Complexity Theory and Extralogical Reasoning

Almost everything in this universe—from galaxies and clusters of galaxies to solar systems and planets, to climates and ecosystems to industries and economies—comes from things organizing themselves. They are SELF-organized. Most things can’t be understood by straightforwardly analyzing the parts/variables, like in math, physics, and engineering; only by accounting for unknowns and appreciating the complexity of the interactions BETWEEN variables can one hope to understand the rest of Nature. The former type of thinking is called reductive or reductionistic; extralogical reasoning calls the latter scientific holistic thinking.

 

Complexity theory studies complex systems such as economies, ecosystems and their evolution, societies, and various related phenomena. Given these are obviously important parts of the World and that complex systems illustrate the power of self-organization, the necessity of scientific holistic thinking, and the prevalence of nonlinearity (or nonlinear change), the merits of understanding complexity can’t be overstated. 

 

A complex system (which can have many complex SUB-systems) is a system with many known and especially unknown variables that undergo complex interactions, limiting its predictability. The variables could be anything, depending on how it’s modeled: people, businesses, stock markets, animals, bacteria, etc. as well as their respective behaviors. They are characterized by their self-organized evolution and limited predictability--or mere “guess-ability.” In order to remain a complex system, the variables/elements must have the right combination of interdependence and independence and the system must maintain its approximate identity as it perpetually changes; otherwise, the system will veer toward a monolithic system, numerous independent systems, or an incomprehensible mangle of activity.    

 

“COMPLICATED systems” can be very complicated, but they tend to involve only known variables with fully predictable interactions, making the systems, in turn, fully predictable. Examples are mechanical watches and automotive systems. Chaotic systems are treated as having mostly known variables, expressed in the form of nonlinear differential equations, that are highly sensitive to initial conditions. However, though analyses in chaos theory and nonlinear dynamics don’t directly account for unknown variables, unknowns can still have profound effects if they alter the initial conditions of the primary variables. Examples include weather/climate systems (which also possess complexity), nonlinear fluid systems, and three-body gravitational systems.

 

A system’s parts tend to have finite lives. The preservation of the constituent components of complex systems is highly dependent on the systems’ naturally-selected compositions of variables and their interactions, and the system changes over time. Though the true destruction of an entire system is not easily achieved, altering it too quickly comprises its health, drastically shortening the life expectancy of its parts. Since the system’s evolution is mostly or exclusively self-organized, the selective process follows the path of least resistance, only reliably selecting for traits that are “just good enough,” leading to numerous weak-links and a resultant susceptibility to runaway effects. 

 

The idea of quickly figuring out what works and proceeding to way over-rely on it at the expense of vulnerability and mediocrity is such a common theme of the path of least resistance you could almost call it the path of least resistance itself. The designers of an engineered system could put in failsafes to ensure that the breakdown of important functions don't cause systemic collapse, but failsafes aren't reliably produced by self-organized processes. Therefore, when an essential function arises in an evolutionary process, especially within a constituent of a complex system, it is inevitable that it will be over-relied upon. This is extralogical reason’s principle of over-reliance. Oxygen is a wonderful boon to metabolism, but it’s so over-relied upon countless species will die within minutes without it. Since humans and the evolutionary processes that gave rise to them follow the path of least resistance, essential epistemic measures like heuristics usually become over-relied upon, as well—such as the “all-else-being-equal viewpoint,” comparing oneself to other people, and the availability heuristic.

While systems are far from infallible, they are “self-omniscient,” and given the inordinate number of unknown variables and the intricacies of the interactions between ALL the variables, no arbitrarily large group of human beings could ever possess enough information to effectively PLAN an economy. Moreover, active interference with complex systems inhibits their natural evolution, which requires culling elements when they are no longer suited for the systems’ ever-changing environments. Harming such systems is easy (and humans cause tremendous harm to them); “helping them” is potentially dangerous.  

 

Socialism’s economic foundations run directly counter to complexity theory. High taxes and restrictions on free enterprise inhibit the natural evolution of economies. If governments aren't careful, many types of bailouts can take precious resources (directly and/or indirectly) away from prosperous entities and put them into failing ones, also inhibiting a system's evolution. Active socialistic measures may lead to less TOTAL economic slumps, but the ones it fails to prevent tend to be far worse. Biological evolution has produced some fantastic things, and it did it entirely without Planetary oversight or “species and ecosystem bailouts.” In sum, while a government obviously must have some direct involvement in an economy, anyone who thinks an economy can be reliably planned by a central body must not only rethink their understanding of economics, but their understanding of the Universe itself.     

