Extralogical Complexity Part 2: Its Laws and the Structure and Dynamics of Self-Organization

Nature’s elements, by definition, arise naturally. Novelties emerge from randomness and self-organize. Thus, one must understand emergence and self-organization to understand Nature.

 

Nature is an emergence that exceeds the sum of its many parts, or substrate. Such an emergence can only occur if the system of variables undergoes feedback that readily allows outcomes to surpass its initial causes. The laws of causality and energy conservation are always satisfied, but this requires accounting for an arbitrarily large number of intermediary events, which is next to impossible in any real network. Part one showed that synergetic emergences are made possible by the prevalence of nonlinear change. This article, part two, is a treatise on the structure and dynamics of self-organization in emergent systems, also known as Complexity theory.

 

Complex systems theory is a multi-disciplinary subfield and cousin of chaos theory. In extralogical reasoning’s model, Complex networks are secondarily its tangible components/substrate and primarily its emergent system that’s unpredictably different and more powerful than the sum of its parts. Therefore, the network equals the sum of substrate and the emergent system. A society, for example, is secondarily its populus, government, and institutions, etc. and primarily the self-organizing system they give rise to. 

 

Complex networks--like ecosystems, economies, societies, and Nature generally--are much larger and contain far more unidentifiable variables than chaotic systems. Complicated systems, as ER calls them, are systems/networks with many interacting variables that are fully predictable and can be analyzed reductively, or just by assessing the parts. Examples include sophisticated watch and automotive systems. Although often modeled with less variables, chaotic systems undergo more Complex feedback, making them too sensitive to perturbations and initial conditions to have more than short-term predictability. But within that window of predictability, they, too, can be analyzed reductively. Control theory and chaos theory/nonlinear dynamics investigate both types of networks. Complex networks, on the other hand, are only guessable and must be analyzed by holistic analysis: analysis of the whole, analysis that looks at how the parts fit together.     

 

Accounting for how they fit together means understanding the Complexity of their interactions. In modern society, as in a psychology comprised of beliefs, the parts are heavily entangled. It is the Complexity of interactions that separates substrate from system. The substrate’s closer to the sum of the parts and their direct effects; the system more reflects the indirect effects owing to the Complexity of their interactions. 

 

Consider Disney. Whether you invest in Disney not only impacts Disney, but also how you invest in an arbitrarily large number of things—stocks, bonds, businesses, personal items, etc. Buying Disney Plus doesn’t just influence your choice of other streaming services, but how you spend generally. Streaming services, likewise, influence how you invest your time, which affects other investments. What movies and shows you watch influences your tastes in other entertainment, technology, clothes, etc. Some changes could be due to conformity to trends; some merely a change in taste; and some a response to general noise, or random stimulus. Part one cited research that shows human decisions are surprisingly affected by noise. (see part one of Noise by Daniel Kauhnmen, et all).

 

These behaviors generate feedback that merges with other feedback; merged feedback then turns to oscillations by acquiring periodicity like seasonal or micro or macro-economic cycles; some oscillations grow and stabilize into patterns/trends called Complex/network rhythms such as market, business or migrations cycles and their undergirding trends; and these rhythms are collectively organized by coordinating Complex rhythms, which represent systemic infrastructure (both explained in greater detail in the section on the four laws). Thus, a Complex system perpetuates.        

 

The biosphere is a Complex network, and its prolific success over the past four billion years showcases just how much can be achieved without human intervention. Human agency can’t compete with self-organization even in the human world. Socialists might think they can turn society into a simpler, tractable network, but all that does is turn it into a poorly run Complex one. As mentioned in part one, power laws emerge in computational experiments exactly as they do in the real World. Rational human agency not required. When computer-simulated stock traders and companies invest based on dubious heuristics, markets rise and fall with mimetic precision.       

 

Unfortunately, as has been shown in countless posts, people are blind to self-organization and Complexity and underestimate the influences of noise as well as their tendencies to conform and follow the path of least resistance. So long as an animal’s models of reality at least roughly approximate the facts, vertebrate cognition will prioritize self-consistency over consistency with reality. To avoid a perceived reality of disjointed thoughts and images, subconscious and sensory inputs are doctored to create the coherent experience people call reality. This “fallacious linearization” of (actual) reality is responsible for most fallacious reasoning and blindness to Complexity.

 

The Sham has been discussed at length in other posts: the widespread con/delusion that rational human/anthropomorphic agency predominates society, that people make decisions way more rationally and objectively than the case, that the course of societal events is directed by individuals and institutions in power rather than its own natural evolution. Rational human agency is the opposite of Complex agency or Complexity, the true governing agency of the World. The Sham is society’s counterpart to the cognitive system and perceived freewill. As Yuval Noah Harari author of Sapiens will tell you, group cohesion and empowerment require belief in tangible unifying agencies, and the human thinking organ is designed to create whatever delusions are necessary to ensure it. 

 

But this isn’t (just) a rant: The Sham is major component of the structure of society’s self-organization, part of systemic infrastructure. The cognitive system and perceived freewill play a similar role in a belief network.     

 

To better appreciate the structure and laws of Complex organization, one must look first understand evolution from the Complex systems perspective.    

 

 

Network Evolution

 

Owing to powerful connectivity discussed in part one, nonlinear change occurs when effects/final outcomes exceed causes/initiating events. Network flux is the birth and death of the networks smaller or “fragile” parts that Complex causation acts upon. ER treats this as random variation. Nonlinear change acts upon random variation, giving rise to emergence, self-organization, and synergy. 

 

A network’s natural selection and random variation are good at finding the “right” feedback. One should not encourage “wrong” feedback, or lack thereof. Failing species, businesses, and industries are more likely to be enmeshed in maladaptive feedback. Taking resources away from (more likely to be) sources of adaptive feedback impedes it and reinforces maladaptation. For example, an indiscriminate bailout policy pulls money from successful entities or “good” feedback loops toward maladaptive ones, resulting in less frequent but greater crashes. The same is likely to occur if you took an environment’s resources away from prosperous animals toward one’s no longer suited for the environment. High taxes often take money away from higher generators and distributors of wealth and put it in the hands of lower ones.

 

Indiscriminately bailing out dying constituents impedes random variation and natural selection; restraining prosperous ones, likewise, impedes natural selection. Hampering the network’s evolutionary mechanisms is fragilization, the suppression of a network’s antifragility (robustness and adaptability from Nassem Taleb). Treating them as fragile fragilizes them, making it self-reinforcing. How could it not? It suppresses random variation and natural selection. Although negative outcomes and occasional crashes are absolutely ensured, networks’ natural evolutionary mechanisms have proven time and time again to find the right feedback loops, demonstrating their antifragility. The wrong types of governmental “control” suppress network flux/random variation and the force that drives it—self-interest. Aggressive efforts at control also tend to unduly increase dependencies that make networks susceptible to runaway effects. In fact, such efforts tend to decrease governmental control over the system, which will be explained when the law of anticontrol is introduced. 

 

A limited disparity in resources spreads them too thin, resulting in too few components antifragile enough to survive crises. For instance, if an industry has too many small businesses, the industry is fragile and, therefore, vulnerable to shocks, like a sudden drop in demand or resources. This remains the case so long as a component(s) or constituent(s) doesn’t cross the monopoly threshold: when the component becomes so prosperous it shifts from participant to extractor. Humans have crossed this in the biosphere, who alter its naturally-selected conditions far faster than evolution can keep pace. If too high a percent of a country’s wealth is controlled by too small a percentage of the population, this wouldn’t just encourage exploitation, but make an economy fragile by reducing diversity. Small to-medium-scale network flux is the lifeblood of systems. Since less are investing, money would go into less industries and/or businesses, putting an economy’s eggs in too few baskets. Reduced diversity in ecosystems poses similar dangers.   

 

Products of network flux constantly multiply, creating uncontrollable consequences. When operating optimally (and with a bit of luck), dynamic patterns emerge that lead to all the novelties in Nature. As a general rule, human agency will never match a system’s evolutionary mechanisms, but acting otherwise inhibits them, which, in turn, leads to stagnant networks. Sadly, facts, logic, and evidence will never be as powerful as The Sham.

