Complexity: The Emerging Science at the Edge of Order and Chaos

he envisions a new second law that explains how emergent entities will do the most interesting things when they’re at the edge of chaos, and how adaptation will inexorably build these entities up into higher and higher levels of complexity.

There is no general-purpose procedure that can scan the code and the input and give you the answer any faster than that.

Instead of being designed from the top down, the way a human engineer would do it, living systems always seem to emerge from the bottom up, from a population of much simpler systems.

Any given organism’s ability to survive and reproduce depends on what niche it is filling, what other organisms are around, what resources it can gather, even what its past history has been.

In particular, he says, he wanted to see the program take some of the classical problems in economics, the hoary old chestnuts of the field, and see how they changed when you looked at them in terms of adaptation, evolution, learning, multiple equilibria, emergence, and complexity—all the Santa Fe themes.

Order → “Complexity” → Chaos where “complexity” referred to the kind of eternally surprising dynamical behavior shown by the Class IV automata.

Watson and Crick finally unraveled the molecular structure of DNA a few years later, in 1953, they discovered that it fulfilled von Neumann’s two requirements precisely.

von Neumann’s analysis of self-reproduction was simplicity itself. To restate it in a slightly more formal way, he was saying that the genetic material of any self-reproducing system, natural or artificial, has to play two fundamentally different roles. On the one hand, it has to serve as a program, a kind of algorithm that can be executed during t

The cluster of rules forming a default hierarchy is essentially synonymous with what Holland calls an internal model. We use weak general rules with stronger exceptions to make predictions about how things should be assigned to categories: