Complex Adaptive Systems Quotes

...repeated trauma in childhood forms and deforms the personality. The child trapped in an abusive environment is faced with formidable tasks of adaptation. She must find a way to preserve a sense of trust in people who are untrustworthy, safety in a situation that is unsafe, control in a situation that is terrifyingly unpredictable, power in a situation of helplessness. Unable to care for or protect herself, she must compensate for the failures of adult care and protection with the only means at her disposal, an immature system of psychological defenses.

To adapt to our complex world of weaponized information, maybe schools should teach data as we do languages.

In a systemic world, there is no such thing as a discrete or isolated event - impacts cascade and spill over.

Racism is a complex and interconnected system that adapts to challenges over time. Colorblind ideology was a very effective adaptation to the challenges of the Civil Rights Era. Colorblind ideology allows society to deny the reality of racism in the face of its persistence, while making it more difficult to challenge than when it was openly espoused.

Artificial intelligence is defined as a machine's ability to automatically learn, adapt, and solve complex problems with increasing precision and performance that benefit society.

Playing nice" comes naturally when our neuroception detects safety and promotes physiological states that support social behavior. However, pro-social behavior will not occur when our neuroception misreads the environmental cues and triggers physiological states that support defensive strategies. After all, "playing nice" is not appropriate or adaptive behavior in dangerous or life-threatening situations. In these situations, humans - like other mammals - react with more primitive neurobiological defense systems. To create relationships, humans must subdue these defensive reactions to engage, attach, and form lasting social bonds. Humans have adaptive neurobehavioral systems for both pro-social and defensive behaviors.

Human-level AI is defined as adaptable systems that can not only learn and do complex tasks but also behave in a social manner similar to that of civilized humans.

complex system: a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution.

The goal of science is to make the wonderful and complex understandable and simple—but not less wonderful. —Herb Simon, Sciences of the Artificial

Few human inventions are more complex and tightly coupled than the banking system; Charles

The five elements of the complex adaptive system are conformity enforcers, diversity generators, inner-judges, resource shifters, and intergroup tournaments.

All living things must grow or they will die. Adaptation to change is a characteristic of all living systems. Thus, all living things must grow, adapt, evolve, or die. Evolution is nature’s creative way of pushing living organisms to higher degrees of complexity. We adapt up, not compromise down.

Often, scholars distinguish between complex systems—systems in which the entities follow fixed rules—and complex adaptive systems—systems in which the entities adapt. If the entities adapt, then the system has a greater capacity to respond to changes in the environment.

Emergence is when micro-level complex systems that are far from equilibrium (thus allowing for the amplification of random events) self-organize (creative, self-generated, adaptability-seeking behavior) into new structures, with new properties that previously did not exist, to form a new level of organization on the macro level.

Actually, the entire ascent of life can be presented as an adaptive radiation in the time dimension. From the beginning of replicating molecules to the formation of membrane-bounded cells, the formation of chromosomes, the origin of nucleated eukaryotes, the formation of multicellular organisms, the rise of endothermy, and the evolution of a large and highly complex central nervous system, each of these steps permitted the utilization of a different set of environmental resources, that is, the occupation of a different adaptive zone.

Good modeling requires that we have just enough of the “right” transparencies in the map. Of course, the right transparencies depend on the needs of a particular user.

Models need to be judged by what they eliminate as much as by what they include—like stone carving, the art is in removing what you do not need.

Complexity, therefore, results in flexibility. Increasing complexity always increases capability and adaptability.

The similarity of architecture in organized, complex systems suggests that they all share universal requirements. They are designed to be “efficient, adaptive, evolvable, and robust.

Small societies are particularly vulnerable to disruption of key lifelines, such as trading relations, or to large perturbations like wars or natural disasters. Larger societies, with more diverse and extensive resources, can rush aid to disaster victims. But the complexity that brings resilience may also impede adaptation and change, producing social inertia that maintains collectively destructive behavior. Consequently, large societies have difficulty adapting to slow change and remain vulnerable to problems that eat away their foundation, such as soil erosion. In contrast, small systems are adaptable to shifting baselines but are acutely vulnerable to large perturbations. But unlike the first farmer-hunter-gatherers who could move around when their soil was used up, a global civilization cannot.

Current studies of networks (Newman, Barabasi, and Watts 2006) using notions of community and synchrony within subgroups help to make the niche concept more precise. However, it is noteworthy that few network studies concentrate on the formation of boundaries within a network. And there is even less study of mechanisms for the formation of hierarchies—mechanisms that would explain the pervasiveness of hierarchies in natural systems.

Tags [distinctive agent features observable by other agents] almost always define the network by delimiting the critical interactions, the major connections. Tags acquire this role because the adaptive processes that modify cas [complex adaptive systems] select for tags that mediate useful interactions and against tags that cause malfunctions. That is, agents with useful tags spread, while agents with malfunctioning tags cease to exist.

How is it that those systems in nature we call complex and adaptive—brains, insect colonies, the immune system, cells, the global economy, biological evolution—produce such complex and adaptive behavior from underlying, simple rules?

All of the likely or possible independent inventions of writing (in Sumer, Mexico, China, and Egypt), and all of the early adaptations of those invented systems (for example, those in Crete, Iran, Turkey, the Indus Valley, and the Maya area), involved socially stratified societies with complex and centralized political institutions, whose necessary relation to food production we shall explore in a later chapter. Early writing served the needs of those political institutions (such as record keeping and royal propaganda), and the users were full-time bureaucrats nourished by stored food surpluses grown by food-producing peasants. Writing was never developed or even adopted by hunter-gatherer societies, because they lacked both the institutional uses of early writing and the social and agricultural mechanisms for generating the food surpluses required to feed scribes.

If we increase r [in a logistic map] even more, we will eventually force the system into a period-8 limit cycle, then a period-16 cycle, and so on. The amount that we have to increase r to get another period doubling gets smaller and smaller for each new bifurcation. This cascade of period doublings is reminiscent of the race between Achilles and the tortoise, in that an infinite number of bifurcations (or time steps in the race) can be confined to a local region of finite size. At a very special critical value, the dynamical system will fall into what is essentially an infinite-period limit cycle. This is chaos.

began to realize how important emergent strategy, strategy for building complex patterns and systems of change through relatively small interactions, is to me—the potential scale of transformation that could come from movements intentionally practicing this adaptive, relational way of being, on our own and with others.

Because complex animals can evolve their behavior rapidly. Changes can occur very quickly. Human beings are transforming the planet, and nobody knows whether it’s a dangerous development or not. So these behavioral processes can happen faster than we usually think evolution occurs. In ten thousand years human beings have gone from hunting to farming to cities to cyberspace. Behavior is screaming forward, and it might be nonadaptive. Nobody knows. Although personally, I think cyberspace means the end of our species.” “Yes? Why is that?” “Because it means the end of innovation,” Malcolm said. “This idea that the whole world is wired together is mass death. Every biologist knows that small groups in isolation evolve fastest. You put a thousand birds on an ocean island and they’ll evolve very fast. You put ten thousand on a big continent, and their evolution slows down. Now, for our own species, evolution occurs mostly through our behavior. We innovate new behavior to adapt. And everybody on earth knows that innovation only occurs in small groups. Put three people on a committee and they may get something done. Ten people, and it gets harder. Thirty people, and nothing happens. Thirty million, it becomes impossible. That’s the effect of mass media—it keeps anything from happening. Mass media swamps diversity. It makes every place the same. Bangkok or Tokyo or London: there’s a McDonald’s on one corner, a Benetton on another, a Gap across the street. Regional differences vanish. All differences vanish. In a mass-media world, there’s less of everything except the top ten books, records, movies, ideas. People worry about losing species diversity in the rain forest. But what about intellectual diversity—our most necessary resource? That’s disappearing faster than trees. But we haven’t figured that out, so now we’re planning to put five billion people together in cyberspace. And it’ll freeze the entire species. Everything will stop dead in its tracks. Everyone will think the same thing at the same time. Global uniformity. Oh, that hurts. Are you done?” “Almost,” Harding said. “Hang on.” “And believe me, it’ll be fast. If you map complex systems on a fitness landscape, you find the behavior can move so fast that fitness can drop precipitously. It doesn’t require asteroids or diseases or anything else. It’s just behavior that suddenly emerges, and turns out to be fatal to the creatures that do it. My idea was that dinosaurs—being complex creatures—might have undergone some of these behavioral changes. And that led to their extinction.

