The Computing of Digital Ecosystems

Introduction

Digital Ecosystems are distributed adaptive open socio-technical systems, with properties of self-organisation, scalability and sustainability, inspired by natural ecosystems (Briscoe, 2009), and are emerging as a novel approach to the catalysis of sustainable regional development driven by Small and Medium sized Enterprises (SMEs). Digital Ecosystems aim to help local economic actors become active players in globalisation, valorising their local culture and vocations, and enabling them to interact and create value networks at the global level (Dini et al., 2008).

We have previously considered the biological inspiration for the technical component of Digital Ecosystems (Briscoe, Sadedin, & Paperin, 2007; Briscoe & Sadedin, 2007), and we will now consider the relevant computing technologies. Based on our understanding of biological ecosystems in the context of Digital Ecosystems (Briscoe & De Wilde, 2006), we will now introduce fields from the domain of computer science relevant to the creation of Digital Ecosystems. As we are interested in the digital counterparts for the behaviour and constructs of biological ecosystems, instead of simulating or emulating such behaviour or constructs, we will consider what parallels can be drawn. We will start by considering MASs to explore the references to agents and migration (Briscoe et al., 2007; Briscoe & Sadedin, 2007); followed by evolutionary computing and SOAs for the references to evolution and self-organisation (Briscoe et al., 2007; Briscoe & Sadedin, 2007).

The value of creating parallels between biological and computer systems varies substantially depending on the behaviours or constructs being compared, and sometimes cannot be done so convincingly. For example, both have mechanisms to ensure data integrity. In computer systems, that integrity is absolute, data replication which introduces even the most minor change is considered to have failed, and is supported by mechanisms such as the Message-Digest algorithm 5 (Rivest, 1992). While in biological systems, the genetic code is transcribed with a remarkable degree of fidelity; there is, approximately, only one unforced error per one hundred bases copied (McCulloch et al., 2004). There are also elaborate proof-reading and correction systems, which in evolutionary terms are highly conserved (McCulloch et al., 2004). In this example establishing a parallel is infeasible, despite the relative similarity in function, because the operational control mechanisms in biological and computing systems are radically different, as are the aims and purposes. This is a reminder that considerable finesse is required when determining parallels, or when using existing ones.