Handbook of Research on Complexity
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Handbook of Research on Complexity

Edited by J. Barkley Rosser Jr.

Complexity research draws on complexity in various disciplines. This Handbook provides a comprehensive and current overview of applications of complexity theory in economics. The 15 chapters, written by leading figures in the field, cover such broad topic areas as conceptual issues, microeconomic market dynamics, aggregation and macroeconomics issues, econophysics and financial markets, international economic dynamics, evolutionary and ecological–environmental economics, and broader historical perspectives on economic complexity.
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Chapter 2: Complexity and the Economy

W. Brian Arthur


* W. Brian Arthur Complexity What exactly is complexity? Different people have different definitions and none is absolute. But common to all studies on complexity are systems with multiple elements adapting or reacting to the pattern these elements create. The elements might be cells in a cellular automaton, or ions in a spin glass, or cells in an immune system, and they may react to neighboring cells’ states, or local magnetic moments, or concentrations of B and T cells— “elements” and the “patterns” they respond to vary from one context to another. But the elements adapt to the world—the aggregate pattern— they co-create. Time enters naturally here via adjustment and change: as the elements react, the aggregate changes, as the aggregate changes, elements react anew. Barring some asymptotic state or equilibrium reached, complex systems are systems in process, systems that constantly evolve and unfold over time. Thus, complexity in the sciences is not a discipline. It is a movement that takes process seriously. Why did the complexity movement come along in the late 1970s and early 1980s? The answer is simple. Generally, complex systems have no analytic “solution.” The patterns that are in the process of being formed are too complicated to be worked out analytically and hence are beyond analytic study. But with the computer we can do agent-based modeling: we can treat each element in the system as a computational object that responds to the pattern created by the others, and thereby observe how the overall pattern forms....

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