Complex systems produce complicated results, and still there are identifiable patterns: Patriarchy is a system that delivers material benefits to men—unequally depending on men’s other attributes (such as race, class, sexual orientation, nationality, immigration status) and on men’s willingness to adapt to patriarchal values—but patriarchy constrains all women. The physical, psychological, and spiritual suffering endured by women varies widely, again depending on other attributes and sometimes just on the luck of the draw, but no woman escapes some level of that suffering. And at the core of that system is men’s control of women’s sexuality and reproduction: Without

You see the impact of humans on Earth’s environment every day. We are trashing the place: There is plastic along our highways, the smell of a landfill, the carbonic acid (formed when carbon dioxide is dissolved in water) bleaching of coral reefs, the desertification of enormous areas of China and Africa (readily seen in satellite images), and a huge patch of plastic garbage in the Pacific Ocean. All of these are direct evidence of our effect on our world. We are killing off species at the rate of about one per day. It is estimated that humans are driving species to extinction at least a thousand times faster than the otherwise natural rate. Many people naïvely (and some, perhaps, deceptively) argue that loss of species is not that important. After all, we can see in the fossil record that about 99 percent of all the different kinds of living things that have ever lived here are gone forever, and we’re doing just fine today. What’s the big deal if we, as part of the ecosystem, kill off a great many more species of living things? We’ll just kill what we don’t need or notice. The problem with that idea is that although we can, in a sense, know what will become or what became of an individual species, we cannot be sure of what will happen to that species’ native ecosystem. We cannot predict the behavior of the whole, complex, connected system. We cannot know what will go wrong or right. However, we can be absolutely certain that by reducing or destroying biodiversity, our world will be less able to adapt. Our farms will be less productive, our water less clean, and our landscape more barren. We will have fewer genetic resources to draw on for medicines, for industrial processes, for future crops. Biodiversity is a result of the process of evolution, and it is also a safety net that helps keep that process going. In order to pass our own genes into the future and enable our offspring to live long and prosper, we must reverse the current trend and preserve as much biodiversity as possible. If we don’t, we will sooner or later join the fossil record of extinction.

Even more complex and dangerous than the river itself were the fishes, mammals, and reptiles that inhabited it. Like the rain forest that surrounds and depends upon it, the Amazon river system is a prodigy of speciation and diversity, serving as home to more than three thousand species of freshwater fishes—more than any other river system on earth. Its waters are crowded with creatures of nearly every size, shape, and evolutionary adaptation, from tiny neon tetras to thousand-pound manatees to pink freshwater boto dolphins to stingrays to armor-plated catfishes to bullsharks. By comparison, the entire Missouri and Mississippi river system that drains much of North America has only about 375 fish

The diversity of 'cas'(complex adaptive systems) is a dynamic patter, often persistent and coherent like the standing wave we alluded to earlier. If you disturb the wave, say with a stick or paddle, the wave quickly repairs itself once the disturbance is removed. Similarly in 'cas', a pattern of interactions disturbed by the extinction of component agents often reasserts itself, though the new agents may differ in detail from the old. There is, however, a crucial difference between the standing wave pattern and 'cas' patterns: 'cas' patterns evolve. The diversity observed in 'cas' is the product of progressive adaptations. Each new adaptation opens the possibility for further interactions and new niches.

Helicobacter pylori (H. pylori). H. pylori is frequently accused of contributing to the development and progression of autoimmune disease (and is also one of the best-understood persistent infections). As mentioned in the previous section, H. pylori is a bacterium found in the upper gastrointestinal tract of approximately 50 percent of the population and is known to cause stomach ulcers in susceptible individuals. It also modulates the adaptive immune system through a very complex interaction. In fact, the interaction is so complex that acquiring H. pylori early in life prevents immune and autoimmune diseases. By contrast, acquiring H. pylori as an adult (which is more common in Western countries) increases the risk of immune dysfunction.

We cannot predict the behavior of the whole, complex, connected system. We cannot know what will go wrong or right. However, we can be absolutely certain that by reducing or destroying biodiversity, our world will be less able to adapt. Our farms will be less productive, our water less clean, and our landscape more barren. We will have fewer genetic resources to draw on for medicines, for industrial processes, for future crops. Biodiversity

[That] the driving force of the evolution of human intelligence was the coordination of multiple cognitive systems to pursue complex, shared goals [is called] the social brain hypothesis. It attributes the increase in intelligence to the increasing size and complexity of hominid social groups. Living in a group confers advantages, as we have seen with hunting, but it also demands certain cognitive abilities. It requires the ability to communicate in sophisticated ways, to understand and incorporate the perspectives of others, and to share common goals. The social brain hypothesis posits that the cognitive demands and adaptive advantages associated with living in a group created a snowball effect: As groups got larger and developed more complex joint behaviors, individuals developed new capabilities to support those behaviors. These new capabilities in turn allowed groups to get even larger and allowed group behavior to become even more complex.

In contrast to mainstream artificial intelligence, I see competition as much more essential than consistency," he says. Consistency is a chimera, because in a complicated world there is no guarantee that experience will be consistent. But for agents playing a game against their environment, competition is forever. "Besides," says Holland, "despite all the work in economics and biology, we still haven't extracted what's central in competition." There's a richness there that we've only just begun to fathom. Consider the magical fact that competition can produce a very strong incentive for cooperation, as certain players spontaneously forge alliances and symbiotic relationships with each other for mutual support. It happens at every level and in every kind of complex, adaptive system, from biology to economics to politics. "Competition and cooperation may seem antithetical," he says, "but at some very deep level, they are two sides of the same coin.

Selection is also important in non-biological contexts. In designing machines and computer programs, it has been found that a very efficient way to find the optimal design is to successively make small, random changes to the design, keeping versions that do the job well, and discarding others. This is increasingly being used to solve difficult design problems for complex systems. In this process, the engineer does not have a design in mind, but only the desired function. Adaptations

The emergence of self-organized ecoranges generates the potential for highly adaptable responses to environmental perturbations that might affect that ecorange. Within that system, all the plants are continually communicating with each other, sending chemical communications along the mycelial network to other plants in the community. (Plants also speak using auditory signals through a complex sound-based language that is far more ancient than the human though it exists in a much subtler sound spectrum than our own.)

As a network is swamped by chronic anxiety, it is marked by reactivity. Those within the system no longer act rationally, but rather, high emotion becomes the dominant form of interaction. The system’s focus is directed toward the most emotionally immature and reactive members. Those who are more mature and healthy begin to adapt their behavior to appease the most irrational and unhealthy. This creates a scenario where the most emotionally unhealthy and immature members in the system become de facto leaders, shaping the emotional landscape with the focus on their negative behavior and what they see as the negative behavior of others.

