Chapter 8: Complexity, Adaption, and Order: Visualizing the Invisible Hand

Yet this government never of itself furthered any enterprise, but by the alacrity with which it got out of its way. — Henry David Thoreau (1817–62)

Do not think of what you see but see what it took to produce what you see.  — Benoit Mandelbrot (1924–2010)

Life is not only stranger than we imagine; life is stranger than we can imagine. — John Haldane (1954–)

When we envision two people interacting, we can easily picture an orderly scene. But as we add more people to that image, the orderliness becomes more difficult to envision. We can easily sense in our duo scene that each person is acting orderly, because we likely see ourselves as one of them, knowing that cooperation is a more efficacious course of action in our pursuit of well-being. Not only would we not sense a need for a central planner to direct and enforce our interactions, we would also consider the idea absurd and, if imposed on us, downright disorderly.

Irrespective of the size of the group, whether a nation of millions or a planet of billions, each person is an individual actor, genetically guided to act based on the same local, self-serving, simple rules that are no different from those in our duo scene. “Tit for tat,” “the Golden Rule,” and “live and let live” are all normal, adaptive, neighborhood rules of human conduct, the essence of which are cooperating/competing/ostracizing algorithms.

Robert Axelrod conducted two computer tournaments to identify the winning rule in a game theory setting. The objective was to gain a deeper understanding of how to perform well in such a setting. The winning rule in both tournaments was “tit for tat,” in which a player cooperates on the first move and then does whatever the other player did on the previous move. The results show that there is value in being somewhat forgiving while at the same time not being the first to defect — as well as the importance of being provocable.1 In evolutionary jargon, “tit for tat” drove the other rules into extinction.

The natural orderliness of unordered interactions has been observed for centuries. One of the earliest records comes from Chuang-tzu (369–286 BC) who noted, “Good order results spontaneously when things are let alone.” In the eighteenth century, Adam Smith observed that individuals acting in their own self-interest lead to an orderly and prosperous society as if led by an “invisible hand.” A century later, Charles Darwin (1809–82) observed that organisms of myriad structures neatly fit into their surrounding environment as if selected by nature.

The new field of chaos theory, which has gained the more descriptive names of “systems theory,” “complexity theory,” and “theory of self-organizing systems,” gives us a deeper understanding of how living and nonliving matter naturally form orderly systems and structures of enormous complexity. This new way of looking at and understanding the world comes from the perspective of a system, rather than only from an atomistic or reductionist perspective. Even when we know the properties of the individual parts of or participants in the system, we cannot accurately predict the outcome of their interactions because it will not equal the sum of their respective properties. The characteristic of such a system is that the whole is greater than the sum of its parts because the system has properties that are not possessed by any of its parts.

The crux of this exciting and revolutionary science is that a social order of unimaginable complexity emerges from simple but profound local rules without any top-down directives.

Social scientist, Scott Page provides us with a brilliant distillation of complexity theory: “An actor in a complex system controls almost nothing but influences almost everything. Attempts to intervene may be akin to poking a tiger with a stick.”2

The idea of spontaneous order that results from randomly interacting organic and inorganic parts without a designer or director is counterintuitive. This new science is just that; we can’t easily envision it, and equally frustrating is the impossibility of predicting long-term outcomes while knowing the initial conditions, the properties of the parts, and understanding the process. We are inclined to believe that order requires a designer, but design does not need an interacting designer — it is an inherent part of the universe.3

When we imagine millions of people interacting, we are overwhelmed by the enormity and complexity of that scene and are compelled to conclude that top-down intervention is necessary — that someone needs to be in charge to rule and orchestrate all of us — otherwise, all hell will break loose. Secondly, we intuitively sense that the more complex a system is, the more complex the rules must be to effectively bring about and maintain order. Thirdly, we naturally assume that the greater the number of participants in a system, the greater the potential for disorder and chaos, thus requiring a greater degree of top-down orchestration. Finally, we assume that the more diverse the participants are in the system, the lower the probability of a successful and orderly system will be.

Remarkably, this new science dispels all four of these notions by demonstrating how orderly systems naturally emerge and evolve from randomly interacting parts.

• First, the order and complexity we see in all of nature’s systems emerge from bottom-up, local rules without being guided by a master or a master plan. Feedback following an act will guide actors to either repeat or modify future acts. Top-down enforcement of rules that interfere with the emergence of order that stems from self-serving, volitional adaption to local circumstances simply squanders energy to offset the disorderly intrusions. Additionally, imposed political rules and regulations hamper the natural ability of people to quickly adapt to changing conditions in their attempt to optimize utility.

• Second, complexity emerges from simple, local, neighborhood rules; generally, the simpler the rule, the more successful and orderly the outcome.4 Tit for tat is an example of such a rule that most of us naturally employ in our acquisition of resources and pursuit of mating opportunities.

• Third, as discussed earlier, the bonding of people of different cultures is best accomplished through trade. With ever more participants, there is a greater potential for innovative individuals to bring exceptional ideas, products, and services to the marketplace, thereby attracting and bonding an even larger array of diverse traders. The benefits gained by each trading participant will engender an interdependency that will make conflict counter to their own self-interest. As such, state tariffs and import restrictions are as disorderly as they are uneconomical.

• Finally, without diversity, markets and societies are worthless; the synergistic benefit of markets is realized only when people of different skills and preferences interact. According to Scott Page: Progress depends as much on our collective differences as it does on our individual IQ scores. Diverse groups of problem solvers outperformed the groups of the best individuals at solving complex problems. The reason: the diverse groups got stuck less often than the smart individuals, who tended to think similarly.5 Diversity also tends to diminish social disorder because individuals have different thresholds of reacting to an event. For example, people may rush out of a building if they see others doing so, but only when the number of people they see exceeds their individual threshold. As such, the mass exit may never even begin if just one person rushes out. Bees are an excellent example of the benefits of such diversity: they maintain the temperature of their hive at 86°F and can sense whether it is too hot or too cold. However, each bee does so at a different temperature. Some bees, sensing the hive as too hot at a lower temperature than others do, will leave, flap their wings outside the hive to cool it off, and then return. Other bees, with a higher temperature threshold, will stay put. If bees were not diverse in their temperature sensing, they would all leave and rejoin the hive at the same time, and the temperature would fluctuate widely.

This new science addresses the nonlinear systems of nature, where the output is not directly proportional to the input. This view is a major departure from the reductionist or atomistic approach to science that is the mainstay of scientific literature and studies. The scientific method, dating back to the seventeenth century, in essence involves observing a phenomenon, forming a causal hypothesis, extrapolating that hypothesis, and continually testing it to either validate or invalidate it. This method assumes a deterministic world where phenomena are potentially predictable. Based on this school of science, all complex systems in nature can be understood through the nature of their parts because the whole is simply the sum of its parts. Accordingly, the ability to predict a system’s future state should improve commensurate with the increased understanding of the nature of its parts in its current state. The reductionist view of nature also presumes that a small change in a current state will likewise result in a small change in its future state.

No matter how deeply scientists delved into the workings of a system’s parts, they were continually confronted with frustrating discrepancies and variations in the outcome. Unexpected discrepancies were attributed to mathematical errors, instrument failure or inadequacies, or simply so-called noise. However, despite improved instruments and refined mathematics, these discrepancies continued to exist. In short, the deeper the reductive search, the more reductive scientists must become.

With discrepancies at every level of resolution, a few scientists eventually concluded that maybe what they observed were not discrepancies but rather inherent features of nature that could not be reduced to their parts. This new, non-reductive science was and remains a bit too unorthodox for many scientists. The late physicist Michel Baranger, for example, initially thought this new science was an affront to everything he and most scientists understood. However, he eventually succumbed to the realization that nature is not a reductive, schematic system. In The End of Certainty, Nobel Laureate Ilya Prigogine (1917–2003) contends that determinism is no longer a viable scientific belief: “The more we know about our universe, the more difficult it becomes to believe in determinism.”6 This view is a major departure from the approaches of Isaac Newton and Albert Einstein and their theories, which are expressed as linear deterministic equations. According to Prigogine, determinism loses its explanatory power in the face of irreversibility and instability. He notes numerous examples, among which are evolution and the emergence of life.

