This study investigates the notion of limits to socioeconomic growth with a specific focus on the role of climate change and the declining quality of fossil fuel reserves. A new system dynamics model has been created. The World Energy Model (WEM) is based on the World3 model (The Limits to Growth, Meadows et al., 2004) with climate change and energy production replacing generic pollution and resources factors. WEM also tracks global population, food production and industrial output out to the year 2100. This paper presents a series of WEM's projections; each of which represent broad sweeps of what the future may bring. All scenarios project that global industrial output will continue growing until 2100. Scenarios based on current energy trends lead to a 50% increase in the average cost of energy production and 2.4-2.7 degrees C of global warming by 2100. WEM projects that limiting global warming to 2 degrees C will reduce the industrial output growth rate by 0.1-0.2%. However, WEM also plots industrial decline by 2150 for cases of uncontrolled climate change or increased population growth. The general behaviour of WEM is far more stable than World3 but its results still support the call for a managed decline in society's ecological footprint.

A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant, personalized, and context-sensitive information delivery. Yet traditional approaches to user profiles rely on explicit, brittle models that go out of date very quickly, lack relevance, and have few natural connections to related models. In this chapter the authors show how it is possible to create adaptive user profiles without any explicit input at all. Rather, leveraging implicit behaviour on social information networks, the authors can create profiles that are both adaptive and socially connective. Such profiles can help provide personalized access to enterprise resources and help identify other people with related interests.

Purpose The purpose of this paper is to explore the nature of interactions between learners in a massive open online course (MOOC), particularly role of the tutors in such interactions. For educators concerned with sustainability literacy, the authors are necessarily both affected by, and effectors of, digital pedagogies. The call for papers for this special issue challenged the authors to consider whether digital pedagogies are “supportive of sustainability or perpetuators of unsustainability”. As might be expected, this question is not a simple binary choice and the authors have chosen to address it indirectly, by considering the nature of interaction in a global, digitally connected community of learners. In particular, the changing role of tutors in these communities, and the possible implications of this change on sustainable literacy, are examined. Design/methodology/approach The authors focus on the “Sustainability for Professionals” massive open online course (MOOC) delivered by the University of Bath on the FutureLearn platform which hosts the “Inside Cancer” MOOC, also from Bath. “Sustainability for Professionals” is pedagogically connectivist, with “Inside Cancer” being more traditional and instructor led. The authors used social network analysis (SNA) for the research. It is a key tool to understand interactions in an online environment and allows quantitative comparison between different networks and thus between courses. In the context of digital pedagogy, the authors used a number of relevant SNA metrics to carry out analysis of MOOC network structures. Findings It was found that MOOCs are different in their network structure but tend to adapt to the subject matter. Digital pedagogies for sustainability result in a qualitative as well as quantitative change in learning where course design affects the learning process and gatekeepers are critical for information flow. These gatekeepers are distinct from tutors in the network. In such a network, tutors’ role is limited to course delivery and verifying, depending on course content, the information within the network. The analysis shows that network learning is dependent on course design and content, and gatekeepers exercise influence over the information within the network. Originality/value This study has implications for sustainability literacy. The authors examined the extent to which patterns of interaction in the network affect the learning process, and how this can help participants engage with the concept of sustainability. They used SNA to explore the nature of interaction between learners in a MOOC, particularly the role of the tutors in mediating such interactions. They also found that tutors can and do take a central role in early runs of the MOOC; however, with the subsequent runs, the removal of tutor nodes has little effect, suggesting that different modes of learning driven by participants are possible in a MOOC community.

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological con-

This paper explores measurement of product performance with respect to circular economy principles. Potential indicators are assessed, with special attention given to questions such as: the variables that should be measured; how these variables should be assessed; and in which format they should be presented. The resulting considerations are used to develop a prototype whose design is informed through feedback from Circular Economy experts. The prototype uses a points-based questionnaire which converges into a simple final result with minimum and maximum limits. The selected approach is critically appraised, and its utility for decision-making discussed. The strengths include: ease of use; simplicity; speed; and an effective metaphor for the diffusion of circular economy principles. The limitations include: the opaque and potentially misleading nature of a single metric; superficial engagement with decision making; and the reliance on context specific assumptions. Future developments could include refining the approach to encourage deeper reflection, and generalization of the approach to different industry sectors or sustainability frameworks.

