Institutional Dynamics, Deterministic Chaos, and Self-Organizing Systems

Radzicki, Michael J.

doi:10.1080/00213624.1990.11505001

Cited by 86 publications

(39 citation statements)

References 37 publications

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“…6 Figure 2 presents an example of such a chaotic strange attractor, in particular the Rössler (1976) attractor 7 that has been found in several economics models exhibiting chaotic dynamics (Goodwin, 1990;Radzicki, 1990), with x, y, and z possibly representing deviations from long-term trends of capital stock, GDP, and price level respectively. This pattern represents what might be an equilibrium trajectory in three variables in the sense that a deterministic system would follow it forever-if it ever got onto the path.…”

Section: Trade Cycle Model Savings Is a Linear Function Of Income ( Y)mentioning

confidence: 99%

On the Complexities of Complex Economic Dynamics

Rosser

1999

Journal of Economic Perspectives

303

122

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Complex economic nonlinear dynamics endogenously do not converge to a point, a limit cycle, or an explosion. Their study developed out of earlier studies of cybernetic, catastrophic, and chaotic systems. Complexity analysis stresses interactions among dispersed agents without a global controller, tangled hierarchies, adaptive learning, evolution, and novelty, and out-of-equilibrium dynamics. Complexity methods include interacting particle systems, self-organized criticality, and evolutionary game theory, to simulate artificial stock markets and other phenomena. Theoretically, bounded rationality replaces rational expectations. Complexity theory influences empirical methods and restructures policy debates.

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Section: Trade Cycle Model Savings Is a Linear Function Of Income ( Y)mentioning

confidence: 99%

On the Complexities of Complex Economic Dynamics

Rosser

1999

Journal of Economic Perspectives

303

122

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“…The feedback mechanisms of the CS with the dimension of three or more prevent the errors from growing to infinity. Instead, a CS can go through extremely complicated behavior where it "stretches and folds over onto itself, like a baker kneading dough" (Radzicki 1990). When mapped on the phase space, an abstract geometrical space representing states of the system in time, behavior of a CS could exhibit three different patterns.…”

Section: Conceptual Foundationsmentioning

confidence: 99%

“…Behavior of a system can be represented in terms of an attractor, which is a "set of points in the phase space of a dynamical feedback system that defines its steady state motion" (Radzicki 1990). Every attractor has a basin of attraction, or a set of points in the space of system variables that evolve to a particular attractor.…”

Section: Conceptual Foundationsmentioning

confidence: 99%

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Information systems fitness and risk in IS development: Insights and implications from chaos and complex systems theories

Samoilenko

2008

Inf Syst Front

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Information Systems Development often takes place within a complex and uncertain socio-technical environment. The use of methodologies is considered an appropriate way of reducing risks of failures of the ISD projects, allowing for managing of the complexity of the process and product of ISD. Traditional functionalist methodologies, however, are not adequately equipped for dealing with non-linear interactions endemic to such complex social systems as IS. This paper examines IS development from the perspective of the complex systems behavior and chaos theory. It offers insights and implications for augmenting traditional approaches to ISD that could lead to better strategies for managing complexities in the ISD process and the behavior of an IS.

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“…In the next section we review the key issues in the theory of chaos. Since a number of comprehensive surveys of the economie applications of this theory in the last decade have been published recently (see Kelsey, 1988;Baumol and Benhabib, 1989;Boldrin and Woodford, 1991;Scheinkman, 1990;Radzicki, 1990;Rosser, 1991), our survey can be brief. However, we will illustrate the key issues by means of two models of economie development in discrete time.…”

Section: Introductionmentioning

confidence: 99%