Handbook of Research on Nature Inspired Computing for Economics and Management, 1st Edition

  • Editor: Jean-Philippe Rennard [Grenoble Graduate School of Business (France)]
  • Published By:
  • ISBN-10: 1591409853
  • ISBN-13: 9781591409854
  • Grade Level Range: College Freshman - College Senior
  • 925 Pages | eBook
  • Original Copyright 2006 | Published/Released April 2008
  • This publication's content originally published in print form: 2006

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The Handbook of Research on Nature-Inspired Computing for Economics and Management is the original, comprehensive reference work on research and applications of nature inspired computing to economics and management. It is an authoritative source, providing global coverage of this new and exciting field. Gathering the work of over 100 internationally known contributors, this two-volume set explores how complexities found in nature can be modeled to simulate and optimize business situations. It provides practitioners a global view of the current and future applications of this ground-breaking technology, and also includes more than 1,900 references to existing literature in the field.

Key features include:

  • Authoritative contributions by 103 internationally renowned experts
  • A single source for comprehensive information on an expansive field
  • In-depth definitions of more than 400 key terms
  • Organized by topic and indexed, making it a convenient method of reference for all IT/IS scholars and professionals
  • More than 1,900 references to existing literature and research on computer modeling for business
  • Cross-referencing of key terms, figures, and information pertinent to nature-inspired computing
  • 112 tables and more than 380 figures with detailed illustrations



  • Jean-Philippe Rennard [Grenoble Graduate School of Business (France)]

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Editorial Advisory Board.
Other Frontmatter.
List of Contributors.
Table of Contents.
Detailed Table of Contents.
1: Nature–Inspired Computing.
2: Artificiality in Social Sciences.
3: Multi–Cellular Techniques.
4: Stochastic Optimization Algorithms.
5: Evolutionary Algorithms.
6: Genetic Programming.
7: Evolutionary Multi–Objective Optimization in Finance.
8: Social Modeling.
9: Simulation in the Social Sciences.
10: Multi–Agent Systems Research and Social Science Theory Building.
11: A Dynamic Agent–Based Model of Corruption.
12: Human Nature in the Adaptation of Trust.
13: Cognitively Based Modeling of Scientific Productivity.
14: Nature–Inspired Knowledge Mining Algorithms for Emergent Behaviour Discovery in Economic Models.
15: The Grid for Nature–Inspired Computing and Complex Simulations.
16: Economics.
17: Agent–Based Computational Economics.
18: Data Gathering to Build and Validate Small–Scale Social Models for Simulation.
19: Modeling Qualitative Development.
20: Agent–Based Modeling with Boundedly Rational Agents.
21: Heterogeneous Learning Using Genetic Algorithms.
22: Modeling the Firm as an Artificial Neural Network.
23: Evolutionary Modeling and Industrial Structure Emergence.
24: Population Symbiotic Evolution in a Model of Industrial Districts.
25: Competitive Advantage of Geographical Clusters.
26: A Simulation of Strategic Bargainings within a Biotechnology Cluster.
27: Knowledge Accumulation in Hayekian Market Process Theory.
28: On Technological Specialization in Industrial Clusters.
29: Simulating Product Invention Using InventSim.