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Waldrop, M. Mitchell, Complexity (XXI)

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Matt Domsalla

SAASS 601/7

Complexity Precis

In Complexity, M. Mitchell Waldrop, an editor at Nature magazine, explores the emergence of the science of complexity along with the founding of the Santa Fe Institute. The science of complexity studies complex, self-organizing, adaptive systems that achieve a unique balance between order and chaos at a unique point called the edge of chaos. At the edge of chaos, the system components never quite lock into place, yet also never dissolve into turbulence. Waldrop postulates 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.” The Santa Fe Institute (SFI) was founded to study complexity. SFI brings together a broad range of scientists – Nobel laureate physicists Murray Gell-Mann and Philip Anderson along with Nobel laureate economist Kenneth Arrow – who believe that complexity provides a theoretical framework that can help illuminate nature and human behavior. SFI scientists “believe that they are forging the first rigorous alternative to the kind of linear, reductionist thinking that has dominated science since the time of Newton.”

The Irish Idea of a Hero (Brian Arthur)

· “Self-organization depends upon self-reinforcement: a tendency for small effects to become magnified when conditions are right….Positive feedback seemed to be the sine qua non of change, of surprise, life itself.” (34)

· “Neoclassical [economic] theory would have us believe that a free market will always winnow out the best and most efficient technologies…But…what are we to make of the standard QWERTY keyboard…Beta versus VHS.” (35)

· “That’s why you get patterns in any system: a rich mixture of positive and negative feedbacks can’t help producing patterns.” (36)

· “Tiny accidents of history…get magnified by the positive feedback into major differences in the outcome.” (36)

· “Increasing returns, lock-in, unpredictability, tiny events that have immense historical consequences.” (37)

· “The point was that you have to look at the world as it is, not as some elegant theory says it ought to be.” (38)

· “One of the main reasons the United States has had such a big problem with “competitiveness” is that government policy-makers and business executives alike were very slow to recognize the winner-take-all nature of high-technology markets.” (44)

· “Increasing returns cut to the heart of that myth [American individual freedom]. If small chance events can lock you in to any of several possible outcomes, the the outcome that’s actually selected may not be the best. And that means that maximum individual freedom – and the free market – might not produce the best of all possible worlds.” (48)

The Revolt of the Old Turks (George Cowan)

· “Everything is connected, and often with incredible sensitivity. Tiny perturbations won’t always remain tiny…even some very simple systems could produce astonishingly rich patterns of behavior. All that we required was a little bit of nonlinearity.” (66)

· “Whenever you look at very complicated systems in physics or biology…you generally find that the basic components and the basic laws are quite simple; the complexity arises because you have a great many of these simple components interacting simultaneously. The complexity is actually in the organization – the myriad possible ways that the components of the system can interact.” (86)

Secrets of the Old One

· “Somehow, that single egg cell manages to divide and differentiate into never cells and muscle cells and liver cells – hundreds of different kinds. And it does so with the most astonishing precision. The strange thing isn’t that birth defects happen, as tragic as they are; the strange thing is that most babies are born perfect and whole.” (106)

· “Technology isn’t really like a commodity at all. It is much more like an evolving ecosystem…technologies for a highly interconnected web – or…a network. Furthermore, these technological webs are highly dynamic and unstable…technological webs can undergo bursts of evolutionary creativity and massive extinction events, just like biological ecosystems…once a new technology starts opening up new niches for other goods and services, the people who fill those niches have every incentive to help that technology and prosper. Moreover, this process is a major driving force behind the phenomenon of lock-in: the more niches that spring up dependent on a given technology, the harder it is to change that technology.” (119)

· “If innovations result from new combinations of old technologies, then the number of possible innovations would go up very rapidly as more and more technologies became available.” (126)

“You Guys Really Believe That?”

· “Perfectly rational agents do have the virtue of being perfectly predictable.” (141)

· “In nonlinear systems…chaos theory tells you that the slightest uncertainly in your knowledge of the initial conditions will often grow inexorably. After a while, your predictions are nonsense.” (142)

Master of the Game

· “Complex adaptive systems – each of these systems is a network of many “agents” acting in parallel…the control of a complex adaptive system tends to be highly dispersed… a complex adaptive system has many levels of organization, with agents at any one level serving as the building blocks for agents at a higher level…complex adaptive systems are constantly revision and rearranging their building blocks as they gain experience…all complex adaptive systems anticipate the future…every complex adaptive system is constantly making predictions based on its various internal models of the world – its implicit or explicit assumptions about the way things are out there…complex adaptive systems typically have many niches, each one of which can be exploited by an agent adapted to fill that niche.” (145 – 147)

· “We human players have to make do with rules of thumb – hard-learned heuristic guides that tell us what kind of strategies will work best in a given situation.” (151)

· “A network that started out at random would rapidly organize itself. Experience would accumulate through a kind of positive feedback: the strong, frequently used synapses would grow stronger, while the weak, seldom-used synapses would atrophy…the selective strengthening of the synapses would cause the brain to organized itself into “cell assemblies” – subsets of several thousand neurons in which circulating nerve impulses would reinforce themselves and continue to circulate.” (158)

· “The payoff (or lack of it) gives agents the feedback they need to improve their performance: if they are going to be “adaptive” at all, they somehow have to keep the strategies that pay off well, and le the others die out.” (165)