But now Holland was beginning to realize just how prescient Samuel's focus on games had really been. This game analogy seemed to be true of any adaptive system. In economics the payoff is in money, in politics the payoff is in votes, and on and on. At some level, all these adaptive systems are fundamentally the same. And that meant, in turn, that all of them are fundamentally like checkers or chess: the space of possibilities is vast beyond imagining. An agent can learn to play the game better-that's what adaptation is, after all. But it has just about as much chance of finding the optimum, stable equilibrium point of the game as you or I have of solving chess.

Joint-stock companies could be similarly flexible. “The absence of close control by the British crown in the early stages of colonization,” Elliott points out, left considerable latitude for the evolution of those forms of government that seemed most appropriate to the people actively involved in the process of overseas enterprise and settlement—the financial backers of the enterprise and the colonists themselves—as long as they operated within the framework of their royal charter. In contrast to Spain’s “new world” colonies—and to the territories that France, more recently, had claimed (but barely settled) along the banks of the St. Lawrence, the Great Lakes, and the Ohio and Mississippi rivers—British America “was a society whose political and administrative institutions were more likely to evolve from below than to be imposed from above.” 10 That made it a hodgepodge, but also a complex adaptive system. Such systems thrive, theorists tell us, from the need to respond frequently—but not too frequently—to the unforeseen. Controlled environments encourage complacency, making it hard to cope when controls break down, as they sooner or later must. Constant disruptions, however, prevent recuperation: nothing’s ever healthy. There’s a balance, then, between integrative and disintegrative processes in the natural world—an edge of chaos, so to speak—where adaptation, especially self-organization, tends to occur. 11 New political worlds work similarly.

Here was this elusive "Santa Fe approach": Instead of emphasizing decreasing returns, static equilibrium, and perfect rationality, as in the neoclassical view, the Santa Fe team would emphasize increasing returns, bounded rationality, and the dynamics of evolution and learning. Instead of basing their theory on assumptions that were mathematically convenient, they would try to make models that were psychologically realistic. Instead of viewing the economy as some kind of Newtonian machine, they would see it as something organic, adaptive, surprising, and alive. Instead of talking about the world as if it were a static thing buried deep in the frozen regime, as Chris Langton might have put it, they would learn how to think about the world as a dynamic, ever-changing system poised at the edge of chaos.

what if a better material for breathability comes along? You would have to junk the entire garment and get a new one to take advantage of it: costly. It takes more time, energy, and resources to maintain a unified system; that is, even though it may be more efficient, the trade-off is that it is more costly and not as robust. Because each layer can provide a wide range of diverse functions, the system has greater flexibility as a whole, giving it a great advantage when facing a changing environment. This type of layout is ideal in an evolutionary sense because the number of vulnerabilities in the system is limited, while the opportunities for diversification are abundant. As an environment changes over time, such systems can adapt more readily. Overall, a layered architecture is ideal for complex systems because it is easily fixable, less costly, more flexible, and evolvable. However, layered complex

Nothing in the period that followed was too good for the Rouge; it had the best blast furnaces, the best machine tools, the best metal labs, the best electrical systems, the most efficient efficiency experts. At its maturity in the mid-twenties, the Rouge dwarfed all other industrial complexes. It was a mile and a half long and three quarters of a mile wide. Its eleven hundred acres contained ninety-three buildings, twenty-three of them major. There were ninety-three miles of railroad track on it and twenty-seven miles of conveyor belts. Some seventy-five thousand men worked there, five thousand of them doing nothing but keeping it clean, using eighty-six tons of soap and wearing out five thousand mops each month. By the standards of the day the Rouge was, in fact, clean and quiet. Little was wasted. A British historian of the time, J. A. Spender, wrote of its systems: “If absolute completeness and perfect adaptation of means to end justify the word, they are in their own way works of art.” Dissatisfied with the supply and quality of the steel he was getting from the steel companies, Ford asked how much it would cost to build a steel plant within the Rouge. About $35 million, Sorensen told him. “What are you waiting for?” said Ford. Equally dissatisfied with both the availability and the quality of glass, he built a glass factory at the Rouge as well. The

When General Genius built the first mentar [Artificial Intelligence] mind in the last half of the twenty-first century, it based its design on the only proven conscious material then known, namely, our brains. Specifically, the complex structure of our synaptic network. Scientists substituted an electrochemical substrate for our slower, messier biological one. Our brains are an evolutionary hodgepodge of newer structures built on top of more ancient ones, a jury-rigged system that has gotten us this far, despite its inefficiency, but was crying out for a top-to-bottom overhaul. Or so the General genius engineers presumed. One of their chief goals was to make minds as portable as possible, to be easily transferred, stored, and active in multiple media: electronic, chemical, photonic, you name it. Thus there didn't seem to be a need for a mentar body, only for interchangeable containers. They designed the mentar mind to be as fungible as a bank transfer. And so they eliminated our most ancient brain structures for regulating metabolic functions, and they adapted our sensory/motor networks to the control of peripherals. As it turns out, intelligence is not limited to neural networks, Merrill. Indeed, half of human intelligence resides in our bodies outside our skulls. This was intelligence the mentars never inherited from us. ... The genius of the irrational... ... We gave them only rational functions -- the ability to think and feel, but no irrational functions... Have you ever been in a tight situation where you relied on your 'gut instinct'? This is the body's intelligence, not the mind's. Every living cell possesses it. The mentar substrate has no indomitable will to survive, but ours does. Likewise, mentars have no 'fire in the belly,' but we do. They don't experience pure avarice or greed or pride. They're not very curious, or playful, or proud. They lack a sense of wonder and spirit of adventure. They have little initiative. Granted, their cognition is miraculous, but their personalities are rather pedantic. But probably their chief shortcoming is the lack of intuition. Of all the irrational faculties, intuition in the most powerful. Some say intuition transcends space-time. Have you ever heard of a mentar having a lucky hunch? They can bring incredible amounts of cognitive and computational power to bear on a seemingly intractable problem, only to see a dumb human with a lucky hunch walk away with the prize every time. Then there's luck itself. Some people have it, most don't, and no mentar does. So this makes them want our bodies... Our bodies, ape bodies, dog bodies, jellyfish bodies. They've tried them all. Every cell knows some neat tricks or survival, but the problem with cellular knowledge is that it's not at all fungible; nor are our memories. We're pretty much trapped in our containers.

It's not that we're dumb. On the contrary, many millions of people have exerted great intelligence and creativity in building the modern world. It's more that we're being swept into unknown and dangerous waters by accelerating economic growth. On just one single day of the days I have spent writing this book, as much world trade was carried out as in the whole of 1949; as much scientific research was published as in the whole of 1960; as many telephone calls were made as in all of 1983; as many e-mails were sent as in 1990.11 Our natural, human, and industrial systems, which evolve slowly, are struggling to adapt. Laws and institutions that we might expect to regulate these flows have not been able to keep up. A good example is what is inaccurately described as mindless sprawl in our physical environment. We deplore the relentless spread of low-density suburbs over millions of acres of formerly virgin land. We worry about its environmental impact, about the obesity in people that it fosters, and about the other social problems that come in its wake. But nobody seems to have designed urban sprawl, it just happens-or so it appears. On closer inspection, however, urban sprawl is not mindless at all. There is nothing inevitable about its development. Sprawl is the result of zoning laws designed by legislators, low-density buildings designed by developers, marketing strategies designed by ad agencies, tax breaks designed by economists, credit lines designed by banks, geomatics designed by retailers, data-mining software designed by hamburger chains, and automobiles designed by car designers. The interactions between all these systems and human behavior are complicated and hard to understand-but the policies themselves are not the result of chance. "Out of control" is an ideology, not a fact.

