Robert G. Reynolds - Cultural Algorithms: Tools to Model Complex Dynamic Social Systems

1. Chapter 3
2. Chapter 4
3. Chapter 5
4. Chapter 6
5. Chapter 7
6. Chapter 8
7. Chapter 9
8. Chapter 10

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 4
5. Chapter 5
6. Chapter 6
7. Chapter 7
8. Chapter 8
9. Chapter 9
10. Chapter 10

IEEE Press
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IEEE Press Editorial Board
Ekram Hossain, Editor in Chief

Robert G. Reynolds

Department of Computer Science
College of Engineering
Wayne State University
Detroit, Michigan 48202

and

Visiting Research Scientist
Museum of Anthropological Archaeology
University of MIchiganAnn Arbor
Ann Arbor, MIchigan 481071259

Copyright 2021 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
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Library of Congress CataloginginPublication Data:

Names: Reynolds, Robert G., author.
Title: Cultural algorithms : tools to model complex dynamic social systems / Robert G. Reynolds.
Description: Hoboken, New Jersey : John Wiley & Sons, [2020] | Series: IEEE Press series on computational intelligence | Includes bibliographical references and index.
Identifiers: LCCN 2020001817 (print) | LCCN 2020001818 (ebook) | ISBN 9781119403081 (hardback) | ISBN 9781119403098 (adobe pdf) | ISBN 9781119403104 (epub)
Subjects: LCSH: Social systemsMathematical models. | CultureMathematical models. | Algorithms. | Social intelligence. | Computational intelligence.
Classification: LCC H61.25 .R49 2020 (print) | LCC H61.25 (ebook) | DDC 300.1/5181dc23
LC record available at https://lccn.loc.gov/2020001817
LC ebook record available at https://lccn.loc.gov/2020001818

Cover Design: Wiley
Cover Image: engel.ac/Shutterstock

Anas AL-Tirawi
Department of Computer Science, Wayne State University, Detroit, MI, USA

Rami Alazrai
Department of Computer Engineering, German Jordanian University, Amman, Jordan

Mostafa Z. Ali
Department of Computer Information Systems, Jordan University of Science and Technology, Irbid, Jordan

Mohammad I. Daoud
Department of Computer Engineering, German Jordanian University, Amman, Jordan

Samuel Dustin Stanley
Computer Science Department, Wayne State University, Detroit, MI, USA

Mehdi Kargar
Ted Rogers School of Management, Ryerson University, Toronto, ON, Canada

Khalid Kattan
Computer Science Department, Wayne State University, Detroit, MI, USA

Leonard Kinnaird-Heether
Department of Computer Science, Wayne State University, Detroit, MI, USA

Ziad Kobti
School of Computer Science, University of Windsor, Windsor, ON, Canada

Thomas Palazzolo
Department of Computer Science, Wayne State University, Detroit, MI, USA

Robert G. Reynolds
Department of Computer Science, Wayne State University, Detroit, MI, USA
The Museum of Anthropological
Archaeology, University of Michigan-Ann Arbor, Ann Arbor, MI, USA

Kalyani Selvarajah
School of Computer Science, University of Windsor, Windsor, ON, Canada

Faisal Waris
Department of Computer Science, College of Engineering, Wayne State University, Detroit, MI, USA

This book is accompanied by a companion website:

www.wiley.com/go/CAT

The website includes:

• Supplementary materials

Robert G. Reynolds

Computer Science, Wayne State University, Detroit, MI, USA

The Museum of Anthropological Archaeology, University of MichiganAnn Arbor, Ann Arbor, MI, USA

By and large, most approaches to machine learning focus on the solution of a specific problem in the context of an existing system. Cultural Algorithms are a knowledgeintensive framework that is based on how human cultural systems adjust their structures and contents to address changes in their environments []. These changes can produce a solution to the new problem within the existing social framework. Beyond that, the system can adapt its framework in order to produce the solution for a larger class of related problems. Cultural Algorithms are able to mimic this behavior by the selfadaptation of its knowledge and population components.

In other words, we are participating in the Cultural learning process right now. However, as part of the process it is hard to assess what progress, if any, is being made by the system. The Cultural Algorithm provides a framework by which we can step outside of the system so that we can assess its trajectories more clearly. This issue is addressed somewhat by the notion of humancentric learning. However, such an approach suggests that we are ultimately in control of the learning activities. In reality, we are embedded in a performance environment that we have partially created on the one hand, and have been passed down as the result of millions of years of evolution on the other.