The hyper-competitive nature of e-business has raised the need for a generic way to appraise the merit of a developed business strategy. Although progress has been made in the domain of strategy evaluation, the established literature differs over the ‘tests’ that a strategy must pass to be considered well-constructed. This paper therefore investigates the existing strategy-evaluation literature to propose a more integrated and comprehensive normative strategic assessment that can be used to evaluate and refine a business’ s competitive strategy , adding to its robustness and survivability.

, We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems, urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologists are increasingly finding fault with traditional self-nonself thinking and a new ‘Danger Theory’ (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of ‘grounding’ the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we to defend this controversial theory per se, although a deliverable project to the body of knowledge in this area. we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems. Abstract We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new ‘Danger Theory’ (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of ‘grounding’ the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.

Supported by concurrent engineering, three dimensional concurrent engineering (3DCE) is a simple yet powerful model of new product development (NPD) in which the traditional focus on an appropriate match between product and process is augmented by an additional consideration of supply chain configuration. This paper presents the results from an in-depth study of 3DCE theory, explores its impact on the integration of environmental considerations into the NPD process and maps its benefits onto environmental NPD.

The Dendritic Cell Algorithm (DCA) is inspired by the function of the dendritic cells of the human immune system. In nature, dendritic cells are the intrusion detection agents of the human body, policing the tissue and organs for potential invaders in the form of pathogens. In this research, and abstract model of DC behaviour is developed and subsequently used to form an algorithm, the DCA. The abstraction process was facilitated through close collaboration with laboratory-based immunologists, who performed bespoke experiments, the results of which are used as an integral part of this algorithm. The DCA is a population based algorithm, with each agent in the system represented as an 'artificial DC'. Each DC has the ability to combine multiple data streams and can add context to data suspected as anomalous. In this chapter the abstraction process and details of the resultant algorithm are given. The algorithm is applied to numerous intrusion detection problems in computer security including the detection of port scans and botnets, where it has produced impressive results with relatively low rates of false positives.

Sustainability is a theme of increasing global prominence. The ICT sector is responding in two important ways. The first is to reduce its own carbon footprint, estimated at 2% of global emissions. The second is to concentrate on the rest of the economy (the 98%) and to produce innovative products and services that have a positive sustainability impact. In this position paper I show that various sustainability perspectives allow us to embed the paradigm across the whole computer science curriculum, and to use this work to deepen links with other disciplines. We should avoid getting drawn into climate science debates that will detract from the value Sustainable IT can add to our students' education. Nevertheless, sustainability offers an opportunity for us to engage the next generation of technologists and policy makers and equip them with the skills they will need to compete in the low carbon economy.

Sustainability is becoming an increasingly important driver for which decision makers -- consumers, corporate and government -- rely on principled, accurate and provenanced metrics to make appropriate behavior changes. Our assertion here is that a Sustainability Hub which manages such metrics together with their context and chains of reasoning will be of great benefit to the global community. In this paper we explain the Hub vision and explain its triple value proposition of context, chains of reasoning and community. We propose a data model and describe our existing prototype.

A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant, personalized, and context-sensitive information delivery. Yet traditional approaches to user profiles rely on explicit, brittle models that go out of date very quickly, lack relevance, and have few natural connections to related models. In this chapter the authors show how it is possible to create adaptive user profiles without any explicit input at all. Rather, leveraging implicit behaviour on social information networks, the authors can create profiles that are both adaptive and socially connective. Such profiles can help provide personalized access to enterprise resources and help identify other people with related interests.

Over the last decade, a new idea challenging the classical self-non-self viewpoint has become popular amongst immunologists. It is called the Danger Theory. In this conceptual paper, we look at this theory from the perspective of Artificial Immune System practitioners. An overview of the Danger Theory is presented with particular emphasis on analogies in the Artificial Immune Systems world. A number of potential application areas are then used to provide a framing for a critical assessment of the concept, and its relevance for Artificial Immune Systems.

It has previously been shown that a recommender based on immune system idiotypic principles can out perform one based on correlation alone. This paper reports the results of work in progress, where we undertake some investigations into the nature of this beneficial effect. The initial findings are that the immune system recommender tends to produce different neighbourhoods, and that the superior performance of this recommender is due partly to the different neighbourhoods, and partly to the way that the idiotypic effect is used to weight each neighbours recommendations.