In a nonlinear system, we can predict events accurately more or less only one generation at a time, each with a high probability of accuracy, but the accuracy level diminishes over time because the impact of the slightest perturbation or error magnifies exponentially. The time horizon for predicting future events varies depending on the system. In some systems, e.g., electrical, this horizon may be milliseconds, while in others, such as weather, it may be days or even millions of years, as with the solar system.7

No matter how sensitive the instruments, how meticulous the observations, or how precise the mathematics, the exponential growth of an unavoidable error and of chance will overwhelm the ability to accurately predict beyond a given period. By either lowering our standards or improving our initial measurement, however, we can always increase the predictable time period. The problem with predictability in a nonlinear system is that if you want to double, triple, or quadruple the predictable time period and maintain the same level of accuracy, you need to work 10, 100, or 1,000 times harder, respectively.8 In other words, if you want to obtain the same level of predictable accuracy for a ten-day period as a one-day period, you need to increase the preciseness of your initial measurements one billion times.

The “butterfly effect,” a term coined by meteorologist Edward Lorenz (1917–2008), metaphorically describes the amplifying effect of a minor wind disturbance such as the flapping of a butterfly’s wings causing a major hurricane at a future time in a far-off place.9 As a pioneer of chaos theory, Lorenz demonstrates why weather predictions will never be accurate for more than a couple of weeks out, irrespective of the increased sensitivity of our instruments and the amount of data collected. In these systems, Lorenz sees order masquerading as randomness. This rapid exponential amplification of a minor event is the trademark of chaos.

Many married couples have likely played a game in which they recall the least significant happenstance that led to their meeting — for instance, “if it hadn’t rained that day,” “if I hadn’t missed the bus,” “if we hadn’t bumped into each other in the hallway,” etc. Or consider the sequencing of so-called butterfly occurrences that led to your parents’ matchup and the resulting matchup of the two gametes that became you. As Steven Strogatz points out, while Lorenz is credited with the term “butterfly effect,” the realization that little events can have major consequences is likely an ancient observation.

As in all natural complex systems, there are no organizers, orchestrators, or planners to guide the process because there are no blueprints or plans to follow. Even if some parts of a system are disrupted or removed, the remaining parts will reconfigure and reestablish the functions of the system without any central authority controlling them. For example, each ant colony has a given ratio of worker, warrior, and forager ants, depending on the species. Remove or destroy a portion of that colony, and within hours, the ratio of workers, warriors, and foragers will reestablish itself without a central authority. Somehow, these relationships give rise to changes in the physiological development of the ants: warrior ants become much bigger than forager ants, which are bigger than worker ants.10

This self-organizing property of the ant colony, like that of other social animals, gives the intuitive appearance that the animals have a plan, or at least some individual animal has a plan. However, that is not the case. There is no plan or design to know. The future state of a system emerges from the interaction of its parts and participants using simple, local, if/then rules without any participants knowing what will eventually emerge from the sum of their actions. The internet is a testament to the unimaginable self-organizing complexity of a system that in just twenty-five years has grown to include and connect half the people of the world.

Knowing the chemical properties of each part will not provide insight into the resulting outcome of their association. Take, for example, the elements sodium and chlorine — both are toxic, but together they organize to form common table salt, which has none of the properties of its parts and cannot be predicted by knowing the properties of each part. Likewise, DNA, whether of a tree, an insect, or a human, contains four bases (adenine, cytosine, guanine, and thymine), with the sequencing making the difference. The amazing structure of an embryo that emerges from two interacting cells provides insight into nature’s inherent bottom-up, self-organizing system.

Yaneer Bar-yam writes:

It is generally believed that the design of plant or animal physiology is contained within the nuclear DNA of the cells. DNA is often called the blueprint for the biological organism. However, it is clear that DNA does not function like an architect’s blueprint because the information does not represent the structure of the physiology in a direct way — there is no homunculus there. DNA specifies the interaction between a cell and its environment, including cells in its vicinity, as well as the internal functioning of the cell.11

Stephen Wolfram, author of A New Kind of Science, suggests there is strong evidence that the level of complexity of individual parts of organisms has not changed much in at least several hundred million years.12 In other words, the building blocks used for structuring a biological organism have remained basically the same, with only their arrangement and quantity producing the differences between one organism and another. For instance, the neurons of a fly, chimpanzee, or human look the same, with only the quantity making the difference in each animal’s cognitive ability.

