Managing Catastrophic Changes in a Collective

We address the important practical issue of understanding, predicting, and eventually controlling catastrophic endogenous changes in a collective. Such large internal changes arise as macroscopic manifestations of the microscopic dynamics, and their presence can be regarded as one of the defining features of an evolving complex system. We consider the specific case of a multiagent system related to the El Farol Bar model and show explicitly how the information concerning such large macroscopic changes becomes encoded in the microscopic dynamics. Our findings suggest that these large endogenous changes can be avoided either by pre-design of the collective machinery itself or in the postdesign stage via continual monitoring and occasional “vaccinations.”

[1]  Richard A. Heath.  Can People Predict Chaotic Sequences? , 2002 .

[2]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[3]  Edgar Kaufmann,et al.  Selecting the optimal sample fraction in univariate extreme value estimation , 1998 .

[4]  W. Arthur.  Inductive Reasoning and Bounded Rationality , 1994 .

[5]  David M. Raup,et al.  How Nature Works: The Science of Self-Organized Criticality , 1997 .

[6]  Mike Mannion,et al.  Complex systems , 1997, Proceedings International Conference and Workshop on Engineering of Computer-Based Systems.

[7]  Neil F. Johnson,et al.  Crowd effects and volatility in markets with competing agents , 1999 .

[8]  N. Johnson,et al.  Deterministic dynamics in the minority game. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  David Lamper,et al.  Anatomy of extreme events in a complex adaptive system , 2003 .

[10]  R. Metzler.  Antipersistent binary time series , 2001, cond-mat/0109243.

[11]  Stanley,et al.  Stochastic process with ultraslow convergence to a Gaussian: The truncated Lévy flight. , 1994, Physical review letters.

[12]  D Lamper,et al.  Predictability of large future changes in a competitive evolving population. , 2002, Physical review letters.

[13]  B. M. Hill,et al.  A Simple General Approach to Inference About the Tail of a Distribution , 1975 .

[14]  L. Haan,et al.  Using a Bootstrap Method to Choose the Sample Fraction in Tail Index Estimation , 2000 .

[15]  Neil F. Johnson,et al.  Trader Dynamics in a Model Market , 1999 .

[16]  Michael L. Hart,et al.  Dynamics of the time horizon minority game , 2002 .

[17]  A. Vulpiani,et al.  Predictability: a way to characterize complexity , 2001, nlin/0101029.

[18]  Michael L. Hart,et al.  An investigation of crash avoidance in a complex system , 2002 .

[19]  Yi-Cheng Zhang,et al.  Emergence of cooperation and organization in an evolutionary game , 1997 .

[20]  Yicheng Zhang,et al.  On the minority game: Analytical and numerical studies , 1998, cond-mat/9805084.

[21]  Per Bak,et al.  How Nature Works: The Science of Self-Organised Criticality , 1997 .

[22]  J. Corcoran.  Modelling Extremal Events for Insurance and Finance , 2002 .

[23]  M. Potters,et al.  Theory of Financial Risk , 1997 .

[24]  D. Sornette.  Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization and Disorder: Concepts and Tools , 2000 .

[25]  Didier Sornette,et al.  Large Stock Market Price Drawdowns are Outliers , 2000, cond-mat/0010050.

[26]  Kagan Tumer,et al.  Collective Intelligence for Control of Distributed Dynamical Systems , 1999, ArXiv.

[27]  Shlesinger.  Comment on "Stochastic process with ultraslow convergence to a Gaussian: the truncated Lévy flight" , 1995, Physical review letters.