We propose that designing a manufacturers equipment-based service value proposition in outcome-based contracts is the design of a new business model capable of managing threats to the firms viability that can arise from the contextual variety of use
that customers may subject the firms value propositions. Furthermore, manufacturers need to understand these emerging business models as the capability of managing both asset and service provision to achieve use outcomes with customers, including emotional outcomes such as customer experience. Service-Dominant Logic proposes that all goods are a distribution mechanism for service provision, upon which we propose a value-centric approach to understanding the interactions between the asset and service provision, and suggest a viable systems approach towards reorganising the firm to achieve such a business model. Three case studies of B2B equipment-based service systems were analysed to understand customers co-creation activities in achieving outcomes, in which we found that the co-creation of complex multi-dimensional value could be delivered through the different value propositions of the firm catering to different aspects (dimensions) of the value to be co-created. The study provides a way for managers to understand the effectiveness (rather than efficiency) of firms in adopting emerging business models that design for value co-creation in what are ultimately complex socio- technical systems.
The 2011 Grand Challenge in Service conference aimed to explore, analyse and evaluate complex service systems, utilising a case scenario of delivering on improved perception of safety in the London Borough of Sutton, which provided a common context t
o link the contributions. The key themes that emerged included value co-creation, systems and networks, ICT and complexity, for which we summarise the contributions. Contributions on value co-creation are based mainly on empirical research and provide a variety of insights including the importance of better understanding collaboration within value co-creation. Contributions on the systems perspective, considered to arise from networks of value co-creation, include efforts to understand the implications of the interactions within service systems, as well as their interactions with social systems, to co-create value. Contributions within the technological sphere, providing ever greater connectivity between entities, focus on the creation of new value constellations and new demand being fulfilled through hybrid offerings of physical assets, information and people. Contributions on complexity, arising from the value co- creation networks of technology enabled services systems, focus on the challenges in understanding, managing and analysing these complex service systems. The theory and applications all show the importance of understanding service for the future.
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 proces
s 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 evolu- tionary computing. Finally, we conclude with a discussion of the apparent compromises resulting from the hybrid model created, such as the network topology.
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 biol
ogical sciences, providing a measure of the information in an organisms 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.
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 organisms genome. It is base
d on Shannons 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 to 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.
A Multi-Agent System is a distributed system where the agents or nodes perform complex functions that cannot be written down in analytic form. Multi-Agent Systems are highly connected, and the information they contain is mostly stored in the connecti
ons. 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 Multi-Agent 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 Multi-Agent Systems using Markov chains. In this paper, we look at Multi-Agent 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 Multi-Agent 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 non-evolving Multi-Agent 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.
While service-dominant logic proposes that all Goods are a distribution mechanism for service provision (FP3), there is a need to understand when and why a firm would utilise direct or indirect (goods) service provision, and the interactions between
them, to co-create value with the customer. Three longitudinal case studies in B2B equipment-based complex service systems were analysed to gain an understanding of customers co-creation activities to achieve outcomes. We found the nature of value, degree of contextual variety and the firms legacy viability to be viability threats. To counter this, the firm uses (a) Direct Service Provision for Scalability and Replicability, (b) Indirect Service Provision for variety absorption and co-creating emotional value and customer experience and (c) designing direct and indirect provision for Scalability and Absorptive Resources of the customer. The co-creation of complex multidimensional value could be delivered through different value propositions of the firm. The research proposes a value-centric way of understanding the interactions between direct and indirect service provision in the design of the firms value proposition and proposes a viable systems approach towards reorganising the firm. The study provides a way for managers to understand the effectiveness (rather than efficiency) of the firm in co-creating value as a major issue in the design of complex socio-technical systems. Goods are often designed within the domain of engineering and product design, often placing human activity as a supporting role to the equipment. Through an SDLogic lens, this study considers the design of both equipment and human activity on an equal footing for value co-creation with the customer, and it yielded interesting results on when direct provisioning (goods) should be redesigned, considering all activities equally.
We investigate an abstract conceptualisation of DigitalEcosystems from a computer science perspective. We then provide a conceptual framework for the cross pollination of ideas, concepts and understanding between different classes of ecosystems throu
gh the universally applicable principles of Complex Adaptive Systems (CAS) modelling. A framework to assist the cross-disciplinary collaboration of research into Digital Ecosystems, including Digital BusinessEcosystems (DBEs) and Digital Knowledge Ecosystems (DKEs). So, we have defined the key steps towards a theoretical framework for Digital Ecosystems, that is compatible with the diverse theoretical views prevalent. Therefore, a theoretical edifice that can unify the diverse efforts within Digital Ecosystems research.
A primary motivation for our 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, dynamic pr
oblems. However, the computing technologies that contribute to these properties have not been made explicit in digital ecosystems research. Here, we 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, considering the self-organised diversity of its evolving agent populations relative to the user request behaviour.
A primary motivation for our 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, dynamic pr
oblems. Self-organisation is perhaps one of the most desirable features in the systems that we engineer, and it is important for us to be able to measure self-organising behaviour. We investigate the self-organising aspects of Digital Ecosystems, created through the application of evolutionary computing to Multi-Agent Systems (MASs), aiming to determine a macroscopic variable to characterise the self-organisation of the evolving agent populations within. We study a measure for the self-organisation called Physical Complexity; based on statistical physics, automata theory, and information theory, providing a measure of information relative to the randomness in an organisms genome, by calculating the entropy in a population. We investigate an extension to include populations of variable length, and then built upon this to construct an efficiency measure to investigate clustering within evolving agent populations. Overall an insight has been achieved into where and how self-organisation occurs in our Digital Ecosystem, and how it can be quantified.
