Dynamic Models of Simple Judgments: II. Properties of a Self-Organizing PAGAN (Parallel, Adaptive, Generalized Accumulator Network) Model for Multi-Choice Tasks.
Douglas Vickers & Michael D. Lee, pg. 1
Keywords: connectionism, stochastic modeling, reaction time, identification, adaptation
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Simulating Withdrawal Behaviors in Work Organizations: An Example of a Virtual Society.
Steven T. Seitz, Charles L. Hulin, & Kathy A. Hanisch, pg. 33
Keywords: computational modeling, organizational withdrawal behavior, virtual societies, behavioral dynamics
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Local Rules and Fitness Landscapes: A Catastrophe Model.
Tim Haslett, Simon Moss, Charles Osborne, & Paul Ramm, pg. 67
Keywords: local rules, fitness landscapes, work effort, catastrophe shift, adaptive behaviour
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Random Fractal Time Series and the Teen-Birth Phenomenon.
Bruce J. West, Patti Hamilton, & Damien J. West, pg. 87
Keywords: teen births, fractal random time series, scaling, aggregated relative dispersion, sexual partner selection, sociological model
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Self-Organization and Leadership Emergence: A Cross-Cultural Replication.
Gonzalo Zaror & Stephen J. Guastello, pg. 113
Keywords: emergence, leadership, rugged landscape, self-organization, swallowtail catastrophe, South America
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BOOK REVIEW: Proceedings of the International Workshop on Nonlinear Dynamics and Chaos, edited by H.-T. Moon, S. Kim, R. P. Behringer, and Y. Kuramoto.
Robert A. M. Gregson, pg. 121
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Fish and Plankton Interplay Determines Both Plankton Spatio-Temporal Pattern Formation and Fish School Walks: A Theoretical Study.
Alexander B. Medvinsky, Dmitry A. Tikhonov, Jörg Enderlein, & Horst Malchow, pg. 135
Keywords: marine plankton, predator-prey interactions, reaction-diffusion equations, fish school motion, cellular automaton, wave propagation
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The Complexity of Artificial Grammars.
Erik M. Bollt & Michael A. Jones, pg. 153
Keywords: artificial grammars, implicit learning, symbolic dynamics, complexity, topological entropy
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Symbolic Dynamic Patterns of Written Exchanges: Hierarchical Structures in an Electronic Problem Solving Group.
Stephen J. Guastello, pg. 169
Keywords: topological entropy, Lyapunov dimension, symbolic dynamics, creative problem solving, group dynamics
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On Controlling Chaos in Cournot-Games with Two and Three Competitors.
E. Ahmed & S. Z. Hassan, pg. 189
Keywords: chaos control, Cournot games, duopoly and oligopoly
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Software Review.
Chaos Data Analyzer, Professional Version, by J. Sprott, & G. Rowlands. Reviewed by Patti Hamilton, pg. 195
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Nonlinear EEG Changes Associated with Clinical Improvement in Depressed Patients.
Nitza Thomasson, Laurent Pezard, Jean-François Allilaire, Bernard Renault, & Jacques Martinerie, pg. 203
Keywords: depression, dynamical disease, EEG, nonlinear dynamics, depressive mood
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Magnitude Estimations for Line Bisection Under Lateral Visuo-Spatial Neglect.
Robert A. M. Gregson, pg. 219
Keywords: psychophysics, nonlinear dynamics, vision, blindsight, lateral imbalance
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Modeling Maher’s Attribution Theory of Delusions as a Cusp Catastrophe.
Rense Lange & James Houran, pg. 235
Keywords: delusion, cusp catastrophe, GEMCAT II, attribution theory, belief formation
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Instabilities in Creative Professions: A Minimal Model.
Sergio Rinaldi, Roberto Cordone, & Renato Casagrandi, pg. 255
Keywords: creativity, motivation, model, cycles, Hopf bifurcation
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Epidemiology and Self-Organized Critical Systems: An Analysis in Waiting Times and Disease Heterogeneity.
Pierre Philippe, pg. 275
Keywords: complex systems, nonlinear dynamics, epidemiology, paradigm, inverse power law, fractal, incubation period, waiting times
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Chaotic Dynamics Underlying Action Selection in Mice.
Agnès Guillot & Jean-Arcady Meyer, pg. 297
Keywords: nonlinear dynamics, chaos, action selection, behavioral sequences, mouse
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A Dynamic Systems Approach to Understanding Reaching Movements with a Prosthetic Arm.
Stephen A. Wallace, D. L. Weeks, & P. Foo, pg. 311
Keywords: dynamical systems, artificial limbs, motor coordination
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Temperament Development Modeled as a Nonlinear Complex Adaptive System.
Ty Partridge, pg. 339
Keywords: determining the structural components of the system and Kauffman’s (1993) Boolean models of self-organization are adapted to estimate the parameter functions. In this model P(AW) = f(, ) where P(AW) is the probability density function of an approach or a withdrawal response,  is a standardized parameter estimate of the biological sensitivity to stimulation, and q is a standardized parameter estimate of the contextual response to an approach or withdrawal response. It is theorized that the functions of f and f follow a Hill function of the forms: df /dt = (2/c2 + 2) - K1, d/dt = (2/c2 + 2) - K2, where K1, K2, and c are system constants. This results in a double sigmoid function in which at extreme values of  and  the system stabilizes on a steady state of either approach or withdrawal response patterns. At intermediate parameter values the probability density functions of approach and withdrawal responses are wider. Thus, AW can be modeled as representing two basins of attraction. In addition, considerations are given to the systems sensitivity to initial conditions
ABSTRACT   PDF   

Ad Hoc Reviewers for 1999.
pg. 359
Keywords: determining the structural components of the system and Kauffman’s (1993) Boolean models of self-organization are adapted to estimate the parameter functions. In this model P(AW) = f(, ) where P(AW) is the probability density function of an approach or a withdrawal response,  is a standardized parameter estimate of the biological sensitivity to stimulation, and q is a standardized parameter estimate of the contextual response to an approach or withdrawal response. It is theorized that the functions of f and f follow a Hill function of the forms: df /dt = (2/c2 + 2) - K1, d/dt = (2/c2 + 2) - K2, where K1, K2, and c are system constants. This results in a double sigmoid function in which at extreme values of  and  the system stabilizes on a steady state of either approach or withdrawal response patterns. At intermediate parameter values the probability density functions of approach and withdrawal responses are wider. Thus, AW can be modeled as representing two basins of attraction. In addition, considerations are given to the systems sensitivity to initial conditions