Complex systems are more spontaneous, more disorderly, more alive than that. At the same time, however, their peculiar dynamism is also a far cry from the weirdly unpredictable gyrations known as chaos. In the past two decades, chaos theory has shaken science to its foundations with the realization that very simple dynamical rules can give rise to extraordinarily intricate behavior; witness the endlessly detailed beauty of fractals, or the foaming turbulence of a river. And yet chaos by itself doesn't explain the structure, the coherence, the self-organizing cohesiveness of complex systems. Instead, all these complex systems have somehow acquired the ability to bring order and chaos into a special kind of balance. This balance point—often called the edge of chaos—is were the components of a system never quite lock into place, and yet never quite dissolve into turbulence, either. The edge of chaos is where life has enough stability to sustain itself and enough creativity to deserve the name of life. The edge of chaos is where new ideas and innovative genotypes are forever nibbling away at the edges of the status quo, and where even the most entrenched old guard will eventually be overthrown. The edge of chaos is where centuries of slavery and segregation suddenly give way to the civil rights movement of the 1950s and 1960s; where seventy years of Soviet communism suddenly give way to political turmoil and ferment; where eons of evolutionary stability suddenly give way to wholesale species transformation. The edge of chaos is the constantly shifting battle zone between stagnation and anarchy, the one place where a complex system can be spontaneous, adaptive, and alive. Complexity, adaptation, upheavals at the edge of chaos—these common themes are so striking that a growing number of scientists are convinced that there is more here than just a series of nice analogies. The movement's nerve center is a think tank known as the Santa Fe Institute, which was founded in the mid-1980s and which was originally housed in a rented convent in the midst of

Similarly, the computers used to run the software on the ground for the mission were borrowed from a previous mission. These machines were so out of date that Bowman had to shop on eBay to find replacement parts to get the machines working. As systems have gone obsolete, JPL no longer uses the software, but Bowman told me that the people on her team continue to use software built by JPL in the 1990s, because they are familiar with it. She said, “Instead of upgrading to the next thing we decided that it was working just fine for us and we would stay on the platform.” They have developed so much over such a long period of time with the old software that they don’t want to switch to a newer system. They must adapt to using these outdated systems for the latest scientific work. Working within these constraints may seem limiting. However, building tools with specific constraints—from outdated technologies and low bitrate radio antennas—can enlighten us. For example, as scientists started to explore what they could learn from the wait times while communicating with deep space probes, they discovered that the time lag was extraordinarily useful information. Wait times, they realized, constitute an essential component for locating a probe in space, calculating its trajectory, and accurately locating a target like Pluto in space. There is no GPS for spacecraft (they aren’t on the globe, after all), so scientists had to find a way to locate the spacecraft in the vast expanse. Before 1960, the location of planets and objects in deep space was established through astronomical observation, placing an object like Pluto against a background of stars to determine its position.15 In 1961, an experiment at the Goldstone Deep Space Communications Complex in California used radar to more accurately define an “astronomical unit” and help measure distances in space much more accurately.16 NASA used this new data as part of creating the trajectories for missions in the following years. Using the data from radio signals across a wide range of missions over the decades, the Deep Space Network maintained an ongoing database that helped further refine the definition of an astronomical unit—a kind of longitudinal study of space distances that now allows missions like New Horizons to create accurate flight trajectories. The Deep Space Network continued to find inventive ways of using the time lag of radio waves to locate objects in space, ultimately finding that certain ways of waiting for a downlink signal from the spacecraft were less accurate than others. It turned to using the antennas from multiple locations, such as Goldstone in California and the antennas in Canberra, Australia, or Madrid, Spain, to time how long the signal took to hit these different locations on Earth. The time it takes to receive these signals from the spacecraft works as a way to locate the probes as they are journeying to their destination. Latency—or the different time lag of receiving radio signals on different locations of Earth—is the key way that deep space objects are located as they journey through space. This discovery was made possible during the wait times for communicating with these craft alongside the decades of data gathered from each space mission. Without the constraint of waiting, the notion of using time as a locating feature wouldn’t have been possible.

The Seventh Central Pay Commission was appointed in February 2014 by the Government of India (Ministry of Finance) under the Chairmanship of Justice Ashok Kumar Mathur. The Commission has been given 18 months to make its recommendations. The terms of reference of the Commission are as follows:  1. To examine, review, evolve and recommend changes that are desirable and feasible regarding the principles that should govern the emoluments structure including pay, allowances and other facilities/benefits, in cash or kind, having regard to rationalisation and simplification therein as well as the specialised needs of various departments, agencies and services, in respect of the following categories of employees:-  (i) Central Government employees—industrial and non-industrial; (ii) Personnel belonging to the All India Services; (iii) Personnel of the Union Territories; (iv) Officers and employees of the Indian Audit and Accounts Department; (v) Members of the regulatory bodies (excluding the RBI) set up under the Acts of Parliament; and (vi) Officers and employees of the Supreme Court.   2. To examine, review, evolve and recommend changes that are desirable and feasible regarding the principles that should govern the emoluments structure, concessions and facilities/benefits, in cash or kind, as well as the retirement benefits of the personnel belonging to the Defence Forces, having regard to the historical and traditional parties, with due emphasis on the aspects unique to these personnel.   3. To work out the framework for an emoluments structure linked with the need to attract the most suitable talent to government service, promote efficiency, accountability and responsibility in the work culture, and foster excellence in the public governance system to respond to the complex challenges of modern administration and the rapid political, social, economic and technological changes, with due regard to expectations of stakeholders, and to recommend appropriate training and capacity building through a competency based framework.   4. To examine the existing schemes of payment of bonus, keeping in view, inter-alia, its bearing upon performance and productivity and make recommendations on the general principles, financial parameters and conditions for an appropriate incentive scheme to reward excellence in productivity, performance and integrity.   5. To review the variety of existing allowances presently available to employees in addition to pay and suggest their rationalisation and simplification with a view to ensuring that the pay structure is so designed as to take these into account.   6. To examine the principles which should govern the structure of pension and other retirement benefits, including revision of pension in the case of employees who have retired prior to the date of effect of these recommendations, keeping in view that retirement benefits of all Central Government employees appointed on and after 01.01.2004 are covered by the New Pension Scheme (NPS).   7. To make recommendations on the above, keeping in view:  (i) the economic conditions in the country and the need for fiscal prudence; (ii) the need to ensure that adequate resources are available for developmental expenditures and welfare measures; (iii) the likely impact of the recommendations on the finances of the state governments, which usually adopt the recommendations with some modifications; (iv) the prevailing emolument structure and retirement benefits available to employees of Central Public Sector Undertakings; and (v) the best global practices and their adaptability and relevance in Indian conditions.   8. To recommend the date of effect of its recommendations on all the above.

Organs are supple and capable of adapting to different situations and domains. Finally, they are open to development, and this is precisely what “stable systems” (Case, 1992a, p. 5) are not. But organs are much more complex than such systems, and it is much more difficult to understand them.