· “The whole point of biological evolution is that there are no heuristic rules, no guidance of any soft; succeeding generations explore the space of possibilities by mutations and random reshuffling of genes among the sexes – in short, by trial and error…those succeeding generations don’t conduct their search in a step-by-step manner. They explore it in parallel: each member of the population has a slightly different set of genes and explores a slightly different region of the space.” (168)

· “That’s what this business of “emergence” was all about: building blocks at one level combining into new building blocks at a higher level.” (169)

· “Small, simple problems are easier to solve than big, messy problems.” (169)

· “Schema theorem, the fundamental theorem of genetic algorithms: in the presence of reproduction, crossover, and mutation, almost any compact cluster of genes that provides above-average fitness will grow in the population exponentially.” (174)

· “What actually has to happen for game-playing agents to survive and prosper? Prediction and feedback.” (176)

· “They talked about something called “knowledge engineering,” in which they would create the hundreds of rules needed for a new expert system by sitting down with the relevant experts for months: “What would you do in this situation? What would you do in that situation?” (180)

· Detectors keep the system up to date about what’s going on in the outside world. Effectors are subroutines that allow the system to affect the outside world. (182)

· “By adding the genetic algorithm as a third layer on top of the bucket brigade and the basic rule-based system, Holland could make an adaptive agent that not only learned from experience but could be spontaneous and creative.” (189)

· “Knowledge can be expressed in terms of mental structures that behave very much like rules; that these rules are in competition, so that experience causes useful rules to grow stronger and unhelpful rules to grow weaker: and that plausible new rules are generated from combinations of old rules…these three principles ought to cause the spontaneous emergence of default hierarchies as the basic organizational structure of all human knowledge.” (193)

Life at the Edge of Chaos

· Wolfram’s four classes of cellular automata (225 – 226)

o Class I contains doomsday rules: no matter what patterns of living and dead cells you started them out with, everything would die within 1 or 2 steps.

o Class II rules were more lively but resulted in frozen stagnation and death.

o Class III rules were too lively. These rules, in the language of dynamic systems, corresponded to “strange” attractors – chaos.

o Class IV rules produce coherent structures that propagated, grew, spilt apart, and recombined in a wonderfully complex way

Peasants Under Glass

· “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…they would learn how to think about the world as a dynamic, ever changing system poised at the edge of chaos.” (252)

· “Deep paradox in evolution: the fact that the same relentless competition that gives rise to evolutionary arms races can also give rise to symbiosis and other forms of cooperation.” (262)

Waiting for Carnot

· “The essence of a mechanical process – the “thing” responsible for its behavior – is not a thing at all. It is an abstract control structure, a program that can be expressed as a set of rules without regard to the material the machine is made of.” (278)

· “Since it’s effectively impossible to cover every conceivable situation, top-down systems are forever running into combinations of events that they don’t know to handle. They tend to be touchy and fragile, and they all too often grind to a halt in dither of indecision.” (279)

· “Right in between the two extremes, at a kind of abstract phase transition called “the edge of chaos,” you also find complexity: a class of behaviors in which the components of the system never quite lock into place, yet never quite dissolve into turbulence, either. These are to systems that are both stable enough to store information, and yet evanescent enough to transit it. These are the systems that can be organized to perform complex computations, to react t the world, to be spontaneous, adaptive, and alive.” (293)

· “You’d expect learning and evolution to make the edge of chaos stable, the natural place for complex, adaptive systems to be.” (295)

· “It seems that learning and evolution don’t just pull agents to the edge of chaos; slowly, haltingly, but inexorably, learning and evolution move agents along the edge of chaos in the direction of greater and greater complexity.” (296)

· “You can tell that a system is at the critical state and/or the edge of chaos if it shows waves of change and upheaval on all scales and if the size of the changes follows a power law.” (308)

Work in Progress

· “In Taoism there is no inherent order.” (338)

· Six fundamental transitions (350)

o Demographic to stable population

o Technological to minimal environmental impact per person

o Economic to charge real costs of goods and services with incentives to live off nature’s income rather than depleting its capital

o Social to broader sharing of income

o Institutional to set of supranational alliances

o Informational in which scientific research, education, and global monitoring allow large numbers of people to understand the nature of the challenges they face


Complexity: The Emerging Science at the Edge of Order and Chaos: Waldrop (1992)

Waldrop a scientific journalist is writing about an emerging field of science, Complexity, and the Santa Fe Institute that becomes a central home for its practice.

He writes a group biography where he tells numerous people’s stories that show the founding of the institute and the science – His mostly hidden purpose is to push the ideas of complexity.

The Science

Complexity – Complex systems that undergo spontaneous self-organizations

- Emergence – whole becomes bigger than the parts

- Increasing Returns (instead of diminishing returns) often associated with lock in of technology (QWERTY, VHR v. Beta, Clocks, Nuclear reactors, etc.)

- Edge of Chaos – a balanced place between order an chaos

These scientists (physicians, economists, biologist, etc.) work together in a cross discipline way to search for common mathematical formulas that can explain the theories, solve problems.

Spend allot so time talking about “what is life” and the origins of life, Darwinian evolution as the cross disciplined model of the 19th century.

The Institute

Had to find likeminded scientists, experts in their field that were open minded and had an affinity for complexity.

Had to define a purpose.

Had to fund raise, find a facility, answer all the administrative questions.

How to keep it fresh, relevant. How to force cross disciplined exchange.

Men with ego, driven and passionate about what they were doing.

Hughes à Hap Arnold, “Were will the AF get its longhaired officers?” – How do strategists reach out to the longhaired expert? How do you know what is solid and what is crap?

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