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Physica A: Statistical Mechanics and its Applications
Volume 287, Issues 3-4, 1 December 2000, Pages 599-612
 
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doi:10.1016/S0378-4371(00)00396-4    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2000 Elsevier Science B.V. All rights reserved.

Stochastic urn models of innovation and search dynamics

Werner EbelingCorresponding Author Contact Information, E-mail The Corresponding Author, a, Lutz Molgedeya and Axel Reimanna

a Humboldt-University Berlin, Institute of Physics, Invalidenstrasse 110, D-10115 Berlin, Germany

Received 8 May 2000;
revised 13 June 2000.
Available online 4 December 2000.

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Abstract

This work is devoted to applications of the Ehrenfest urn model to innovation and search processes. In the first part we discuss systems of two urns serving as models of innovation processes. The elementary act of innovation is considered as a transition from old (technologies, way of production, behavior, decisions) to new. The survival probability of the new under the influence of stochastic effects is discussed. In the second part we study systems of smuch greater-than1 urns serving as models for optimal solution searching in optimization problems. The problem is to find the minimum on a large set of real numbers Ui using a total of N seekers (Nsimilar, equals2–100) simultaneously. The potential Ui is defined on the integer set i=1,…,s, where s is extremely large. In particular, we consider the frustrated periodic strings and the merit problem. The known equations for thermodynamic search processes and for simple models of biological evolution are unified by defining a two-parameter family of equations which embeds both cases. The search parameters are controlled by means of seeker ensemble dispersion.

Author Keywords: Urn model; Innovation; Search process; Variability; Parameter control

PACS classification codes: 02.50.Ey; 05.10.-a; 05.10.Gg

Article Outline

1. Introduction
2. Ehrenfest's urn model: dynamics on the occupation number space
3. Stochastic modeling of mixed strategies
4. Simulations – the dependence on search parameters
5. Search parameter adaption by variability control
6. Discussion
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