This is a miracle of coevolution—the bacteria that coexist with us in our bodies enable us to exist. Microbiologist Michael Wilson notes that “each exposed surface of a human being is colonized by microbes exquisitely adapted to that particular environment.”21 Yet the dynamics of these microbial populations, and how they interact with our bodies, are still largely unknown. A 2008 comparative genomics analysis of lactic acid bacteria acknowledges that research is “just now beginning to scratch the surface of the complex relationship between humans and their microbiota.”22 Bacteria are such effective coevolutionary partners because they are highly adaptable and mutable. “Bacteria continually monitor their external and internal environments and compute functional outputs based on information provided by their sensory apparatus,” explains bacterial geneticist James Shapiro, who reports “multiple widespread bacterial systems for mobilizing and engineering DNA molecules.”23 In contrast with our eukaryotic cells, with fixed genetic material, prokaryotic bacteria have free-floating genes, which they frequently exchange. For this reason, some microbiologists consider it inappropriate to view bacteria as distinct species. “There are no species in prokaryotes,” state Sorin Sonea and Léo G. Mathieu.24 “Bacteria are much more of a continuum,” explains Lynn Margulis. “They just pick up genes, they throw away genes, and they are very flexible about that.”25 Mathieu and Sonea describe a bacterial “genetic free market,” in which “each bacterium can be compared to a two-way broadcasting station, using genes as information molecules.” Genes “are carried by a bacterium only when needed . . . as a human may carry sophisticated tools.”26

that is simple, neat and wrong. —H. L. Mencken Things should be made as simple as possible—but no simpler. —Albert Einstein

For every complex problem, there is a solution

Therefore, democratic referenda are the best mechanisms for maximizing social welfare in a world consisting of only a single town. Oddly, when we allow additional towns into the system, democratic referenda no longer lead to the highest social welfare. In fact, the effectiveness of the different choice mechanisms is completely reversed, and democratic referenda become the worst possible institution rather than the best. (See figure 2.4.) Why does this happen?

If social worlds are truly complex, then we might need to recast our various attempts at understanding, predicting, and manipulating their behavior. In some cases, this recasting may require a radical revision of the various approaches that we traditionally employ to meet these ends.

Modern organizations have other characteristics as well. Samuel Huntington lists four criteria for measuring the degree of development of the institutions that make up the state: adaptability-rigidity, complexitysimplicity, autonomy-subordination, and coherence-disunity.16 That is, the more adaptable, complex, autonomous, and coherent an institution is, the more developed it will be. An adaptable organization can evaluate a changing external environment and modify its own internal procedures in response. Adaptable institutions are the ones that survive, since environments always change. The English system of Common Law, in which law is constantly being reinterpreted and extended by judges in response to new circumstances, is one prototype of an adaptable institution. Developed institutions are more complex because they are subject to a greater division of labor and specialization. In a chiefdom or early state, the ruler may be simultaneously military general, chief priest, tax collector, and supreme court justice. In a highly developed state, all of these functions are performed by separate organizations with specific missions and a high degree of technical capacity to undertake them. During the Han Dynasty, the Chinese bureaucracy ramified into countless specialized agencies and departments at national, prefectural, and local levels. While much less complex than a modern government, it nonetheless represented an enormous shift away from earlier governments that were run as simple extensions of the imperial household. The two final measures of institutionalization,

He intends only his own gain, and he is in this, as in many other cases, led by an invisible hand to promote an end which was no part of his intention. —Adam Smith, Wealth of Nations Any sufficiently advanced technology is indistinguishable from magic.

When dealing with a complex adaptive system, sticking to the plan is a recipe for failure. 

Networks are evolving systems, constantly mutating and adapting. As physicists Mark Newman, Albert-Lazslo Barabasi, and Duncan J. Watts explain, "Many networks are the product of dynamical processes that add or remove vertices or edges....The ties people make affect the form of the network, and the form of the network affects the ties people make. Social network structure therefore evolves in a historically dependent manner, in which the role of the participants and the patterns of behavior they follow cannot be ignored." In some cases, the changes do not take weeks or months, but minutes or hours. And it is not only the network that adapts; whatever is being exchanged within the system also fluctuates over time (e.g., information, energy, water, a virus).

These ideas returned in various guises in complexity theory, emphasizing the general theme of adaptation. Thus he introduced into strategic theory the concept of open complex adaptive systems struggling to survive in a contested, dynamic, non-linear world pregnant with uncertainty, constantly attempting to improve and update its schemata and repertoire of actions and its position in the ecology of the organization. Such an eclectic holistic approach became an argument in itself: he considered it a prerequisite for sound strategic thinking. He wanted to inculcate his audience not so much with a doctrine as with an understanding of the dynamics of war and strategy and a style of thinking about that dynamic that differed from the deterministic mindset that prevailed in the strategic discourse of the 1960s and 1970s. Applying his argument in practice – constantly showing the dynamic of move and countermove, stripping bare, analyzing, the essence of certain strategies, and then recombining them with new insights and hypotheses – allowed him to expand and go ‘deeper’ into the essence of strategy and war than previous strategists.

A complex system benefits from not following the same practices over and over again. By enduring a little bit of stress and continuously adapting to variability in the environment, the system learns to become fitter and healthier.

The term “organization” thus suggests a certain bareness, a lean, no-nonsense system of consciously co-ordinated activities.[1] It refers to an expendable tool, a rational instrument engineered to do a job. An “institution,” on the other hand, is more nearly a natural product of social needs and pressures—a responsive, adaptive organism. This distinction is a matter of analysis, not of direct description. It does not {6} mean that any given enterprise must be either one or the other. While an extreme case may closely approach either an “ideal” organization or an “ideal” institution, most living associations resist so easy a classification. They are complex mixtures of both designed and responsive behavior.

A view often implicit in form approaches holds that only generative algorithmic models, specifying unique outputs from given inputs, are scientific, so that the underdetermined nature of blending, as we analyze it, brands our theory as unscientific. This objection is simply wrong. Theories of probability, subatomic particles, chaos, complex adaptive systems, evolution, immunology, and many others could not get off the ground as sciences if they were required to offer models in which the specified inputs determined unique outputs

A few key terms that frame the dynamics of complexity theory will be a starting point for further study and further reflection on how complexity theory can increase our awareness of organizational dynamics and the nested systems of change that constitute life and change.

Robustness is achieved by strengthening parts of the system (the pyramid); resilience is the result of linking elements that allow them to reconfigure or adapt in response to change or damage (the coral reef).

But to Holland, the concept of prediction and models actually ran far deeper than conscious thought-or for that matter, far deeper than the existence of a brain. "All complex, adaptive systems- economies, minds, organisms-build models that allow them to anticipate the world," he declares. Yes, even bacteria. As it turns out, says Holland, many bacteria havve special enzyme systems that cause them to swim toward stronger concentrations of glucose. Implicitly, those enzyme model a crucial aspect of the bacterium's world: that chemicals diffuse outward from their source, growing less and less concentrated with distance. And the enzymes simultaneously encode an implicit prediction: If you swim toward higher concentrations, then you're likely to find something nutritious. "It's not a conscious model or anything of that sort," says Holland. "But it gives that organism an advantage over one that doesn't follow the gradient.

So no one in the CIA could imagine Mubarak ever being out of power, right? You know, complex, adaptive systems go through state changes, and they do it in very complex and unpredictable ways where one day they’re one way and the next day there’s been a dramatic shift. And, you know, the way that you undermine a complex, adaptive system is you begin to undermine the inputs that sustain it. So the inputs that sustain complex adaptive systems are energy, money, goodwill, and you know, I think the health care system is doing a very good job of eroding all of those things.