 

 

The Four Laws of Extralogical Complexity 

 

Everything in economies, industries, societies, psychologies, the biosphere, and, ultimately, life and Nature are directly and indirectly connected. They are Complex networks, which are dominated by the “systems” they give rise to. 

 

For maximum Complexity—self-organization, synergy emergence, novelties, etc.--various things must exist in dynamic proportion. First, a network’s parts/variables must be sufficiently interactive to have a system and network at all; otherwise, they dissolve into an aggregate of units. On the other hand, they can’t be so dependent and entangled that they solidify into a single unit. Too much dependence, you tend to get a loss of synergy by rigidifying interactions along with “amplification points” that makes the network susceptibility to run away effects. Too much independence, organization suffers, and it also hinders synergetic capacity due to the loss in connectivity that allows for nonlinear change. As a general rule, networks function best when Complexity and synergy are high. Since both tend to be high when independence and dependence are in dynamic proportion, most “dynamic proportionalities” come down to independence vs. dependence in one form or another.

 

Diversity keeps a system’s reliance on key components in check, curbing excessive dependency on food sources or investment strategies. At the same time, redundancy lends reinforcement if crucial components collapse. You need both fragility, and antifragility: Optimal system/network antifragility requires expendable components to keep network flux in a dynamic state, but naturally, you need the system stalwarts to ensure at least a few components withstand shocks. Constraints must flow in both directions; you must have hierarchy without tyranny. To maintain network flux and the antifragility of system stalwarts, smaller components must be heavily influenced by those higher in the food chain while providing enough pushback to prevent the alphas from stripping them of their autonomy. 

 

In sum, Complexity is maximum when independence-dependence, diversity-redundancy, top-down vs. bottom-up constraints, and fragility-antifragility exist in relative proportion. This is what Scott E. Page calls “The interesting in between” (see the Teaching Company's Great Course: Complexity). This the first law, the law of the interesting in between. 

 

In part one, I explained that in sufficiently connected networks, connectivity grows highly nonlinearly from one degree of separation to the next. Say the thinking cells/neurons of a given network have an average of five hundred direct connections (zero degrees). If a thinking cell directly connects with one more, it increases secondary connections by five hundred (one degree) and its tertiary connections by 25,000 (two degrees). This connectivity makes network interactions prone to multiply, not merely add, and the resultant domination of secondary effects gives rise to Complexity. 

 

In the supplemental article to part one, the law of synergy, I analogized a Complex network to a higher dimensional arrangement of chimes. Owing to myriad interactions, the arrangement wouldn’t be in one, two, or three dimensions, but perhaps thousands or millions. A few intentional collisions between chimes would lead to many more, and the system’s musical output would become less and less a reflection of surface inputs as initiating collisions increased. Stronger inputs, more chimes, more dimensional connections—all tend to lead to a growing disproportionality in secondary effects.   

 

ER calls this the law of Complex synergy, and systems become more Complex and synergetic at an ever-increasing rate as they grow. Sociopolitical structure, consequently, isn’t scalable; it becomes nonlinearly Complex as it grows. Although Complexity drives economic development, as society becomes more populus and industrialized, it usurps more and more personal and communal autonomy from those it’s supposed to serve. Complex synergy is what drives the self-organizing, adaptive powers of Nature, and attempts to control networks tend to hamper the synergy produced by secondary interactions. In societies, there’s a tradeoff: The industrial machine drives economies and technological progress but consumes more and more of everything else.  

 

In a way, synergy is the opposite of energy: It can emerge, but it is not conserved, making it easier to suppress than enhance.     

 

The least understood components are the most powerful, and very often, the better-known parts are among the least predictive (of the network), especially in the long-term. Fads come and go, and, as information cascades show, people underestimate their tendency to conform. Societal “beliefs” don’t reflect the behavior as much as thought, hardly surprising given how overrated a predictor of behavior and general perspectives personal beliefs tend to be. Steve Job’s notoriously ignored surveys about products people thought they wanted and changed the World—twice. Naturally, the inverse proportionality in understanding and predictability creates gross misconceptions about the long-term predictably of networks.

