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The Computing of Digital Ecosystems
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Author(s): Gerard Briscoe (London School of Economics and Political Science, UK)and Philippe De Wilde (Heriot-Watt University, UK)
Copyright: 2012
Pages: 18
Source title:
Intelligent and Knowledge-Based Computing for Business and Organizational Advancements
Source Author(s)/Editor(s): Hideyasu Sasaki (Chinese University of Hong Kong, Hong Kong), Dickson K.W. Chiu (The University of Hong Kong, Hong Kong), Epaminondas Kapetanios (University of Westminster, UK), Patrick C.K. Hung (University of Ontario Institute of Technology, Canada), Frederic Andres (National Institute of Informatics, Japan), Ho-fung Leung (The Chinese University of Hong Kong, Hong Kong)and Richard Chbeir (Bourgogne University, LE2I CNRS, France)
DOI: 10.4018/978-1-4666-1577-9.ch015
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Abstract
A primary motivation this research in digital ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex and dynamic problems. However, the computing technologies that contribute to these properties have not been made explicit in digital ecosystems research. In this paper, the authors discuss how different computing technologies can contribute to providing the necessary self-organising features, including Multi-Agent Systems (MASs), Service-Oriented Architectures (SOAs), and distributed evolutionary computing (DEC). The potential for exploiting these properties in digital ecosystems is considered, suggesting how several key features of biological ecosystems can be exploited in Digital Ecosystems, and discussing how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, which consider the self-organised diversity of its evolving agent populations relative to the user request behaviour.
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