In Stacey’s view what is common to both the radical implications of complex adaptive systems theory and the sociology of Norbert Elias is that they provide a coherent explanation of how global patterning arises out of local interaction without separating them out onto different ‘levels’. There is no need to think in terms of an organisation as a self-regulating metaphysical entity separate from the actions of individual members of staff or managers. An organisation arises purely out of the activities, intentions, idealisations and the attempts to make meaning of the many employees who join together with the intention of achieving something collectively. In what Elias would refer to as a figuration, or web of people engaged in fluctuating and asymmetric power relations cooperating in their undertaking and competing over meaning and ideology, the organisation becomes.

Inquiry into the neuroscience of aesthetics can give us insight into, and lead to new questions about, emotion, the adaptability of neural structures in different individuals, and the relations between complex neural systems ranging from those underpinning imagery to those supporting memory and identity.

Perhaps more worrisome, while government policies should be intended to serve the many for the long term, they are being gamed by interested parties to ensure that they serve the few in the short term, with damaging impact over the long term. The Persona Project respondents could feel this. To them, they were outsiders and others were playing the game to their own advantage, and to the respondents’ disadvantage. These outcomes are systemic, and without a fundamental shift in how we manage the economy, they will get only more out of alignment with our hopes and assumptions. I believe that this shift needs to start with abandoning the perfectible-machine model of the economy. We should instead understand the economy in more natural terms, as a complex adaptive system—one that is too complex to be perfectible, one that continuously adapts in ways that will almost certainly frustrate any attempts to engineer it for perfection. In addition, rather than striving singularly for ever more efficiency, we need to strive for balance between efficiency and a second feature: resilience.

Humankind is now moving toward a time, possibly as soon as within a few generations, when we will no longer be able to expect nature to adjust rapidly enough to ensure our own survival. Rather, civilization on Earth will either have to adapt to the natural environment with ever-accelerating speed, or generate artificial environmental conditions needed for our ecological existence. From two magnificent yet local systems—society and machines—will likely emerge a symbiotically functioning technoculture, the epitome (as far as we know) of complexity writ large in nature[.]

The most important training involved the latest American breakthrough in communications technology: a handheld, portable, two-way radio transceiver that made ground-to-air communications possible for the first time. A predecessor of the mobile telephone, the equipment had been designed at the RCA electronics laboratories in New York before being refined and developed for the OSS by De Witt R. Goddard and Lieutenant Commander Stephen H. Simpson. The device would eventually become known as a “walkie-talkie,” but at the time of its invention this pioneering gizmo went by a more cumbersome and quaint title: the “Joan-Eleanor system.” “Joan” was the name for the handheld transmitter carried by the agent in the field, six inches long and weighing three pounds, with a collapsible antenna; “Eleanor” referred to the larger airborne transceiver carried on an aircraft flying overhead at a prearranged time. Goddard’s wife was named Eleanor, and Joan, a major in the Women’s Army Corps, was Simpson’s girlfriend. The Joan-Eleanor (J-E) system operated at frequencies above 250 MHz, far higher than the Germans could monitor. This prototype VHF (very high frequency) radio enabled the users to communicate for up to twenty minutes in plain speech, cutting out the need for Morse code, encryption, and the sort of complex radio training Ursula had undergone. The words of the spy on the ground were picked up and taped on a wire recorder by an operator housed in a special oxygen-fed compartment in the fuselage of an adapted high-speed de Havilland Mosquito bomber flying at over twenty-five thousand feet, outside the range of German anti-aircraft artillery. An intelligence officer aboard the circling aircraft could communicate directly with the agent below. As a system of communication from behind enemy lines, the J-E was unprecedented, undetectable by the enemy, easy to use, and so secret that it would not be declassified until 1976.

the Greek philosophical tradition thought of the world as, first and foremost, an overarching order: at once harmonious, just, beautiful, and good. The word “cosmos” connotes all of this. For the Stoics, for example, to whom the Latin poet Ovid defers in his Metamorphoses—when reinterpreting after his fashion the great myths dealing with the origins of the world—the universe resembles a magnificent living organism. If we want to get an idea of this, we might think of what doctors or physiologists or biologists discover when they dissect a rabbit or a mouse. What do they find? Firstly, that each organ is marvelously adapted to its function: What is better constructed than an eye for seeing, than lungs for oxygenating the muscles, than a heart for pumping blood via an irrigation system? These organs are a thousand times more ingenious, more harmonious and complex, than almost all of the machines devised by man. Moreover, our biologist discovers something else: that the ensemble of these organs, which considered individually are sufficiently astonishing, together form a quite perfect and “logical” whole—what the Stoics indeed named the logos, to refer to the coherent ordering of the world as well as to verbal discourse—and a whole that is infinitely superior again to any human invention. From this point of view, we must humbly acknowledge that the creation of even the humblest being—a tiny ant, a mouse, or a frog—is still far beyond the reach of our most sophisticated scientific laboratories.

regardless of the details of the agent’s interior apparatus, a cas agent can always be defined in terms of a set of signal-processing rules called classifier rules. Each rule accepts certain signals as inputs (specified by the condition part of the rule) and then processes the signals to produce outgoing signals (the action part of the rule). Formally, both signals and boundaries can be defined using strings of “letters” drawn from one basic “alphabet.” This limited alphabet has a counterpart in cellular biology, where both the structure of protein signals and important parts of “gateways” in semi- permeable membranes are defined using an alphabet of twenty amino acids.

since in practical terms, each run of a complex adaptive system is unique, educational and child-rearing techniques that might work for a Hannah may not work for a Gabriel. And techniques that might have worked for Hannah yesterday may not today.

The current political system was never designed to deal with the high complexity and frenetic pace of a knowledge-based economy. Parties and elections may come and go. New methods for fund-raising and campaigning are emerging, but in the United States, where the knowledge economy is most advanced and the Internet allows new political constituencies to form almost instantly, significant change in political structure comes so slowly as to be almost imperceptible. One hardly needs to defend the economic and social importance of political stability. But immobility is another matter. The U.S. political system, two centuries old, changed fundamentally after the Civil War of 1861–1865 and again in the 1930s after the Great Depression, when it adapted itself more fully to the industrial era. Since then the government has certainly grown. But as far as basic, institutional reform is involved, the U.S. political structure will continue crawling along at three miles per hour, with frequent rest stops at the side of the road, until a constitutional crisis strikes.

As an example, this means it is much easier for an individual who craves visual simplicity (Crickets and Ladybugs) to go against their nature and learn to hang their coat on a hook than for someone who craves visual abundance (Bees and Butterflies) to put it away in a closet. It is also easier for a perfectionist (Crickets and Bees) who craves a detailed organizing system to relax their expectations and opt for a more macro approach than for someone who needs organizational simplicity (Butterflies and Ladybugs) to adapt to a complex approach.

Many man-made systems, including ICT systems, have positive feedback loops that cause certain local events to propagate and create extreme global behaviors. The extreme behaviors, especially unplanned downtime, become more common than stakeholders can accept. These outliers are modeled by probability distributions with thick tails. Unfortunately, classical methods for risk analysis based on predictions of future events tend to underestimate or ignore extreme global behaviors in complex adaptive ICT systems, even though these events may very well dominate the overall risk to stakeholders.