 

Characteristics of Complex Systems 

 

Self-Organization

Self-organization means what one would assume--a system of elements' ability to organize itself without direct human intervention. More specifically, it's a system's ability to give rise to phenomena just by obeying certain rules and following the path of least resistance. Since people tend to follow the path of least resistance, especially in large groups, and since they are part of complex systems, much of what occurs within society need not be consciously organized. What appears to be conscious collusion or the scrupulous development of a field's practices could oftentimes be little more than what emerges from people looking after their own interests, mimicking their colleagues, and following the path of least resistance. People fail to notice the prevalence of self-organization within society due to an overestimation of the rationality of human decision-making, an underestimation of people's tendency to follow the path of least resistance, and the misbelief that causality has to be tangible, ascertainable, and satisfying--the causation bias.   

Self-organization is even more responsible for the evolution of society’s readily-available pool of beliefs than that of individual fields. Beliefs have many attractive properties other than basis in logic and evidence. Beliefs that are gratifying, motivating, glamorous, and reinforce other beliefs are often more robust and easier to find (especially if they make people money). The selective criteria are extensive and complex, and the pool has little oversight. Unlike in science and engineering where beliefs/principles are very self-CORRECTIVE, those within society would be better described as self-ADAPTIVE.

Anyone who doubts the effects of self-organization and people's tendency to conform can look at the history of riots, inquisitions, and relevant research, such as the Stanford prison experiments. Complexity theory's "information cascades" provide a quantitate description. Even if not ideologically, everyone has a tendency to conform. A person having a "conformity threshold" of one means they will mimic the behavior of a single person performing an act; someone with two will commence said behavior after two engage in the act--and so on. If you have a continuity of thresholds from one to a hundred in a group of one hundred or more people, you can elicit a mass behavior involving one hundred individuals. This, of course, is not completely reliable since a continuity of thresholds is necessary, but it illustrates how such things can occur.  

    

Thus, complexity theory and the self-organization inherent to complex systems expose many misconceptions about causality, human psychology, and society.    

Antifragility of the Whole and Fragility of the Parts

“Antifragility,” as defined by Nassim Taleb, is the quality of being robust and adaptable. As mentioned, natural selection requires ELIMINATION; the parts must have a minimal level of fragility and dispensability. However, complex systems are always limited in how QUICKLY they can adapt. Over a prolonged period, they might be highly adaptable, eventually turning existential threats into indispensable assets, such as atmospheric oxygen; but in the short-term, they may have almost none. Humans alter Earth’s ecosystems far faster than they can adapt, and the First World’s industries are now changing at rates that challenge the economy’s adaptability.   

 

Perhaps some are inclined to question the fragility of the lives of individual species. And if the path of least resistance only builds traits that are just-good-enough, how has evolution produced such awesome things? 

 

First of all, virtually all steps along the way of evolution are mediocre; it’s just that there’s been enough competition for survival over enough time that the total steps have amounted to great things. Secondly, however great evolutionary achievements may be, you might feel very differently about the fragility of animal life if you weren’t a modern human living in a cushy environment for an inkling of geologic time. If you lived in the wild for the duration of a species’ lifetime, say a million years, your opinions about the efficacy of mammalian anatomy and physiology would probably change a good deal. If you were deprived of oxygen, you’d most assuredly be inclined to question the prudence of being so reliant on it. Over ninety-nine percent of species that have every existed are extinct. Similar numbers exist for businesses.

 

Moreover, one must be careful of how they view the mortality of systems. The human view is drastically different than the viewpoint of large systems. 

 

To most humans, life on Earth only means life as THEY know it. Any scenario that changes that and threatens their existence is perceived as the end of the ENTIRE Planet—even if the extinction would ultimately be as low as twelve percent. The Earth could undergo a SEVENTY-percent extinction where all mammals and most reptiles die off, and Earth’s biosphere would remain large, even if “unhealthy.” Humans themselves are extremely fragile and dispensable, individual classes of animals, such as mammals, are fragile and marginally important; but though its “HEALTH” can be drastically reduced, the biosphere is nearly immortal. To people, an eighty-percent human depopulation and loss of fifteen percent of mammals would be considered the end of life on Earth; to the Earth, so long as the runaway effects were relatively contained, it’d be little more than having the flu. 

