Gerard Briscoe

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures (EOA) where the word ecosystem is more than just a metaphor.

Value, Variety and Viability: New Business Models for Co-Creation in Outcome-based Contracts

While manufacturing in the past century has been essential to wealth creation, developed economies are gradually becoming service-oriented (Ramirez, 1999). Research recommends that manufacturers should diversify into providing services to remain viable, aiming to facilitate equipment use for customer outcomes rather than just transferring the ownership of equipment ( Neely, 2008; Baines et al, 2007). This means that the value proposition of the manufacturer changes from exchange value obtained from equipment provision, to value-in-use, obtained from the outcomes of equipment use. Outcome-based contracts such as Rolls-Royce’s “Power-by-the hour ®”, exemplifies such a change in value proposition, as the firm is paid not according to its service activities such as material and repairs, but based on the outcome of such activities in continual use situations i.e. the number of hours of engine in the air. This change in business model requires firm-customer relationships to be embedded in the processes and interactions of collaborative value-creating activities, ie value co-creation. Therefore, cooperation between the firm and its customer is a partnership that requires a “mutual and synergistic pooling of resources and capabilities and a substantial degree of co-mingling between partners in terms of people, systems, skills etc. in order to attain their objectives” (Madhok & Tallman, 1998).

Stability of Evolving Multiagent Systems

A multiagent system is a distributed system where the agents or nodes perform complex functions that cannot be written down in analytic form. Multiagent systems are highly connected, and the information they contain is mostly stored in the connections. When agents update their state, they take into account the state of the other agents, and they have access to those states via the connections. There is also external user-generated input into the multiagent system. As so much information is stored in the connections, agents are often memory less. This memory-less property, together with the randomness of the external input, has allowed us to model multiagent systems using Markov chains. In this paper, we look at multiagent systems that evolve, i.e., the number of agents varies according to the fitness of the individual agents. We extend our Markov chain model and define stability. This is the start of a methodology to control multiagent systems. We then build upon this to construct an entropy-based definition for the degree of instability (entropy of the limit probabilities), which we used to perform a stability analysis. We then investigated the stability of evolving agent populations through simulation and show that the results are consistent with the original definition of stability in nonevolving multiagent systems, proposed by Chli and De Wilde. This paper forms the theoretical basis for the construction of digital business ecosystems, and applications have been reported elsewhere.

Physical Complexity of Variable Length Symbolic Sequences

A measure called physical complexity is established and calculated for a population of sequences, based on statistical physics, automata theory, and information theory. It is a measure of the quantity of information in an organism’s genome. It is based on Shannon’s entropy, measuring the information in a population evolved in its environment, by using entropy to estimate the randomness in the genome. It is calculated from the difference between the maximal entropy of the population and the actual entropy of the population when in its environment, estimated by counting the number of fixed loci in the sequences of a population. Up until now, physical complexity has only been formulated for populations of sequences with the same length. Here, we investigate an extension to support variable length populations. We then build upon this to construct a measure for the efficiency of information storage, which we later use in understanding clustering within populations. Finally, we investigate our extended physical complexity through simulations, showing it to be consistent with the original.

Ecosystem-oriented distributed evolutionary computing

We create a novel optimisation technique inspired by natural ecosystems, where the optimisation works at two levels: a first optimisation, migration of genes which are distributed in a peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. We consider from the domain of computer science distributed evolutionary computing, with the relevant theory from the domain of theoretical biology, including the fields of evolutionary and ecological theory, the topological structure of ecosystems, and evolutionary processes within distributed environments. We then define ecosystem-oriented distributed evolutionary computing, imbibed with the properties of self-organisation, scalability and sustainability from natural ecosystems, including a novel form of distributed evolutionary computing. Finally, we conclude with a discussion of the apparent compromises resulting from the hybrid model created, such as the network topology.

Self-organisation of evolving agent populations in Digital Ecosystems

We investigate the self-organising behaviour of Digital Ecosystems, because a primary motivation for our research is to exploit the self-organising properties of biological ecosystems. We extended a definition for the complexity, grounded in the biological sciences, providing a measure of the information in an organism’s genome. Next, we extended a definition for the stability, originating from the computer sciences, based upon convergence to an equilibrium distribution. Finally, we investigated a definition for the diversity, relative to the selection pressures provided by the user requests. We conclude with a summary and discussion of the achievements, including the experimental results.