It all gets back to how running progression happens through a series of aerobic, neuromuscular, and musculoskeletal adaptations that build on each other for years and decades. Aerobically, the body improves oxygen processing capacity by increasing the number of capillaries to carry blood to working muscles, decreasing the amount of oxygen those muscles need in the first place, and turning fuel into energy more efficiently. Essentially, the gas mileage goes up while the engine gets bigger, turning a Kia into a Tesla. On the neuromuscular front, the brain gets better at transmitting signals to the body to perform complex movement patterns, decreasing ground contact time, making faster cadences feel easier, and improving how the nervous system operates. Just like you learn to play the piano or dance, you learn to run efficiently.

If heterogeneity is a key feature of complex systems, then traditional social science tools—with their emphases on average behavior being representative of the whole—may be incomplete or even misleading.

In the organization of the future, effective leadership will be about directing and fine-tuning the innovation machine, a complex system that converts signals to ideas and then opportunities to realized innovation, in a fast, adaptive way.

Systems that produce complexity consist of diverse rule-following entities whose behaviors are interdependent. Those entities interact over a contact structure or network. In addition, the entities often adapt.

The Joan-Eleanor (J-E) system operated at frequencies above 250 MHz, far higher than the Germans could monitor. This prototype VHF (very high frequency) radio enabled the users to communicate for up to twenty minutes in plain speech, cutting out the need for Morse code, encryption, and the sort of complex radio training Ursula had undergone. The words of the spy on the ground were picked up and taped on a wire recorder by an operator housed in a special oxygen-fed compartment in the fuselage of an adapted high-speed de Havilland Mosquito bomber flying at over twenty-five thousand feet, outside the range of German anti-aircraft artillery. An intelligence officer aboard the circling aircraft could communicate directly with the agent below. As a system of communication from behind enemy lines, the J-E was unprecedented, undetectable by the enemy, easy to use, and so secret that it would not be declassified until 1976.

The leap from maps to fluid flow seemed so great that even those most responsible sometimes felt it was like a dream. How nature could tie such complexity to such simplicity was far from obvious. "You have to regard it as a kind of miracle, not like the usual connection between theory and experiment," Jerry Gollub said. Within a few years, the miracle was being repeated again and again in a vast bestiary of laboratory systems: bigger fluid cells with water and mercury, electronic oscillators, lasers, even chemical reactions. Theorists adapted Feigenbaum's techiniques and found other mathematical routes to chaos, cousins of period-doubling: such patterns as intermittency and quasiperiodicity. These, too, proved universal in theory and experiment.

Later Turing proved that Turing machines could compute exactly the same functions as lambda calculus, which proved that all three models of computation are equivalent. This is a truly remarkable result, considering how different the three models of computation are. In Church's 1941 paper he made a statement that is now known as the Church-Turing thesis: Any function that can be called computable can be computed by lambda calculus, a Turing machine, or a general recursive function. Recall the point that was made about functions describing relationships between numbers and models of computation describing functions. Well, the Church-Turing thesis is yet another level more fundamental than a model of computation. As a statement about models of computation, it is not subject to proof in the usual sense; thus, it is impossible to prove that the thesis is correct. Once could disprove it by coming up with a model of computation over discrete elements that could calculate things that one of the other models could not; however, this has not happened. The fact that every posed model of computation has always been exactly equivalent to (or weaker than) one of the others lends strong support to the Church-Turing thesis.

Now, take all of your computer's memory and arrange it as one long line of zeros and ones: 0,1,1,1,0,0,0,1,1,0,1....Take this very long number and put a zero and a decimal point in front of it. We've just translated one huge number into a rational number between 0 and 1. By placing this single point at exactly the right spot on the number line, we can store an unlimited amount of information. Ah, if only it were so simple. In the real world, we simply don't have the precision required to put this method of storing memory into practice. We never will, either, but it's an interesting mental exercise to see that it can be done in theory in an idealized world. The point of this whole mental exercise is that in many ways an irrational number has as much "information" as an infinite number of natural numbers.

First, adaptability and resilience require diversity, variation, and fluctuations. Allen (2001) describes the need for this redundancy (that is, having more options or pathways that are necessary to function like a machine) as the law of excess diversity. He is saying that unless there are more pathways or options (called degrees of freedom by mathematicians) than are required to operate efficiently, there is no resilience to changing circumstances. However much diversity seems requisite (Ashby, 1956) for a system to function at a given time, more than this will be required to cope with what is likely to happen in the future.

Allen (1997: 17) says: ‘The capacity to adapt and respond to external and internal variation, although requiring some “instability” can be the origin of the system’s resilience. This is an example of the complexity of some of these issues in which adaptability may allow stasis in a broader sense, and rigidity may lead to collapse.’ He is saying that it is being not entirely stable, being able to wobble about, that allows the system to be resilient and almost stable!

Any discrete piece of information can be represented by a set of numbers. Systems that compute can represent powerful mappings from one set of numbers to another. Moreover, any program on any computer is equivalent to a number mapping. These mappings can be thought of as statements about the properties of numbers; hence, there is a close connection between computer programs and mathematical proofs. But there are more possible mappings than possible programs; thus, there are some things that simply cannot be computed. The actual process of computing can be defined in terms of a very small number of primitive operations, with recursion and/or iteration comprising the most fundamental pieces of a computing device. Computing devices can also make statements about other computing devices. This leads to a fundamental paradox that ultimately exposes the limitations not just of machine logic, but all of nature as well.

rinciples, or "rules" in complexity theory terminology, affect how tools and practices are implemented. Practices are how principles are acted out. Grand principles that generate no action are mere vapor. Conversely, specific practices in the absence of guiding principles are often inappropriately used. Although the use of agile practices may vary from team to team, the principles are constant. Principles are the simple rules of complex human adaptive systems.

The majority of genes in the [MHC (Mean Histocompatibility Complex)] class I region are not involved in the immune system; neither do the class I genes form as a compact cluster as the class II genes. Whereas genes encoding class II molecules are only present in the class II region of the HLA [(Human Leukocyte Antigen) complex], genes encoding class I molecules and related class I-like molecules are found on several different chromosomes. A further difference is that HLA class II molecules are dedicated components of adaptive immunity that serve only to present antigen to T cells, whereas HLA class I and class I-like molecules encompass a broader range of functions, including uptake of IgG in the gut, regulation of iron metabolism, and regulation of NK [(Natural Killer)]-cell function in the innate immune response. Together, these genetic and functional differences show that MCH class I is the older form of MHC molecule and that MHC class II evolved more recently from MHC class I. Consistent with this proposition, MHC class II is not always essential for a vertebrate immune system, unlike class I. The Atlantic cod manages very well with only MHC class I genes.

