2-filinghead.gif (6638 bytes)
Book Summary:

Out of Control: The New Biology of Machines,
Social Systems and the Economic World

By Kevin Kelly

Addison-Wesley, Reading, MA, 1994

ABSTRACT - This ground-breaking, insightful work pulls important new pattern-building findings from fields as diverse as computer science, biology, physics, and economics, relates them to the new worlds of complexity, chaos theory, and post-Darwin evolution and lays out the implications for creating complex organizations and systems of all types. Many of his findings are contrary to management traditions and practices.

Key Points:

  • As organizations become more complex and the need for adaptability increases, leaders will need to adopt lessons from nature’s complex systems (such as the critical role of variation and imperfections), which, in many cases, suggest non-traditional approaches to leadership and organization building.

  • Complex systems (organizations) need to be built up incrementally from simple systems which work.

  • Suggests that co-evolution, collaboration among organizations is a better strategy for insuring long-term survivability and stability than competition

  • Provides guidance to organizations from nature’s complex systems: distributed; decentralized; collaborative; adaptive.

  • Learn and follow the principles of evolution, like punctuated equilibrium, instead of trying to engineer the development of complex organizations.

  • The powerful link between learning and successful evolution is stressed

  • Complex systems have the power to make large scale change through large, rather than incremental shifts.

  • There is a desired number of connections among components of a system. This helps an organization live on the edge between chaos and stability and thus insure is survivability.

  • Makes a case for growth as natural law - presents seven trends underlying this organic evolution.

  • Despite the complexity of systems, certain types of prediction are possible. This, along with organizational flexibility achieved though decentralization and redundancy, foster successful adaptation.

  • Summarizes principle ideas from the book which apply to the creation of complex organizations

Hive Mind

Key Point: As organizations become more complex and the need for adaptability increases, leaders will need to adopt lessons from nature’s complex systems (such as the critical role of variation and imperfections), which, in many cases, suggest non-traditional approaches to leadership and organization building.

  • "It seems that the things we find most interesting in the universe are all dwelling near the web end...The class of systems to which all of the above belong is variously called: networks, complex adaptive systems, swarm systems, vivisystems, or collective systems. Organizationally, each of these is a collection of many (thousands) of autonomous members. "Autonomous" means that each member reacts individually according to internal rules and the state of its local environment. This is opposed to obeying orders from a center, or reacting in lock step to the overall environment. These autonomous members are highly connected to each other, but not to a central hub. They thus form a peer network. Since there is no center of control, the management and heart of the system are said to be decentrally distributed within the system, as a hive is administered. ...One theme of his book is that distributed artificial vivisystems...provide people with some of the attractions of organic systems, but also some of the drawbacks." P. 21-22

  • Benefits of swarm systems - adaptable, evolvable, resilient, boundless. p. 22-23

  • Disadvantages of swarm systems - non-optimal, non-controllable, non-predictable, non-understandable, non-immediate. p. 23-24

  • "As our inventions shift from the linear, predictable, causal attributes of the mechanical motor, to the crisscrossing, unpredictable, and fuzzy attributes of living systems, we need to shift our sense of what we expect from our machines (or organizations, my note). p. 24

  • A simple rule of thumb may help: For jobs where supreme control is demanded, good old clockware is the way to go. Where supreme adaptability is required, out-of-control swarmware is what you want." p. 24

  • "The inefficiencies of a network - all that redundancy and ricocheting vectors, things going from here to there and back just to get across the street - encompassing imperfection rather than rejecting it. A network nurtures small failures in order that large failures don’t happen as often. It is its capacity to hold error rather than scuttle it that makes the distributed being fertile ground for learning, adaptation, and evolution." p. 26

Machines with an Attitude

Key Point: Complex systems (like organizations) need to be built up incrementally from simple systems which work.