These powerful components are, as you’d imagine, intangible. Complex or network rhythms are the self-organizing agencies responsible for network trends and emergent patterns like market cycles, migration patterns, fads, etc. Coordinating Complex rhythms shape the behavior of other rhythms. The Sham and the portions of cognitive system that facilitate functional cognitive coherence are examples. They act like keystone species whose rhythmic behavior, in turn, guide the behavior of other rhythms. In the language of chaos theory, they are attractor-like. As said, feedback merges with other feedback; merged feedback leads to nascent oscillations; some oscillations grow and stabilize into patterns called Complex/network rhythms like market and businesses cycles, migrations, thought patterns, and related trends; and these are shaped by coordinating Complex rhythms. 

 

In the physical sense, infrastructure shapes and maintains a fundamental structural organization. Systemic Organization is what distinguishes infrastructure and foundation, which is more support of weight. In extralogical Complexity, coordinating Complex rhythms represent the system infrastructure. Maintaining and shaping a foundational structure is, likewise, what infrastructure is in the nonliteral sense. Tangible components like laws and regulations help organize by shaping network behavior as parts of substrate infrastructure. Subconscious beliefs called biosap comprise the bulk of the network’s substrate infrastructure. Emerging from cognition and the psychology itself, cognitive coordinating rhythms shape thought and belief rhythms to organize and package beliefs. Arising from society’s pool of beliefs and society itself, The Sham plays a similar role with the belief/ideological rhythms from society’s pool of beliefs to organize societal rhythms.  

   

Real-World data shows that most Complex networks are highly self-organizing and antifragile (robust and adaptable) and function best when allowed to exploit their antifragility, making them easier to harm than help (see Ubiquity by Marc Buchanan for more details on harm caused by invasive tampering with Complex systems). This is the law of antifragility: Networks function best when allowed to exploit their antifragility and synergy, which includes allowing fragility in smaller components to maintain an antifragile whole.  

 

Antifragility makes networks almost immortal—but not immune to illness. Following the path of least resistance, once networks figure out what works, they tend to develop a dependency, turning them into weak-links (the principle of overreliance). This and high connectivity make them suspectable to runaway effects. When first introduced, atmospheric oxygen, for instance, was apocalyptically lethal to the biosphere; now it can barely survive without it. Biospheres are sensitive to their naturally-selected conditions, and humans alter ecosystems far faster than evolution can keep pace, resulting in the mass extinction that’s been happening since the species arose (the rate of extinction has since risen by a factor of a thousand). Even economies can only adjust so quickly. Network flux works best in a dynamic but less than frenetic state. 

 

Preventing harm is not the same thing as aiding. You can prevent lung cancer and conserve cardiovascular health by not smoking three packs a day, but concocting an effective treatment for lung cancer once it’s been acquired is a wee bit harder. Similarly, it’s at least physically possible to prevent harm to the environment by drastically reducing pollution, deforestation, and (over time) the human population, etc., but reversing a global warming catastrophe or runaway extinction once it’s begun is essentially impossible. I’m not necessarily suggesting that humans should never tinker with Complex networks. Alteration of networks is usually what’s dangerous. Directly or indirectly curbing an investment bubble or overpopulation of a predator, for example, is preventing alteration, which can be practical (which should typically be done via discouragement or disincentivizing rather than force). In the end, however, the law of antifragility tells you that effective oversight centers on preventing harm, not delusions of control.    

 

As said, aggressive tampering usually decreases control by suppressing antifragility and unduly increasing dependencies, leading to stagnation and susceptibility to runaway effects. Capricious opinion-forming easily leads to wrong beliefs that are then reinforced, impairing the self-consistency and correctness of a belief network. Complex networks like psychologies and economies are recursively self-modeling. Resolve is just coherence in more emotionally driven beliefs. Large inconsistences in beliefs reduces credibility/self-credibility, resulting in doubt and impulsive responses that cause systemic “misbehavior.”

