Evolutionary Systems

What is this course about?

Evolutionary Systems are rooted in complex systems, complex systems are rooted in complexity theory, complexity theory is rooted in chaos theory, and chaos theory is rooted in non-equilibrium thermodynamics.

The study of Evolutionary Systems seeks to explain how complex system such as the geochemical systems, organisms, ecosystems, and economies organize, grow, and evolve by bottom-up processes; that is, without central planning or control.

Chaos and complexity deal with phenomena that are so prevalent and pervasive in the everyday world that we have come to know these systems intuitively just in the process of living. What most of us have not gotten is a systematic explanation of what these systems are or how they work. It is unfortunately that most of what we are taught in school has little or no bearing on how the real world really works.

Or, said another way, complex evolutionary systems are not strange and esoteric, but part of our common experience, from observations of how the weather cycles, to how the economy behaves, cities expand, and civilization evolves.

But, just because they are familiar does not mean we understand them. For example, we are trying to understand what Adam Smith's "invisible hand" is, and how and why it works to regulate an economy. We are trying to understand how an ant colony works and matures when no one ant is in charge or lives for more than a year (except the queen, and she is definitely not in charge - despite the Woody Allen movie Antz). Or, the dynamics of how a city grows, in spite of our attempts to control it. Or, the evolutionary patterns in human history when thousands of years separate various civilizations. And we plan to explore all these systems.

Theories of chaos and complexity may seem strange at first because of their unfamiliarity, but they are no more difficult to understand than any other subject. What is required is a systematic introduction so that we can get our minds around phenomena we already know intuitively but not rationally.

The experimental methods include mathematical modeling in Artificial Life, SOC (self organized criticality), autocatalytic networks, and neural networks, among other. All of these are based on the computational viewpoint which argues that the behavior and evolution of complex systems cannot be deduced from first principles but must be discovered by watching the system evolve in time.

The heart of the course is Robert May's population model Xnext = rX (1-X). This deceptively simple equation exhibits the most amazing and wonderful behavior, and we have a couple of neat computer programs that model its behavior. X-next allows us to explore questions such as what makes populations stabilize? What makes them fluctuate? Are populations in complex ecosystems more stable than populations in simple ecosystems?

(See for example http://pup.princeton.edu/titles/7050.html).

From this foundation the course uses a variety of other computer based models and experiments to systematically explore concepts such as local rules/global behavior, sensitive dependence on initial conditions (the butterfly effect), self organized criticality and avalanche behavior, and emergent behavior (the whole is more than the sum of the parts).

The difficult part for us is that each subject, each example, we explore has had a very complex history in its own right. Sometimes the main problem is overcoming our preconceived notions, taught to us in school, of how the world should operate when in fact the theory of complex systems says it is operating in a very different way. The reason for these difficulties is that until recently we have not had the theoretical or computational tools to explore systems as complex systems. Yet, that is exactly the challenge of this course, to see familiar systems from unfamiliar perspectives.

Therefore, we must begin by creating an entire new world view, and new models through which to examine that world. Our basic strategies are both Top-Down and Bottom-Up. Top-Down means examining a system from a theoretical viewpoint, from the Platonic ideal if you will.

Bottom-Up means examining a system by gathering observations and inductively drawing conclusions and laws about what they are and how they behave. Beginning with Plato and Aristotle, if not before, these two strategies have oscillated back and forth in importance and prominence, not only in science but in many other disciplines. Indeed, we want to explore and understand even this subject since the history of ideas is also an evolutionary system, and misunderstandings at this level underlie much of the historical controversy in every discipline.

Our strategy is to fluidly move back and forth between Top-Down and Bottom-Up strategies, while keeping them distinct in our minds. We begin the semester with the very abstract, Top-Down, theoretical X-next model, and then examine real world systems to see how they fit it. And then back to another Top-Down model, followed by examination of other real world systems. From our viewpoint, both strategies are essential to understanding evolutionary systems, focusing on one, or the other, leaves you unable to understand the system.

The hardest problem we have is finding enough time in the semester to explore enough systems to demonstrate that the principles of evolutionary systems apply to ALL systems. A quick flip through the course subjects shows us going from mathematicall models, to fractal geometry, to oscillating chemical systems, to intelligent systems, to economic systems, to game theory, to sociology, to the Gaia hypothesis, and many subjects in between. Every one of these is a subject all its own. In most cases you can get advanced degrees studying these systems. How can we hope to deal with all of them?

Yet, that is our goal. Not to systematically explore each subject, but to see how the principles of evolutionary systems applies to each subject. What this means is that we usually do not examine a subject the way the average person in the discipline would. Nor do we build a systematic knowledge base in each discipline. In fact, often the way we explore each subject would curl the toes of the average person in the discipline, not only because they have a different framework from us, but also because we have different goals than they do. Many times the principles we develop are not even part of the common language of their discipline - yet.

But, we believe that in time they will become part of the language. Such a transformation is taking place, for example, in economics. Classical economics, the theory of competitive or general equilibrium, still largely influences the way economists think, yet the very concept of "equilibrium" is anathema to complex evolutionary systems. Equilibrium implies, . . . well equilibrium, resting in a stable state. From the perspective of complex systems all systems self-evolve to the critical state where they are poised for avalanches; in economics, stock market crashes.

From the perspective of complex systems, crashes are an integral and inevitable part of the system, not an aberration. That does not mean there are not economists who study economics as a complex system, but most of their insights are not even yet in basic introductory economics textbooks. The same story can be told about every discipline and evolutionary system we touch on, although some are farther along in using the theory of complex systems.

The final issue is a demonstration that freedom, properly constrained by wise laws, is capable of producing the best outcomes. Or, to quote Milton Friedman winner of the 1976 Nobel Prize in economics:

Freedom.- economic, political, and civil - is an end in itself, not a means to an end. It is what makes life worthwhile. We would prefer to live in a free country even if it did not provide us and our fellow citizens with a higher standard of living than an alternate regime... But . . . . free societies have arisen and persisted only because economic freedom is so much more productive economically than other methods of controlling economic activity.

In the language of game theory, freedom is a non-zero sum game, and is inherent to the system, one of the fundamental rules of the game, and life must play it. Or, as Robert Wright explains it in his book Nonzero.

In the great non-zero games of history, if you're part of the problem, you'll likely be a victim of the solution.

Or, in Wright's other words: The underlying reason that non-zero-sum games wind up being played well is the same in biological evolution as in cultural evolution. Whether you are a bunch of genes or a bunch of memes, if you're all in the same boat you'll tend to perish unless you are conducive to productive coordination.... Genetic evolution thus tends to create smoothly integrated organisms, and cultural evolution tends to create smoothly integrated groups of organisms.

Which leads us into questions of ethics . . . another whole world of issues that complex evolutionary systems has a lot to say about.

So, what you need to bring to the class is an open, flexible mind, one that is able to think logically about a subject that requires a rich and creative imagination. The only prerequisite is curiosity and a willingness to think about and experiment with ideas in non-linear, non-traditional, and unfamiliar ways.