Thursday, July 29, 2010

Some Thoughts on Complexity

Complexity includes at least three states of being where many of the rules we take for granted no longer apply. Concepts like causation, predictability, repeatability, control, analysis, determinism, linearity, and even centralization don’t work in complex systems. When dealing with a complex system it’s as though you’ve gone through a looking glass and everything you thought you knew is no longer valid. Furthermore, many complex systems exist at a human scale. We interact with many of these complex systems in daily life, natural and man made, and as a result become part of some of these complex systems.

It requires a paradigm shift to begin to comprehend complex systems. I shudder to use the word because it’s been so over used and trivialized. But I can think of no other concept to describe the shift. Complexity requires new perceptions, new beliefs, new ways of thinking and new rules for problem solving

The first barrier to be overcome is perception. Our belief systems are so strong, our scotomas so large, and our fears so great that resistance to accept the new reality is enormous. That’s understandable. After all, our world is built on the concepts listed above that aren’t valid in complex systems.

There is a fable about Columbus arriving in the new world and the natives unable to “see” the three ships. They had no mental construct, or paradigm, that would allow them to perceive the ships. A medicine man sat for days looking out at the water. By studying the pattern of the ripples caused by the ships, he was able to finally see them.

In essence that’s about the extent of our perception of complex systems. We can see the effects, and in some cases measure the effects, but we don’t understand how they work, based on our previous paradigm.

The three types of complex systems are critical state, chaotic and emergent. Earthquakes result from a critical state, and markets appear to be in a critical state. Weather is chaotic. And, massively parallel systems of intelligent agents exhibit emergence, like termites.

Lewis Carroll described a complex system with intelligent agents in Alice’s Adventures in Wonderland:

Alice thought she had never seen such a curious croquet ¬ground in her life: it was all ridges and furrows: the croquet balls were live hedgehogs, and the mallets live flamingoes, and the soldiers had to double themselves up and stand on their hands and feet, to make the arches.
The chief difficulty Alice found at first was in managing her flamingo: she succeeded in getting its body tucked away, com¬fortably enough, under her arm, with its legs hanging down, but generally, just as she had got its neck nicely straightened out, and was going to give the hedgehog a blow with its head, it would twist itself round and look up in her face, with such a puz¬zled expression that she could not help bursting out laughing; and, when she had got its head down, and was going to begin again, it was very provoking to find that the hedgehog had unrolled itself, and was in the act of crawling away: besides all this, there was generally a ridge or a furrow in the way wherever she wanted to send the hedgehog to, and, as the doubled-up soldiers were always getting up and walking off to other parts of the ground, Alice soon came to the conclusion that it was a very difficult game indeed.

The players all played at once, without waiting for turns, quarreling all the while, and fighting for the hedgehogs; and in a very short time the Queen was in a furious passion, and went stamping about, and shouting "Off with his head!" or "Off with her head!" about once in a minute. Alice began to feel very uneasy: to be sure, she had not as yet had any dispute with the Queen, but she knew that it might happen any minute, "and then," thought she, "what would become of me? They're dreadfully fond of beheading people here: the great wonder is, that there's any one left alive!"

Complexity is transdisciplinary, i.e. a discipline that crosses many older disciplines. There are examples of complexity in math, chemistry, physics, geology, weather and the environment, economics, markets, biology, medicine, etc.

One last closing thought. I’m not writing about complicated systems. Complicated systems in general do not exhibit the properties of complex systems.

Skills Required to Deal with Complexity
The skill set required to deal with complexity hasn’t been developed yet as you can appreciate as it is a new paradigm that crosses almost all older disciplines. However, there are some things I can say about the concepts that have to be learned.