According to Wolfram: “Incredibly simple rules that produce incredibly complicated behavior is a very robust and very general phenomenon of nature. It has always been a mystery how nature could manage apparently so effortlessly to produce so much that seems to us so complex; it’s as though nature has some secret that allows it to make things so much more complex than we humans can normally build.”13 He suggests that nature is a system of active cells, each sampling a large array of possible rules. What we then see is how those rules play out. The cells that hit upon viable rules are the only ones that can produce organs and organisms able to survive in the given environment in which we find them. The harsher the environment, the fewer the rules that are viable, and thus fewer structural designs can potentially emerge. We can determine whether a rule is viable only by letting it play out. In other words, we cannot determine the viability of a rule a priori.

Symbiosis at both the cellular and organism levels is similar: cells cooperate with neighboring cells, as do organisms with neighboring organisms, in a mutual self-serving social network. Symbiotic behavior of both cells and organisms emerges without any superior cell or organism ordering such behavior. Despite the “selfishness” of genes discussed earlier, the optimal way for a gene to replicate is to join other replicators in building cells, organs, and organisms that serve each replicator. In the human genome, 1,195 genes cooperate to produce the heart, 2,164 genes team up to make a white blood cell, and 3,195 are responsible for the brain, but no gene or cell is in charge of the process.14 Each of the ten thousand pacemaker cells in the human heart beats independently when isolated in a Petri dish, but when joined with other pacemaker cells, all will beat in unison after only a few days — without any one cell orchestrating the beat.

Such self-synchronization is common in nature, including that found in inanimate structures.15 For example, two or more similar pendulums on the same shelf, each swinging independently and out of sync, will slowly and automatically synchronize their swings in unison. When people in an audience are asked to clap in unison, they will do so spontaneously within just a few seconds without a single person leading the pace. A school of fish or flock of birds will move abruptly in a synchronized, choreographic pattern, yet no one fish or bird is directing that movement. Instead, each is acting individually based on feedback from its nearest neighbor, thus displaying as a whole a cooperative, rhythmic flow of beauty without a conductor.

Cosmides and Tooby, both noted evolutionary psychologists, describe why top-down institutional planning cannot replace bottom-up, individual decision-making in bringing about social welfare:

Significantly, the human mind was intensely selected to evolve mechanisms to evaluate its own welfare, and is so equipped by natural selection to compute and represent its own array of preferences in exquisite and often inarticulable detail. The array of n-dimensional rankings that inhabits our motivational systems is too rich to be communicated to others or represented by them, which is one reason displacing value guided decision making to remote  institutions systematically damages social welfare. Under a system of private exchange, this richness need not be communicated or understood by anyone else — its power is harnessed effectively by a simple choice rule built into the human mind: pick the alternative with the highest payoff.16

Every day, we see people moving about, doing an enormous assortment of complicated tasks with seemingly uninterrupted orderliness; only rarely do we see disorder. Attributing such order to the political state mistakenly assigns causality where there is only concurrence. The state only exists at the expense of the working, cooperative members of society, while disrupting and prolonging their natural proclivity to orderliness.

We cannot expect order to emerge from the state’s issuance of laws and rules that are too complicated to understand and too numerous to track. As complexity theory clearly shows, the application of simple, local rules produces orderly systems. With this new science, we can better understand why top-down, state-enforced plans to benefit society have failed and why any other plan — no matter how brilliant — will fail as well.

In Complex Adaptive Systems, John Miller and Scott Page suggest applying “keep it simple, stupid” (KISS) to the formulation of rules to bring about order. Scientific modeling seeks to find the simplest rule by removing all the unneeded parts. Modeling is like stone carving: the art is in removing what you don’t need.17   

In The Social Order of the Underworld, David Skarbek illustrates how convicts develop their own rules of conduct that become far more effective in maintaining order than top-down prison rules. Prison guards rely on such self-imposed arrangements to govern affairs and adjudicate disputes between them.18 If hardened criminals were isolated on a remote island without rules or guards, contrary to expectations, rules of conduct would emerge to govern their behavior. They would enforce contracts and recognize property rights. The ability of prisoners to defy prison authorities and live by their own rules of conduct has led to the adage, “The cons run the joint.” In “running the joint,” inmates construct a society unto itself and establish their own moral order.19 Such a bottom-up emerging order belies Hobbes’s grim view of man’s state of nature.