  • "When something works, don’t mess with it; build on top of it." p. 39

  • "A brain and body are made up the same way. From the bottom up. Instead of towns, you begin with simple behavior - instincts and reflexes. You make a little circuit that does a simple job, and you get a lot of them going. Then you overlay a secondary level of complex behavior that can emerge out of that bunch of working reflexes. The original level keeps working whether of not the second layer work or not. But when the second layer manages to produce more complex behavior, it subsumes the action of the layer below it. Here is the generic recipe for distributed control...It can be applied to most creations: 1. Do simple things first. 2. Learn to do them flawlessly. 3. Add new layers of activity over the results of the simple task. 4. Don’t change the simple things. 5. Make the new layer work as flawlessly as the simple. 6. Repeat, ad infinitum. This script could also be called a recipe for managing complexity of any type, for that is what it is." p. 41

  • "In the human management of distributed control, hierarchies of a certain type will proliferate rather than diminish...While authoritarian "top-down" hierarchies will retreat, no distributed system can survive for long without nested hierarchies of lateral "bottom-up" control. As influence flows peer to peer, it coheres into a chunk- a whole organelle - which then becomes the bottom unit in a larger web of slower actions. Over time a multi-level organization forms around the percolating-up control: fast at the bottom, slow at the top. The second important aspect of generic distributed control is that the chunking of control must be done incrementally from the bottom. It is impossible to take a complex problem and rationally unravel the mess into logical interacting pieces. Such well-intentioned efforts inevitably fail." p. 45

  • "The law is concise: Distributed control has to be grown from simple local control. Complexity must be grown from simple systems that already work." p. 46


Key Point: Suggests that co-evolution, collaboration among organizations is a better strategy for insuring long-term survivability and stability than competition.

  • "Here’s the news: half of the living world is codependent!...The surge of alliance-making in the 1990s among large corporations...is another facet of an increasing, co-evolutionary economic world. Rather than eat or compete with a competitor, the two form an alliance - a symbiosis." p. 75

  • "Paul Ehrlich sees co-evolution pushing two competitors into "obligate cooperation." He wrote, " It’s against the interests of either predator or prey to eliminate the enemy" That is clearly irrational, yet that is clearly a force that drives nature." p. 76

  • "Every complex adaptive organization faces a fundamental tradeoff. A creature must balance perfecting a skill of trait (building up legs to run faster) against experimenting with new traits (wings). It can never do all things at once. This daily dilemma is labeled the tradeoff between exploration and exploitation." p. 87

  • "It turns out that no matter what clever strategy you engineer or evolve in a world laced by chameleon-on-a-mirror loops, if it is applied as a perfectly pure rule that you obey absolutely, it will not be evolutionary resilient to competing strategies. That is, a competing strategy will figure out how to exploit your rule in the long run. A little touch of randomness (mistakes, imperfections), on the other hand, actually creates long-term stability in co-evolutionary worlds by allowing some strategies to prevail for relative eons by not being so easily aped." p.89

  • "The highly connected loops of co-evolutionary conflict mean the whole can reward (or at times cripple) all members. Axelrod told me, "One of the earliest and most important insights from game theory was that nonzero-sum games had very different strategic implications than zero-sum games. In zero-sum games whatever hurts the other guy is good for you. In non-zero-sum games you can both do well, or both do poorly."" p. 89

  • "Perhaps the most useful lesson of co-evolution for "wannabe" gods is that in co-evolutionary worlds control and secrecy are counterproductive. You can’t control, and revelation works better than concealment. "In zero-sum games you always try to hide your strategy," says Axelrod. "But in non-zero-sum games you might want to announce your strategy in public so the other players need to adapt to it."" p. 90

  • "In the Network Era - that age we have just entered - dense communications is creating artificial worlds ripe for emergent co-evolution, spontaneous self-organization, and win-win cooperation. In this Era, openness wins, central control is lost, and stability is a state of perpetual almost-falling ensured by constant error." p. 90

Network Economics

Key Point: Provides guidance to organizations from nature’s complex systems: distributed; decentralized; collaborative; adaptive.

  • "The challenge is simply stated: Extend the company’s internal networks outward to include all those with whom the company interacts in the marketplace. Spin a grand web to include employees, suppliers, regulators, and customers, they; they all become part of your company’s collective being. They are the company." p. 188

  • "One can imagine the future shape of companies by stretching them until they are pure network. a company that was pure network would have the following traits: distributed, decentralized, collaborative, and adaptive. p. 189

  • "Distributed - There is not single location of the business. It dwells among many place concurrently." p. 189

  • "Decentralized - Now, when the economy shifts daily, owning the whole chain of production is a liability....In short, networks make outsourcing feasible, profitable, and competitive." p. 191

  • "Collaborative - Networking internal jobs can make so much economic sense that sometimes vital functions are outsourced to competitors, to mutual benefit. Enterprises may be collaborators on one undertaking and competitors on another, at the same time....The metaphor for corporations is shifting from the tightly coupled, tightly bounded organism to the loosely coupled, loosely bounded ecosystem." p. 193

  • "Adaptive - "DESPITE MY SUNNY FORECAST for the network economy, there is much about it that is worrisome. These are the same concerns that accompany other large decentralized, self-making systems: *You can’t understand them. *You have less control. *They don’t optimize well." p. 194

Artificial Evolution

Key Points: Learn and follow the principles of evolution, like punctuated equilibrium, instead of trying to engineer the development of complex organizations.

