Friday, May 1, 2009

Complexity

This was an exciting, enlightening and humbling book for me to read. I was educated as a physicist and didn’t go past my masters’ degree partially because I was disenchanted with physics. The edge of physics had been left to the mathematical physicists. And, even though I minored in mathematics, this type of mathematics was beyond my capability to appreciate in physical terms. I could not visualize in real world terms what the equations meant. It seemed to be just a lot of mathematical manipulations that sometimes produced answers that described some aspect of reality.

Had I discovered the beginnings of the science of emergence, I might have stayed in the field. It’s a fresh new look at old phenomena with far ranging implications. I despaired when I learned that the field started in 1948 and was being worked on in the late 1950s when I left academia to work on semiconductors in industry. The Santa Fe Institute, an independent offshoot from Los Alamos Labs, was created in 1984 and work has spread all over the world since then.


From: Complexity, Wikipedia

Complexity science is not a single discipline, but a collaboration of disciplines. Some have called it a trans-disciplinary science. George Cowan, the creator of the Santa Fe Institute called it “the sciences of the twenty-first century.”

This book is a history of the development of the science of complexity and the formation of the Santa Fe Institute.
In addressing a number of questions unanswered by pre-complexity science, the author writes:

“In every case, moreover, the very richness of these interactions allows the system as a whole to undergo spontaneous self-organization. Thus, people trying to satisfy their material needs unconsciously organize themselves into an economy through myriad individual acts of buying and selling; it happens without anyone being in charge or consciously planning it. The genes in a developing embryo organize themselves in one way to make a liver cell and in another way to make a muscle cell. Flying birds adapt to the actions of their neighbors, unconsciously organizing themselves into a flock. Organisms constantly adapt to each other through evolution, thereby organizing themselves into an exquisitely tuned ecosystem. Atoms search for a minimum energy state by forming chemical bonds with each other, thereby organizing themselves into structures known as molecules. In every case, groups of agents seeking mutual accommodation and self-consistency somehow manage to transcend themselves, acquiring collective properties such as life, thought, and purpose that they might never have possessed individually.

Furthermore, these complex, self-organizing systems are adaptive, in that they don't just passively respond to events the way a rock might roll around in an earthquake. They actively try to turn whatever happens to their advantage. Thus, the human brain constantly organizes and reorganizes its billions of neural connections so as to learn from experience (sometimes, anyway). Species evolve for better survival in a changing environment-and so do corporations and industries. And the marketplace responds to changing tastes and lifestyles, immigration, technological developments, shifts in the price of raw materials, and a host of other factors.

Finally, every one of these complex, self-organizing, adaptive systems possesses a kind of dynamism that makes them qualitatively different from static objects such as computer chips or snowflakes, which are merely complicated. 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.”

One of the big stories featured in the book is the development of a complexity science for economics. Economics had become very mathematical as it responded to criticisms from the sciences that it was too “soft”. The result was very complex mathematical systems that did not represent the reality of economics. These mathematical systems were all based on an equilibrium model, which history has shown many times is not true for economic systems. Unfortunately, even with all the progress made by the Santa Fe Institute to develop a complexity science for economics, I see little impact in the present environment.

What we have seen is that highly structured and controlled economies have failed all around us.

The story is compelling and the writing fluid. It was a joy to read this book.

Complexity: The Emerging Science at the Edge of Order and Chaos
M. Mitchell Waldrop, Touchstone, 1992, 380 p

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