 

Similarly, the life of the global economy only means life as contemporary First-World citizens perceive it. Any scenario that significantly changes it in a harmful way (which will inevitably happen) is perceived as total destruction. So long as resources are sufficient and there are no major natural disasters or military conflicts, the global economy is, likewise, nearly immortal. Loss of resources, wars, social uprisings, and natural disasters (though perhaps inevitable) are the only things that truly threaten its survival.   

 

Guess-ability 

Since complex systems have countless known and unknown variables that interact in unpredictable ways, one can only guess as to how they fit in with each other--and, ultimately, what to expect from their long-term behaviors. The availability heuristic bias is people's tendency to overestimate the importance of the information they have about something and vastly underestimate both the amount and importance of the information they DON'T have. A specific manifestation of the bias is what extralogical reasoning calls the quantity heuristic bias: people's tendency to confuse quantities and percentages. Since people often know huge amounts of information about complex systems, they all but automatically assume it's a much higher PERCENTAGE of the relevant information than is actually the case (knowledge idolatry, the tendency to idolize knowledge to the point where people lose sight of relevance and applicability, reinforces this). In the case of economies, in order to predict future trends, one would have to be able to predict SOCIAL trends, which may not even be GUESSABLE. However, since systems obey various laws—ecological, biological, economic, sociological, etc.--and maintain their approximate identities over prolonged periods (at least on human timescales), much can be known or guessed.

Predictions in general have never been as successful as most think. It's true that all economic rises and falls have been predicted--but so haven't all those that didn't happen. People make all kinds of predictions and prognoses, and as the scholar Duncan Watts and I have observed, the ones that prove true are more likely to be remembered. In other words, predictions have a confirmation/survivors bias. Moreover, a lot goes on in complex systems; it's easy to take educated guesses about what COULD or could HAVE occurred. This gives complex systems and other things RETROSPECTIVE "predictability," reinforcing the misimpression of true predictability.  

Contrary to popular impressions, the social sciences, subjects laden with unknown variables and complex interactions, are much more complicated (defined in the general sense) than physics. Don't be fooled by physics courses' reputation for being difficult. How hard you can make a test isn't just based on the topic, but on the level of understanding of its creator. Generally speaking, social scientists simply can't understand the relevant topics well enough to construct tests that are especially difficult. This is not a dismissal of these fields. Many useful things are known about them; it's just that too much ISN'T known. As an avid student of Nature with a little experience solving basic physics problems and deriving formulas, I have some knowledge of electromagnetism and quantum mechanics generally, but my understanding of PHYSICS electromagnetism and quantum mechanics is pathetic. If you gave me a few weeks and let me review a bit, I could come up with an exam on these subjects that would require some important knowledge, but the test would be very easy. This isn't, however, a reflection of the difficulties of electromagnetism and quantum mechanics, only my lack of understanding.    

And the above isn't just based on theory. As Duncan Watts points out, people are quick to say many things "aren't rocket science"--something humans are remarkably good at. Space agencies can put rockets into orbit around Mars, which requires profoundly accurate predictive calculations. Mankind's success at treating mental illness and forecasting the stock market and the economy pales in comparison.  

 

Self-organized Criticality, Power Laws, and Nonlinearity

Many things in this universe undergo multiplicative (including exponential) rather than additive change. The cliché “success begets success” is a good example. The more successful an actor, the better the chances other good actors and directors will want to work with him; not only does this itself improve the movies and marketability, but now production companies are willing to put more money into marketing and special effects, improving the movies and further improving his popularity and success. And the cycle repeats. “You need money to make money” is another example. 

 

Multiplicative change (perhaps combined with other factors) leads to the above three traits. Like with most things in the Universe, a complex system’s progression is volatile (nonlinear), and it exists in critical states, on the precipice of significant change. Power laws express an exponential relationship between the frequency and magnitude of an event: If an event within a system happens half as often, for example, its magnitude might be four times greater. This is represented by "fat tails" graphs, which arise in Nature much more frequently than bell curves. There are power laws for Earthquake’s, extinctions, research citations, economic crashes, and wealth and population distributions--among other things. 