  • "To scientists, the most exhilarating news to come out of Ray’s artificial evolution machine is that his small worlds display what seems to be punctuated equilibrium. For relatively long periods of time, the ratio of populations remain in a steady tango of give and take with only the occasional extinction or birth of a new species. Then, in a relative blink, this equilibrium is punctuated by a rapid burst of rolling change with many newcomers and eclipsing of the old. For a short period change is rampant. Then things sort out and stasis and equilibrium reigns again. The current interpretation of fossil evidence on Earth is that this pattern predominates in nature. Stasis is the norm; change occurs in bouts." p. 289

  • "There are only two ways we know of to make extremely complicated things," says Hillis. "One is by engineering, and the other is evolution. And of the two, evolution will make the more complex." p. 295

  • "Little dumb creatures in parallel that can "write" better software than humans can suggests to Ray a solution for our desire for parallel software....When it comes to distributed network kinds of things, Ray says, "Evolution is the natural way to program." The natural way to program! That’s an ego-deflating lesson. Humans should stick to what they do best: small, elegant, minimal systems that are fast and deep. Let natural evolution (artificially injected) do the messy big work." p. 308

  • "The trouble of evolution is not entirely out of our control; surrendering some control is simply a tradeoff we make when we employ it. The things we are proud of in engineering - precision, predictability, exactness, and correctness - are diluted when evolution is introduced. These have to be diluted because survivability in a world of accidents, unforeseen circumstances, shifting environments - in short, the real world - demands a fuzzier, looser, more adaptable, less precise stance. Life is not controlled. Vivisystems are not predictable. Living creatures are not exact." p. 310

  • "Give up control, and we’ll artificially evolve new worlds and undreamed-of richness. Let go, and it will blossom." p. 311

The Structure of Organized Change

Key Point:  The powerful link between learning and successful evolution is stressed.

  • "Despite the confusion about the word "evolution," our strongest terms of change are rooted in the organic: grow, develop, mutate, learn, metamorphose, adapt. Nature is the realm of ordered change. p. 353

  • "Only in the last couple of years has the exhilarating link between learning, behavior, adaptation, and evolution even begun to be investigated...A number of researchers...have shown clearly and unequivocally how a population of organisms that are learning - that is, exploring their fitness possibilities by changing behavior - evolve faster than a population that are not learning." p. 358


Key Point: Complex systems have the power to large scale change through large, rather than incremental shifts.

  • "As the French evolutionist Pierre Grasse said, "Variation is one thing, evolution quite another; this cannot be emphasized strongly enough...Mutations provide change, but not progress." So while natural selection may be responsible for microchange - a trend in variations - no one can say indisputably that it is responsible for macrochange - an open-ended creation of an unexpected novel form and progress toward increasing complexity." p. 370

  • "But intriguing suspicions now accumulating in the study of complex systems, particularly complex systems that adapt, learn, and evolve, suggest Darwin was wrong in his most revolutionary premise. Life is largely clumped into parcels and only mildly plastic. Species either persist of die. They transmute into something else under only the most mysterious and uncertain conditions. By and large, complex things fall into categories and the categories persist. Human institutions clumps - churches, departments, companies - find it easier to grow than to evolve.

  • "Required to adapt too far from their origins, most institutions will die. "Organic" entities are not infinitely malleable because complex systems cannot easily be gradually modified in a sequence of functional intermediates. A complex system is severely limited in the directions and ways it can evolve, because it is a hierarchy composed entirely of sub-entities, which are also limited in their room for adaptation because they are composed of sub-sub-entities, and so on down the tower. It should be no surprise, then, to find that evolution works in quantum steps. The given constituents of an organism can collectively make this or that, but not everything is between this and that. The hierarchical nature of the whole prevents it from reaching all the possible states it might theoretically hit. At the same time, the hierarchical arrangement of the whole gives it power to make some large-scale shifts." p. 381-382

The Butterfly Sleeps

Key Point: There is a desired number of connections among components of a system. This helps an organization live on the edge between chaos and stability and thus insure its survivability.

  • "Deep down Kauffman felt that his systems built themselves. In some way he hoped to discover, evolutionary systems controlled their own structure. From the first glimpse of his visionary network image, he had a hunch that in those connections lay the answer to evolution’s self-governance." p. 398

  • "As Kauffman increased the average number of links between nodes, the system became more resilient, "bouncing back" when perturbed. The system could maintain stability while the environment changed. It would evolve. The completely unexpected finding was that beyond certain level of linking density, continued connectivity would only decrease the adaptability of the system as a whole....In the long run, an overly linked system was as debilitating as a mob of uncoordinated loners" p. 399

  • "At the ideal number of connections, the ideal amount of information flowed between agents, and the system as a whole found the optimal solutions consistently. If their environment was changing rapidly, this meant that the network remained stable - persisting as a whole over time." p. 400

  • "He (Langton) says that systems that are most adaptive are so loose they are a hairsbreadth away from being out of control. Life, then, is a system that is neither stagnant with non-communication nor grid-locked with too much communication. Rather life is a vivsystem tuned "to the edge of chaos" - that lambda point where there is just enough information flow to make everything dangerous." p. 402

  • "Self-tuning may be the mysterious key to evolution that doesn’t stop - the holy grail of open-ended evolution. Chris Langton formally describes open-ended evolution as a system that succeeds in ceaselessly self-tuning itself to higher and higher level of complexity, or in his imagery, a system that succeeds in gaining control over more and more parameters affecting its evolvability and staying balanced on the edge." p. 403

Rising Flow

Key Point: Makes the case for growth as a natural law and presents seven trends underlying this organic evolution.

  • "The search for a Second Law of Biology, a law of rising order, is unconsciously behind much of the search for deeper evaluations and the quest for hyperlife." p. 405

  • "The order accumulated by the rising wave serves as a plank to extend itself, using energy from outside, into the next realm of further order. As long a Carnot’s force flows downhill and cools the universe, the rising flow can steal heat to flow uphill in places, building itself high by pulling on its bootstraps." p. 405

  • "Caveats aside, I discern about seven large trends or directions emerging from the ceaseless, hourly toil of organic evolution. These trends, as far as anyone can tell, are also the seven trends that will bias artificial evolution when it goes marathon; they may be said to be the Trends of Hyper-evolution: Irreversibility, Increasing Complexity, Increasing Diversity, Increasing Number of Individuals, Increasing Specialization, Increasing Codependency, Increasing Evolvability." p. 412

  • "Evolution is a conglomeration of many processes which form a society of evolutions. As evolution has evolved over time, evolution itself has increased in diversity and complexity and evolvability." p. 417

Prediction Machinery

Key Point: Despite the complexity of systems, certain types of prediction are possible. This, along with organizational flexibility achieved though decentralization and redundancy, foster successful adaptation..

  • "....prediction is a form of control. It is a type of control particularly suited to distributed systems. By anticipating the future, a vivisystem can shift its stance to preadapt to it, and in this way control its destiny. John Holland says, "Anticipation is what complex adaptive systems do."" p. 420

  • "...the character of chaos carries both good news and bad news. The bad news is that very little, if anything, is predicable far into the future. The good news - the flip side of chaos - is that in the short term, more may be more predictable than it first seems...."There is order is chaos."" p. 424

  • "The short answer is that tiny errors (caused by limited information) compound into grievous errors when extended very far into the future." p. 426

  • "Most of the time most of a complex system may not be forecastable, but some small part of it may be for short times." p. 428

  • "...the work of Theodore Modis, whose 1992 book, Predictions, nicely sums up the case for utility and believability of predictions. Modis addresses three types of found order in the greater web of human interactions. Each variety forms a pocket of predictability at certain times.

  • Invariants. The natural and unconscious tendency for all organisms to optimize their behavior instills in that behavior "invariants" that change very little over time...

  • Growth Curve. Growing things share several universal characteristics. Among them are a lifespan that can be plotted as an S-shaped curve: slow birth, steep growth, slow decline..."What is hidden under the graceful shape of the S-curve is that fact that natural growth obeys a strict law." This law says that the shape of the ending is symmetrical to the shape of the beginning...