Conventional economics is the dominant intellectual rationalization of today’s world order. As we’ve overextended the growth phase of our global adaptive cycle, this rationalization has become relentlessly more complex and rigid and progressively less tenable. Breakdown will, all at once, discredit this rationalization and create intellectual space for new ideas to flourish. But this space will be brutally competitive. We can boost the chances that humane alternatives will thrive by working them out in detail and disseminating them as widely as possible beforehand.89 Advance planning means we need to develop a wide range of scenarios and experiment with technologies, organizations, and ideas. We’ll do better at these tasks, and we’ll also do better in the confusing aftermath of breakdown, if we use a decentralized approach to solving our problems, because traditional centralized and top-down approaches aren’t nimble enough, and they stifle creativity. Scientists have found that complex systems that are highly adaptive—like markets and even the immune system of mammals—tend to share certain characteristics. First of all, the individual elements that make up the systems—such as companies in a market economy or T-cells and macrophages in an immune system—are extraordinarily diverse. Second, the power to make decisions and solve problems isn’t centralized in one place or thing; instead, it’s distributed across the system’s elements. The elements are then linked in a loose network that allows them to exchange information about what works and what doesn’t. Often in a market economy, for example, several companies will be working at the same time to solve different parts of a shared problem, and important information about solutions will flow between them. Third and finally, highly adaptive systems are unstable enough to create unexpected innovations but orderly enough to learn from their failures and successes. Systems with these three characteristics stimulate constant experimentation, and they generate a variety of problem-solving strategies.90 We

Looking back at the organization of the sciences, we find that at teach level of understanding, traditional scientists study two types of phenomena: agents(molecules, cells, ducks, and species) and interactions of agents (chemical reactions, immune system responses, duck mating, and evolution). Studying agents in isolation is a fruitful way of discovering insights into the form and function of an agent, but doing so has some known limitations. Specifically, reductionism fails when we try to use it in a reverse direction. As we shall see throughout this book, having a complete and perfect understanding of how an agent behaves in no way guarantees that you will be able to predict how this single event will behave for all time or in the context of other agents.

We have, then, three different ways of looking at how things work. We can take a purely reductionist approach and attempt to understand things through dissection. We also can take a wider view and attempt to understand whole collections at once by observing how many agents, say the neurons in a brain, form a global pattern, such as human intelligence. Or we can take an intermediate view and focus attention on the interactions of agents. Through this middle path, the interactions of agents can be seen to form the glue that binds one level of understanding to the next level.

Moreover, multiplicity, iteration, and adaptation are universal concepts in that they are apparently important attributes for agents at all levels-from chemical reactants to biological ecosystems.

The goal of this book is to highlight the computational beauty found in nature's programs.

Complexity theory compares the nature of organizations to that of other living organisms, through a behaviorist and a Darwinian lens, referring to such naturally occurring organizations as “complex adaptive systems.

Open some space, and Spirit will certainly show up. Allow the magic of self-organization to work for you, and the complex adaptive system that we are will find its own power.

Almost all official statistics and policy documents on wages, income, gross domestic product (GDP), crime, unemployment rates, innovation rates, cost of living indices, morbidity and mortality rates, and poverty rates are compiled by governmental agencies and international bodies worldwide in terms of both total aggregate and per capita metrics. Furthermore, well-known composite indices of urban performance and the quality of life, such as those assembled by the World Economic Forum and magazines like Fortune, Forbes, and The Economist, primarily rely on naive linear combinations of such measures.6 Because we have quantitative scaling curves for many of these urban characteristics and a theoretical framework for their underlying dynamics we can do much better in devising a scientific basis for assessing performance and ranking cities. The ubiquitous use of per capita indicators for ranking and comparing cities is particularly egregious because it implicitly assumes that the baseline, or null hypothesis, for any urban characteristic is that it scales linearly with population size. In other words, it presumes that an idealized city is just the linear sum of the activities of all of its citizens, thereby ignoring its most essential feature and the very point of its existence, namely, that it is a collective emergent agglomeration resulting from nonlinear social and organizational interactions. Cities are quintessentially complex adaptive systems and, as such, are significantly more than just the simple linear sum of their individual components and constituents, whether buildings, roads, people, or money. This is expressed by the superlinear scaling laws whose exponents are 1.15 rather than 1.00. This approximately 15 percent increase in all socioeconomic activity with every doubling of the population size happens almost independently of administrators, politicians, planners, history, geographical location, and culture.

So long as module improvement respects the protocols by which the module connects to other modules, module improvement can proceed independently of those other modules. An extreme case of this is when the protocols are between different levels of the modular hierarchy and when there is richness on both sides of the protocol. When the upper side of the protocol is rich, the knowledge base on the lower side of the protocol is often referred to as a 'platform' on which knowledge modules above it can be based. In science, Newton's laws were a platform on which both celestial and terrestrial mechanics could be based. In technology, the personal computer software operating system is a platform on which a rich set of software application can be based. Moreover, when the lower side of the protocol is also rich, the shape of the knowledge network becomes hourglass-like. In the case of technological knowledge, the waist of the hourglass is a distinguished layer or protocol, with technologies underneath implementing the protocol and technologies above building on the protocol - with both sides 'screened' from each other by the protocol itself. As a result, the number of applications explodes independent of implementation details; similarly, the number of implementations explodes independent of application details. The number of software applications built on the Windows operating system is enormous; the number of hardware and software implementations of the Windows operating system is also enormous. In other words, imagine two complex adaptive systems, one organized modularly and one not. At one moment, both might be able to exploit their environments equally and thus be equally 'adapted' to their environment. But they will evolve at vastly different rates, with the one organized modularly quickly outstripping the one not so organized. Modularity appears to be an evolved property in biology, one that is mimicked in the organization of human knowledge.

Why are surprise and consolidation both important to knowledge evolution? Because exploration and exploitation are both important as we create new, and make use of existing, knowledge to interact with the world around us. Surprise emphasizes exploration, the creation of new paradigms; consolidation emphasizes exploitation, the use and extension of existing paradigms. While both are important, the balance between exploration and exploitation, surprise versus consolidation, is not governed by a hard-and-fast rule. In an approximate way, the balance depends on the kind of world in which the evolving knowledge system is embedded and the speed with which evolution for survival must occur. The more complex and changing the world, the more reason to emphasize and incur the cost of exploration; the simpler and more static the world, the more reason to emphasize exploitation and avoid the cost of exploration. In biological evolution, when organism variants are generated, those variants are tested in and by their world. Those that survive go on to reproduce, inheriting the original variation but also adding yet new variations. As formalized in Fisher's fundamental theorem of natural selection, the greater the variance in properties across organisms within each generation, the faster the rate at which the organismal population evolves, becoming fitter generation to generation. Variation, however, is costly, as most variants are less fit and die before reproducing, so the degree of variance is itself an optimizable and evolvable trait. The more complex and changing the world, the more reason to incur the cost of variance; the simpler and more static the world, the less reason to incur the cost of variance. The optimal rate of evolution or 'evolvability' depends on the kind of world in which the organismal population is embedded. In knowledge evolution, analogously, potential paradigms are generated, and those paradigm variants are tested by being played out in the real world. The measure of variance here is the degree to which the paradigm differs from or contradicts conventional wisdom, hence the degree to which one anticipates surprise. Thus, the paradigm generation process can be skewed either toward anticipated surprise or anticipated consolidation. Skewing toward surprise, however, is costly, as most potential paradigms that disagree with conventional wisdom are wrong and will have low utility. Thus, the optimal degree of variance depends on the kind of world in which the cognitive entity is embedded. At one extreme, if the world is complex or changing rapidly, then the optimal variance might weigh anticipated surprise more heavily. Indeed, at this extreme, it might be optimal to explore new paradigms simply for their novelty and potential for surprise. At the other extreme, if the world is simple or changing slowly, then the optimal variance might weigh anticipated consolidation more heavily. Why not make use of conventional wisdom rather than make a risky attempt to overturn it? Thus, there is a balance between paradigm creation and extension, but the precise balance is situational. Human societies and organizations might adopt the balance appropriate for world to which they had to adapt during the long-term course of human evolution. An engineered or augmented human cognition might adopt a balance more appropriate to the current world, and a purely artificial cognition might adopt whatever balance is appropriate to the world into which humans have embedded it. Most importantly, a human society with sufficient self-understanding might adopt a balance that is optimal for its environment and them might implement that balance in its public polocy that determines relative investment in the two - relative investment in research versus development.