 

Moreover, power laws from computer simulations of such events comport with relevant real-life data that were/are thought to necessarily have specific major causes. Simulations of extinctions showed that you get the same behaviors even if the biosphere changed on its own, showing that extinctions don’t necessarily have to be the result of apocalyptic catastrophes like asteroid collisions. Similar research found that if stock market investors used dubious heuristics for investment rather than formulated measures, you’d get the same market behavior. These results typify the general deficiency of reductive thinking, which is bottomed on extralogical reason’s causation bias—the natural human tendency to be way too quick to assume that the relationship between cause and effect will be ascertainable and satisfying.     

 

Most things in the Universe change nonlinearly. In addition to complex systems, most human affairs and biological phenomena, like the effects of aging, progress nonlinearly: Improvement at activities, learning of life lessons, the slowing of metabolism as well as the loss of physical strength, endurance, and recuperative capacity (due to aging) are all examples. LINEARITY and linear phenomena, such as linear differential equations and Gaussian distributions (e.g., the bell curve), better comport with people’s flawed intuitions about Nature and are easier to incorporate into school courses, which obscures the prevalence of nonlinearity. Because complex systems epitomize nonlinear change, studying it cultivates an appreciation for the “the linear illusion”—the misguided notion of a linear universe.          

 

Connectivity 

Complexity theory also deals with networks, often ones WITHIN complex systems. In the 60’s, research revealed that all Americans are, on average, acquainted or connected by “six degrees of separation”—a friend of a friend is one degree of separation. This might sound like a bunch of hippy nonsense, but it’s heavily confirmed by complexity research, among other things. The strength of the connectedness of a social network is mostly the result of the commonality of long-distance friends and, secondarily, to “hubs,” “social giants” with lots of connections (which obeys a power law). These networks came to be called "small-world networks." The brain's neurons have a small-word architecture with only two degrees of seperation and less hubs, increasing speed and efficiency while minimizing its vulnerability to run-away effects resulting from brain damage. Earth’s network of animals, on the other hand, has ten degrees of separation and is more hub-oriented, making it vulnerable if a hub should go extinct. The Internet was originally, in part, a communication and information storage network engineered to minimize such weak-links.        

 

Easy for Modern Man to Harm, Hard or Impossible to Help 

The life expectancy of the constituent elements of a complex system hinge on the system’s naturally-selected composition and interactions; altering them can elicit runway effects. Systems are self-omniscient; humans could never have anywhere near enough relevant information as the systems themselves. The effects of intrusions can only be guessed. 

 

In addition to the ill-effects of dirigisme (governmental intrusions upon economies), this is supported by real-life measures that attempted to protect declining prey species’ by depopulating their predatory counterparts—only to make things worse. In some cases, for example, the predator’s excrement might provide essential bacteria, fertilizer, and/or pollen for the prey’s food source; in others, the predators might keep the prey’s competitors in check or at bay. People really don’t know what effects altering compositions and interactions will have.  

 

PREVENTING a condition is not the same thing as TREATING it. You can prevent yourself from getting lung cancer by not smoking; that’s not at all the same thing as concocting an effective treatment for those who’ve acquired it. Preventing harm to complex systems is possible—don’t pollute the atmosphere with astronomical amounts of waste; don’t kill species off; don’t transplant countless species across continents; don’t make taxes way too high; avoid bailouts--etc. But once a massive runaway effect commences, it could be too late.    

 

 

 

As extralogical reasoning has said many times: 

No person can possess any attribute, resource, or ability in an isolated universe: No person can possess any attribute, resource, or ability in the absence of others; and no person can possess any set of these things in the absence of a complex external environment. Life is not simple, static, and predictable; it’s complex, dynamic, and merely guessable. 

 

The above should sound much like the rest of the post. In addition to the fact that large and small complex systems exist in people’s external environments, in one’s thinking and psychology and the people they deal with, one must account for many known and especially unknown variables that all interact in complex ways. A psychology might be too intangible to be a complex system, but most of the thinking organ is unconscious, necessarily making it a repository of unknowns. Psychologies are also dynamic and undergo much nonlinear change. Therefore, life and complex systems require scientific holistic thinking, and, thus, any study in it ought to be considered enlightening.