The common concepts across all complex systems that must be taught are:
  • Systems : We need to teach - What a system is, The types of systems, How one determines the type of a system, When you have included enough of the system, How a system responds to its environment and History of systems
  • Reductionism and holism: Both concepts need to be taught, along with an understanding of when each one should be applied.
  • Statistics: Statistics is a branch of mathematics concerned with collecting and interpreting data. Depending on the system, different types of statistics need to be used. People need to know how to collect and interpret data taken from the history of all types of complex systems. And, we need to teach the difference between probability and risk.
  • Pattern recognition: In Emergence, Steven Johnson comments, “As the futurist Ray Kurzweil writes, 'Humans are far more skilled at recognizing patterns than in thinking through logical combinations, so we rely on this aptitude for almost all of our mental processes. Indeed, pattern recognition comprises the bulk of our neural circuitry. These faculties make up for the extremely slow speed of human neurons.' ...the brain is a massively parallel system, with 100 billion neurons all working away at the same time.” We need to educate people in how to use this capability, at a conscious level, to see patterns in systems or to use tools to assist in that recognition process.
  • New ways of looking at the future and the development of a long term perspective.
  • Techniology: The study of how technology is developed and how society influences what technologies get developed and how technology influences society (think sociology or ecology)
  • There are several important concepts that must be taught: Chaos, Criticality and Emergence
  • Decentralization: “As we enter the Era of Decentralization, there is an important educational challenge: How can we help people become intellectually engaged with the new types of systems and new ways of thinking that characterize this new era? To date, schools and other educational institutions have done little, if anything, to engage students with the idea of decentralization. Instead, they often perpetuate centralized explanations and approaches” comments Mitchel Resnick, Turtles, Termites, and Traffic Jams. “There is an apparent paradox in people's reactions to decentralized systems.” Resnick explains. “On one hand is the allure of decentralization. They are fascinated by systems that are organized without an organizer, coordinated without a coordinator.” On the other hand, “When people see patterns in the world, they intuitively assume that the patterns are created by lead or by seed.” That's the educational challenge. Sanders and McCabe write, “Complex adaptive systems, and models thereof, are characterized by distributed organizations or networks, whose parts all influence each other, either directly or through feedback loops, which continually evolve and adapt to accomplish overarching goals. This is in fundamental contrast to the top-down, hierarchical management structures found in most government organizations and in much of corporate America, where local experimentation, innovation and adaption are discouraged in favor of rigid bureaucratic rules and planning procedures. Simple cause and effect relationships do not characterize complex adaptive systems, and hence most of the conventional policy-planning tools currently used by decision-makers, both government and corporate, are inappropriate and ineffective. Complexity science offers new ways of understanding, thinking about and designing organizational systems that are capable of responding to and influencing complex nonlinear relationships. Understanding the local dynamics in a complex system can provide great insight into the behavior of the overall system and help identify key leverage points of change and transformation.”
  • Trans-disciplinary approaches: “Complexity science is truly an interdisciplinary science” write Irene Sanders and Judith McCabe. “Adopting a systems view of the world meant that the questions were too big for any one discipline alone to answer. As scientists began looking for connections among the different types of complex systems, the boundaries between disciplines began to open. As a result, we are witnessing the integration of knowledge across the disciplines – the physical sciences, social sciences and the humanities. Insights about complex systems are emerging across a broad spectrum of fields - from physics, mathematics and computer science, to biology, oceanology, neuroscience, art and architecture. From health care to city planning, complexity science is creating a fundamental shift in the way we view the dynamics and interactions of complex systems.” This the real message of complexity science (in the sense of Marshall McLuhan's “The medium is the message.”) Complexity science is the “medium” through which multiple disciplines can together transcend their parochial views, and facilitate the emergence of a new world view.
At this point I only know of two types of skills required – intellective and interpersonal.

The concept of intellective skills was developed by Shoshana Zuboff in In the Age of the Smart Machine: The Future of Work and Power:

…The thinking this operator refers to is of a different quality form the thinking that attended the display of action-centered skills. It combines abstraction, explicit inference, and procedural reasoning. Taken together, these elements make possible a new set of competencies that I call Intellective skills. As long as the new technology signals only deskilling – the diminished importance of action-centered skills – there will be little probability of developing critical judgment at the data interface. To rekindle such judgment, though on a new more abstract footing, a re-skilling process is required. Mastery in a computer-mediated environment depends upon developing intellective skills.

Interpersonal skills will have to include collaboration, decentralization, leadership, creativity, innovativeness, flexibility, openness, self development, tolerance for ambiguity and uncertainty and others I’m sure.

Management of Complexity
First, if it’s true complexity and not just an extremely complicated system, there is no known way to manage it. The best we can do now is to manage the effects of a complex system. For example, I mentioned earthquakes as being the result of a complex system in a critical state. We can’t manage the earthquake or even predict when an earthquake will occur or its magnitude. But, we do know from the history of an earthquake prone fault system that there is a probability of a certain magnitude of earthquake occurring. We then design buildings to survive that magnitude of earthquake.

No analysis of the BP and Toyota examples has been done to determine if the systems are complex or just extremely complicated. Or, what type of complex system they might be.

Tad Patzek, chairman of the Petroleum and Geosystems Engineering Department at the University of Texas, testified in Congress last month about the Gulf of Mexico oil spill. His comments included observations on broad, long-term trends in industry, government and academia. He reported, "Horrible things happen when complex technologies and procedures overtake humans, who service the technologies falsely assuming complete control.” Patzek asserts that it is a complex system with emergent properties. I doubt that this is true. I think it most likely a complex system in a critical state. I suspect its behavior is more like an earthquake (criticality) than a termite mound (emergence). It probably has some characteristics of both as the human and software subsystems (intelligent agents) in the system could exhibit some characteristics of emergence. However, his conclusions are valid: large events like this will occur, and we can't predict when they will occur or prevent them. Understandably, he never states this as directly as I did. How can you tell that to politicians or business people? (Remember the Queen in the croquet game?)

The software developers have tamed the complicatedness of large software programs such as operating systems by turning a hoard of software developers, who are not in the same organization, loose on the project and then by facilitating the development of a complex system of intelligent agents (individual programmers) with positive emergent properties. This requires a degree of openness, decentralization, flexibility, cooperation and loss of control not presently possible in most modern corporations. Nor, do we know how to apply these principles in systems other than software.

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