Handbook of Research on Complexity

Handbook of Research on Complexity

Elgar original reference

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.

Chapter 3: Computational and Dynamic Complexity in Economics

J. Barkley Rosser Jr.

Subjects: economics and finance, evolutionary economics


J. Barkley Rosser, Jr.* 3.1 Introduction As reported by Horgan (1997, p. 305), Seth Lloyd has gathered at least 45 definitions of complexity. Rosser (1999) argued for the usefulness in studying economics of a definition he called dynamic complexity that was originated by Day (1994). This is that a dynamical economic system fails to generate convergence to a point, a limit cycle, or an explosion (or implosion) endogenously from its deterministic parts. It was argued that nonlinearity was a necessary but not sufficient condition for this form of complexity,1 and that this definition constituted a suitably broad “big tent” to encompass the “four Cs” of cybernetics, catastrophe, chaos, and “small tent” (or heterogeneous agents) complexity. Other approaches used in economics have included structural (Pryor, 1995; Stodder, 1995),2 hierarchical (Simon, 1962), and computational (Lewis, 1985; Albin with Foley, 1998; Velupillai, 2000). In recent years (Markose, 2005; Velupillai, 2005a, b, c) there has been a tendency to argue that the latter concept is superior because of its foundation on more well-defined ideas, such as algorithmic complexity (Chaitin, 1987) and stochastic complexity (Rissanen, 1989, 2005). These are seen as founded more deeply on work of Shannon (1948) and Kolmogorov (1983). Mirowski (2007) argues that markets themselves should be seen as algorithms that are evolving to higher levels in a Chomskyian (1959) hierarchy of computational systems, especially as they increasingly are carried over computers and become resolved through programmed double-auction systems and the like. McCauley (2004, 2005) and Israel (2005) argue that...

You are not authenticated to view the full text of this chapter or article.

Elgaronline requires a subscription or purchase to access the full text of books or journals. Please login through your library system or with your personal username and password on the homepage.

Non-subscribers can freely search the site, view abstracts/ extracts and download selected front matter and introductory chapters for personal use.

Your library may not have purchased all subject areas. If you are authenticated and think you should have access to this title, please contact your librarian.

Further information