www.wiley.com.
Library of Congress Cataloging‐in‐Publication 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 systems–Mathematical models. | Culture–Mathematical models. | Algorithms. | Social intelligence. | Computational intelligence.
Classification: LCC H61.25 .R49 2020 (print) | LCC H61.25 (ebook) | DDC 300.1/5181–dc23
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
List of Contributors
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
About the Companion Website
This book is accompanied by a companion website:
www.wiley.com/go/CAT
The website includes:
Supplementary materials
1 System Design Using Cultural Algorithms
Robert G. Reynolds
Computer Science, Wayne State University, Detroit, MI, USA
The Museum of Anthropological Archaeology, University of Michigan‐Ann Arbor, Ann Arbor, MI, USA
Introduction
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 knowledge‐intensive framework that is based on how human cultural systems adjust their structures and contents to address changes in their environments [1]. 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 self‐adaptation 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 “human‐centric” 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.
The framework for the Cultural Algorithm is given in Figure 1.1. A networked population of agents interact with each other in the population space. The network of agents is termed the social fabric. Agents are connected with each other in the network based on their level of interaction. If the level of interaction between a pair of agents falls below a certain level, that connection can be lost. In that sense, the network is like a piece of cloth where a stress on some portion of the fabric can lead to a disruption or tear in the fabric. Such tears can be mended over time if interactions resume. It is a key feature of Cultural Algorithms since they need to be able to simulate not only the growth but also the decline of social systems [2].
Figure 1.1 Cultural Algorithm framework.
The results of agent interaction within the performance environment in which they are embedded can be accepted into the Belief Space. The Belief Space is a repository of the knowledge acquired by the system so far. It is viewed as a network of different knowledge sources. The accepted knowledge is then integrated into the network through the use of learning procedures that make focused adjustments to the cultural compendium of knowledge. The Information “cloud” can be viewed as the current manifestation of the Belief Space using current technology.
These knowledge sources in the Belief Space can be “active” and or “passive.” Active knowledge sources directly select individuals based on their location and history in the social fabric (network). Passive knowledge sources are selected by individual agents in the network. A knowledge source