 

You gain a measure of control by keeping dependencies in check and allowing the system to exploit its antifragility. While evolution might be mostly environment-specific, when it thrives, general adaptability tends to improve. When governmental credibility is high or when personal credibility is high in a psychology, networks tend to behave (however, credibility past a certain point can cause widespread compliancy and other problems; and like independence-dependance, credibility and doubt are part of the interesting in-between, and ought to exist in dynamic proportion). The law of anticontrol states that a measure of control is gained by moderate and strategic management centered on avoiding harm and impaired by aggressive tampering.    

 

The Principals of Paradigm Shifts

 

Notwithstanding the many changes in a network, they can only change so much, so quickly. Changes greater than a certain amount requires paradigm shifts. Paradigm shifts tend to obey certain principles, but being they aren’t entirely absolute, I’ve chosen to label them as principles. 

 

 

(ER’s) The first principal of paradigm shifts: In a Complex network with any appreciable inertia, a paradigm shift only becomes viable if the predecessor paradigm FAILS. 

 

In other words, paradigm shifts are unlikely to occur just because a hypothetical successor is better; it must be preceded by the collapse of the predecessor (see Structures of Scientific Revolutions by Thomas Kuhn and Ted “the Unabomber” Kaczynski’s principals of history its and consequences—i.e., major changes are stable; holistic; rarely planned, but self-organized; not centrally about individual need--in his Modern Industrial Society and its Future). Why? Following the path of least resistance, the network will only reliably select for major components that are “just good enough not to fail.” In becoming established, the components will consume the bulk of the relevant inertia, making failure the only thing that can remove it. The inertia, in sum, rests in what’s just good enough, not “what’s best.” And what’s just good enough is based first on the emergent system’s needs, not those of the substrate, in accordance with the law of Complex synergy. 

 

(ER’s) The second principal of paradigm shifts: Rational human agency is not a viable ultimate cause for major reform: In Complex networks, true paradigm shifts are rarely organized in advance and are based chiefly on the needs of the SYSTEM.

 

Although the laws of Complexity are hardly as absolute as the laws of physics, most proposed sociopolitical “reforms” violate the principals of paradigm shifts. Kaczynski asserted in his principals of history that long-term historical trends are not planned or organized, but, rather, occur through the self-organization of a Complex network based systemic needs, not the populous’. While some argue that Kaczynski’s principals were overly absolute, by and large, major organized reform is a fantasy created by The Sham—in fact, it is The Sham (the delusion of the predominance of rational human agency). Convenient post hoc narratives and correlational fallacies, such as the teleological fallacy, often lead to proximate causes, catalysts, and triggers—or even just coincidences--being mistaken for ultimate causes. 

 

The idea that industrialized society would ever be ecologically responsible, which would go light-years beyond reducing pollution and deforestation, is in total violation of the principals of paradigm shifts and everything that’s known about how large groups make decisions. Even individually, it’s questionable. No one’s been put to the test, and pretentions of duty and sacrifice are almost a human universal. Like everything else in Nature, people tend to follow the path of least resistance and what they perceive as their own self-interest; and, regardless of whatever propaganda spat at the science museum, their perceptions aren’t very broad, logical, or scientific. No large-scale endeavor can persist unless it’s driven by tyranny or perceived self-interest. The carrot is more useful than the stick, and there’s nothing tastier than short to mid-term profits.   

 

 

In short, paradigm shifts can be catalyzed, reinforced, and, perhaps to some degree, shaped by other things; but ultimate causation almost always rests in failure and the all-consuming self-organization of the network.    

 

 

Emergence and self-organization are the heart of Nature, and it beats with the awesome powers of synergy and Complexity. If Nature weren’t good at producing novelty, it wouldn’t be what it is. You wouldn’t be. Successful though humankind has been in science and engineering, it can’t compete with Nature’s achievements. But my analysis doesn’t just show you what Nature can do; it tells you what it does. This can’t be altered by human beings, and pretending otherwise, as I’ve shown, does more harm than good. 

 

It's said that “In a Complex network, man influences almost everything but controls almost nothing.” I don’t want to completely dismiss the role that people play in Complex networks any more than I want to dismiss ER’s effect on a psychology, itself modeled as a Complex network. Better leaders make better communities; better management makes for a healthier psychology and life. But in the end, all people can do is influence Complex networks.