I am always awestruck when I watch the hundreds of couples walking about in a shopping center and wonder how in the world all those people were able to find each other. There’s no State Matchmaker Commission that regulates their activity to ensure adequate propagation; it happens spontaneously in a system where no one is obligated to satisfy the innate mating drive of another. Simple tit-for-tat local rules get the job done with little effort (arguably!). As Steven Pinker observes, people shop for the most desirable person who will accept them, and that is why most marriages pair a bride and a groom of roughly equal desirability. The tens marry tens, the nines marry nines, and so on. That is exactly what should happen in a marketplace, where you want the best price you can get (the other person) for the goods you are offering (you).20  

Political intrusions into the volitional interaction of individuals minding their own affairs are no different from any other intrusions. Individuals adapt and adjust their actions accordingly to survive and prosper using the same cooperating/competing/ostracizing local rules that facilitated the survival of their ancestors. The greater the intrusion, the greater the effort individuals will make to search for and discover avenues and actions that diminish the impact.

The notion that forced compliance with rules imposed by political rulers supersedes one’s volitional compliance with local rules will likely have fewer advocates as the new science of chaos, complexity, and self-organizing systems of nature works its way into general academic curricula. It is more likely that the political state will unravel faster than the academic community will assimilate into its curricula the reasons why such unraveling was inevitable.

The idea that a ruler can override the laws of nature is all that has ever existed because the actual ability to do so is illusory. The laws of nature are no more subject to the dictates of a king, senator, congressman, or bureaucrat today than they have ever been. Nevertheless, those trapped in the political box continue to harbor the mythical belief that political rulers can somehow defy the laws of nature by forcing people to be orderly and prosperous. Such idolatry is bewildering in a world where the historical track record of political intrusion is filled with the devastation of human populations in the hundreds of millions.

The massive edifice of the state is a clear indication of its inability to mastermind and engineer the interactions of individual members of society. The average annual issuance of more than seventy-five thousand pages of new proposed and final federal regulations from 2003 to 2016,21 as well as new regulations issued by regional states, attests to the fact that their individual subjects are far more creative and imaginative than are those trying to rule them.

In his course “Great Scientific Ideas That Changed the World,” Professor Steven Goldman states, “This complex multifaceted idea of systems theory, complexity/chaos theory, and self-organization theory is one of the greatest scientific ideas of all time and is revolutionizing what we mean by scientific knowledge.”22
Evolution of living matter did not come about by a plan or with a discernible purpose; the interaction of a half-dozen common elements led to replicating matter called life. After some three to four billion years, here we are with brains capable of thinking about the entire process. I’m in awe (an understatement to be sure!) to think that my 185 millionth great-grandfather and great-grandmother were fish, but there they were, swimming around someplace on this planet with nary a thought of having me as their 185 millionth great-grandson. Natural evolution is a miraculous process that Richard Dawkins graphically captures in his book The Magic of Reality23 and which Matt Ridley explores in The Evolution of Everything.24

Leonard Read’s masterpiece I, Pencil: My Family Tree depicts the beautiful essence of the invisible hand from which a miraculous pencil emerges. From thousands of individuals, independent of the others, each freely pursuing his own well-being, emerges a pencil — which no single person knows how to produce — to be used by a writer who doesn’t know or give a thought to how it came to be.

The emergence of a pencil rests upon the same natural, bottom-up process as does the emergence of roads, eyes, education, trees, cities, justice, and rules. From the interaction of genes, cells, and individuals, unimaginable miracles emerge. Good economics is simply getting out of the way of miracle makers. As Leslie Orgel observed, “Evolution is smarter than you are.” In time, economists will come to realize that a planned economy — no matter how skillfully designed — cannot result in the outcome for which it is designed. Everything naturally evolves.