  • Cyclic Waves. According to Modis, cyclic phenomenon in nature can infuse a cyclic flavor to systems running within it." p. 436-437

  • "Together, these three modes of prediction suggests that at certain moments of heightened visibility, the invisible pattern of order becomes clear to those paying attention." p. 437
  • "...we know that feedback loops alone are insufficient to breed the behaviors of the vivissystems we find most interesting. There are two additional types of complexity (there may be others) the researchers in this book have found necessary in order to give birth to a full spectrum of vivisystem character: distributed being and open-ended evolution..." p. 448

  • "The key insight uncovered by the study of complex systems in recent years is this: the only way for a system to evolve into something new is to have a flexible structure...This is why distributed being is so important to learning and evolving systems. A decentralized, redundant organization can flex without distorting its function, and this it can adapt. It can manage change. We call that growth. Direct feedback models...can achieve stabilization - one attribute of living systems - but they can’t learn, grow, diversity - three essential complexities for a model of changing culture or life." p. 448

  • "But we cannot import evolution and learning without exporting control." p. 448

The Nine Laws of God

Key Point: Summarizes principle ideas from the book which apply to the creation of complex organizations.

  • "Out of nothing, nature makes something....How do you make something from nothing? Although nature knows this trick, we haven’t learned much just by watching her. We have learned more by our failures in creating complexity and by combining these lessons with small successes in imitating and understanding natural systems. So from the frontiers of computer science, and the edges of biological research, and the odd corners of interdisciplinary experimentation, I have compiled The Nine Laws of God governing the incubation of somethings from nothing...These nine laws are the organizing principles that can be found operating in systems as diverse as biological evolution and SimCity.

  • Distribute being. The spirit of a beehive, the behavior of an economy, the thinking of a supercomputer, and the life in me are distributed over a multitude of smaller units (which themselves may be distributed). When the sum of the parts can add up to more than the parts, then that extra being (that something from nothing) is distributed among the parts...All the mysteries we find most interesting - life, intelligence, evolution - are found in the soil of large distributed systems.

  • Control from the bottom up. When everything is connected to everything in a distributed network, everything happens at once. When everything happens at once, wide and fast moving problems simply route around any central authority. Therefore overall governance must arise from the most humble interdependent acts done locally in parallel, and not from a central command...

  • Cultivate increasing returns. Each time you use an idea, a language, or a skill you strengthen it, reinforce it, and make it more likely to be used again...Anything which alters its environment to increase production of itself is playing the game of increasing returns. And all large, sustaining systems play at the game...Life on Earth alters Earth to beget more life...

  • Grow by chunking. The only way to make a complex system that works is to begin with a simple system that works. Attempts to instantly install highly complex organization...without growing it, inevitably lead to failure... Complexity is created then, by assembling it incrementally from simple modules that can operate independently.

  • Maximize the fringes. In heterogeneity is creation of the world. A uniform entity must adapt to the world by occasional earth-shattering revolutions, one of which is sure to kill it. A diverse heterogeneous entity, on the other hand, can adapt to the world in a thousand daily mini-revolutions, staying in a state of permanent, but never fatal churning. Diversity favors borders, the outskirts, hidden corners, moments of chaos, and isolated clusters. In economic, ecological, evolutionary, and institutional models, a healthy fringe speeds adaptation, increases resilience, and is almost always the source of innovations.

  • Pursue no optima; have multiple goals. Simple machines can be efficient, but complex adaptive machinery cannot be...Rather than strive for optimization of any function, a large system can only survive by "satisficing" (making "good enough") a multitude of functions. For instance, an adaptive system must trade off between exploiting a known path of success (optimizing a current strategy), or diverting resources to exploring new paths (thereby wasting energy trying less efficient methods)....forget elegance; if it works, it’s beautiful.

  • Seek persistent disequilibrium. Neither consistent nor relentless change will support a creation. A good creation, like good jazz, must balance the stable formula with frequent out-of-kilter notes... A Something is persistent disequilibrium - a continuous state of surfing forever on the edge between never stopping but never falling....

  • Change change itself. When extremely large systems are built up out of complicated systems, then each system begins to influence and ultimately change the organization of other systems." p. 468-471

Next | Previous | List of Terms

Copyright 2001, Plexus Institute Permission
to copy for educational purposes only.