “facts” related to living organisms that every study of living systems should take into account. I argue that these facts are self-evident. If we look at what we know about living systems, the evidence is so strong in support of these facts as to be incontrovertible. These facts should be used as a foundation to guide scientific analyses.
The three facts of life are as follows.
- Life is a massively multivariate phenomena.
- Life is a developmental process.
- Life functions through high dimensional chaos.
Corollaries
- Living systems are infinitely variable
- Most traits are normally distributed
- Rates are the cumulative sum of interactions
Literature Review
I need to start building a literature review to support my proposals. This will not be easy because I have studied over 100 different subject areas and have thousands of citations to work through. My first stab at this will be to come up with general subject areas. The first subject area that comes to mind is the new science of complex adaptive systems. I spent many years diving into this literature and I think all of the basic building blocks are present in my model. Some of my ideas developed over the years. The development of “the slow change method,” my master’s thesis, the work on the analysis of change in criminal offender risk scores, the work on the age-crime curve, the development of a better health risk model, are a few of the works that come to mind.
Fact #1: Life is a Massively Multivariate Phenomenon
If one looks at the basic structure of life, one finds that it is based upon thousands of genes interacting with each other and the environment. The mathematical laws of interaction tell us that if we consider each gene as a variable and each environmental interaction as a variable, the number of possible interactions is essentially infinite. The formula determining the number of interactions is 2N, where N is the number of variables. If N is only 1,000, we get the number of variable equal to 21,000, which is essentially infinite.
If we can accept the fact that life is a massively multivariate phenomena, then we have a problem with scientific theories that propose that some facet of a living system is caused by one or only a few variables. The examples of this are almost endless. In criminology, there is a “General Theory of Crime” that states that crime is primarily the result of “low self control.” While I like the basic premise, we need to keep in mind that crime is a massively multivariate phenomenon. Low self-control is a “risk factor” but it is not the only risk factor.
Rather than focusing on one or a few factors impacting a process, scientists should be using “big data” to come up with patterns of variables that impact behavior. We need to study the individual, rather than the population. This might initially seem like a step backwards, but aggregating our knowledge using modern artificial intelligence will help us develop better predictive models that are much more useful.
Fact #2: Life is a Developmental Process
The second fact is obvious. Living systems are born, develop, and die. This process ranges from non-existence, through several intermediate stages, and ends with non-existence. I am pointing this out because it is so often overlooked. There is a science of developmental criminology where the developmental criminologists don’t study human development. The field of developmental psychology stopped considering physical development as a factor in behavior about 50 years ago.
Living systems are “developmental systems.” Ignoring development is ignoring one of the biggest variables in the study of living systems. The scientific analyses of human organisms often ignores development, focusing on development in childhood, decline in old age, and treating adults as non-developmental creatures.
In my studies of the age-crime curve, I found that small changes due to development in adulthood can have a major impact on population changes in the crime rate by age. The rate differences are due to highly nonlinear changes in a cumulative distribution function. The message is clear, and that is that “development matters.”
Fact #3: Life Functions through High Dimensional Chaos
Living systems are dynamic. With whichever dynamic process one studies, the trait will be fluctuating over time. Living systems are constantly changing due to massive numbers of internal and external factors. In general, over the long term, this fluctuation tends to create a fairly stable process. We get up each day and look in the mirror and see someone who looks about the same as yesterday. These looks can be deceiving however and we have to understand that we are constantly changing due to many chaotic systems interacting to create “high dimensional chaos”.
High dimensional chaos is what keeps living systems alive. Living organisms are “complex adaptive systems” with many subsystems that interact with each other in environments that are also chaotic. There is a science of complexity, which I will draw upon in discussing this fact, but this science is itself complex and fairly “complicated.” I will be trying to understand high dimensional chaos from a simpler perspective.
High dimensional chaos leads to infinite variation within individuals. Living systems use high dimensional chaos to survive in chaotic environments and reproduce, which creates offspring adapted for survival. When people think of chaos, they tend to think of “out of control.” High dimensional chaos produces a “dynamic stability” or homeostasis. I will be explaining why this is important and providing examples that show how the characteristics of chaotic systems can be used to build better models.
High dimensional chaos creates infinite within individual variation. I will be showing that mathematically, do to the large number of dynamic variables interacting with each other, high dimensional chaos is a certainty. While this may initially seem to be a step backwards in the study of living systems, I will show that this fact is useful. Chaotic systems can be understood though the study of the types of patterns that are generated. Is the fluctuation large or small? What is the history of this system? Are we seeing growth or decline in the outcome we are interested in? What should we expect, given the history of the system? These are all questions we should be asking.
Corollary #1: Living Systems are Infinitely Variable
If we combine the fact that life is a massively multivariate system with the second two facts of life, development and high dimensional chaos, we come to the conclusion that living systems are infinitely variable, both between and within individuals. They have unique tendencies and are constantly shifting internally so that we are never dealing with the same exact organisms at two different points in time.
presenting some theoretical models that rely on a set of theoretical premises that seem to be “facts” because of the large amount of evidence to support them. One set of facts will be presented for the individual and another set of facts at the population level.
Corollary #2: Most traits are normally distributed
I will be proposing that most traits of interest are almost always normally distributed at the population level. This corollary is almost a certainty if the three facts of life are true. The central limit theorem states that the resulting frequencies of occurrence of outcomes that caused by large number of variables are going to be normally distributed. When there is infinite variation the outcome is almost guaranteed to be normally distributed.
Corollary #3: Rates are the Cumulative Sum of Interactions
The third corollary is that rates are the cumulative sum of all interactions between individuals and environments over the time period being measured. This means that any time that rates of occurrence are generated, one needs to understand that individual rates are points on a cumulative distribution function, where the function involves measuring propensity and environment interactions over time. This fact seems to be overlooked on a regular basis.
Conclusion
The individual and population “facts” of life form the basis for the works that follow. It is important to understand these facts if one wants to understand basic phenomena like the Pareto Principle. The explanation of the Pareto Principle is not possible without understanding the nature of the interactions between individual variation in propensity and the environment in living systems.