As the political state’s edifice unravels, humans will continue to interact and adapt, using simple local rules they heuristically discover as their guide for surviving and propagating. This new science of chaos and complexity is giving us invaluable insight into that process, while leaving us with the wonderment of an unpredictable outcome.

• 1Robert Axelrod, “More Effective Choice in the Prisoner’s Dilemma,” Journal of Conflict Resolution 24, no. 3 (1980): 379–403.
• 2Scott E. Page, Understanding Complexity (The Teaching Company), Lecture 12.
• 3Richard Dawkins provides an exhaustive explanation of how a natural feedback system can create biological complexity in his masterpiece The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe without Design (New York: W.W. Norton, 1986).
• 4Robert Sapolsky, “Human Behavior Biology,” Stanford University, 150/250, Spring 2010, “Chaos and Reductionism” (Lecture 21) https://www.youtube.com/watch?v=_njf8jwEGRo; and “Emergence and Complexity” (Lecture 22) https://www.youtube.com/watch?v=o_ZuWbX-CyE.
• 5Scott E. Page, The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies (Princeton, NJ: Princeton University Press, 2008), p. 26.
• 6Ilya Prigogine, The End of Certainty (New York: Free Press, 1997).
• 7Steven Strogatz, Chaos as Disorder: The Butterfly Effect (The Teaching Company), Lecture 6.
• 8Steven Strogatz, Sync: How Order Emerges from Chaos in the Universe, Nature, and Daily Life (Hachette Books, 2004), p. 190.
• 9James Glieck, Chaos, Making of a New Science (Penguin Books, 2008), ch. 1.
• 10Steven Goldman, Great Scientific Ideas That Changed the World (The Teaching Company), Lecture 34.
• 11Yaneer Bar-yam, Dynamics of Complex Systems (Studies in Nonlinearity) (Westview Press, 1997), p. 622.
• 12Stephen Wolfram, A New Kind of Science (Champaign, IL: Wolfram Media, 2002), p. 389.
• 13Stephen Wolfram, “Computing a Theory of All Knowledge,” lecture, TED2010, February 2010, http://www.ted.com/talks/stephen_wolfram_computing_a_theory_of_everything.
• 14Jurgen Appelo, Management 3.0: Leading Agile Developers, Developing Agile Leaders (Addison–Wesley Professional, 2011), p. 262.
• 15Strogatz, Sync; Steven Strogatz, “The Science of Sync,” lecture, TED 2004, February 2004, http://www.ted.com/talks/steven_strogatz_on_sync. Filmed, February 2004.
• 16Leda Cosmides and John Tooby, “Evolutionary Psychology, Moral Heuristics, and the Law,” in Heuristics and the Law, edited by Gerd Gigerenzer and Christoph Engel (Cambridge, MA: MIT Press, 2006).
• 17John H. Miller and Scott E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton, NJ: Princeton University Press, 2007), p. 246.
• 18David Skarbek, The Social Order of the Underworld, How Prison Gangs Govern the American Penal System (Oxford University Press, 2014). Skarbek discussed his book during the IFREE/ESI Lecture Series at Chapman University on September 12, 2014, available at https://vimeo.com/107054811. See also Michael P. Marks, The Prison as Metaphor: Re-imagining International Relations (New York: Peter Lang International Academic Publishers, 2004), ch. 5.
• 19Charles Stastny and Gabrielle Tyrnauer, “Applied: Who Rules the Joint?” American Anthropologist 85, no. 3 (1983): 716–17.
• 20Steven Pinker, “Crazy Love,” Time, January 17, 2008, http://content.time.com/time/magazine/article/0,9171,1704692,00.html.
• 21Clyde Wayne Crews Jr., Ten Thousand Commandments, An Annual Snapshot of the Federal Regulatory State, 2017 ed. Competitive Enterprise Institute, https://cei.org/10KC/Chapter-2.
• 22Steven Goldman, Great Ideas That Changed the World (The Teaching Company) Lecture 34, “System, Chaos and Self Organization.”
• 23Richard Dawkins, The Magic of Reality: How We Know What’s Really True (Free Press, 2012).
• 24Matt Ridley, The Evolution of Everything: How New Ideas Emerge (New York: Harper Perennial, 2016).