The relaxation of international boundaries has enabled the globalisation of markets making available an ever increasing number of specialised suppliers and markets. Inevitably this results in supply chains sharing suppliers and customers reflected in a network of relationships. Within this context firms buyers configure their supply relationships based on their perception of supply risk. Risk is managed by either increasing trust or commitment or by increasing the number of suppliers. Increasing trust and commitment facilitates collaboration and reduces the propensity for a supplier to exit the relationship. Conversely, increasing the number of suppliers reduces dependency and increases the ease of making alternative supply arrangements. The emergent network of relationships is dynamic and complex, and due in no small part to the influence of inventory management practices, tightly coupled. This critical organization of the network describes a system that contrary to existing supply chain conceptualisation exists far from equilibrium, requiring a different more appropriate theoretical lens through which to view them. This thesis adopts a Complex Adaptive Systems (CAS) perspective to position supply networks as tightly coupled complex systems which according to Normal Accident Theory (NAT) are vulnerable to disruptions as a consequence of normal operations. The consequential boundless and emergent nature of supply networks makes them difficult to research using traditional empirical methods, instead this research builds a generalised supply network agent based computer model, allowing network constituents (agents) to take autonomous parallel action reflecting the true emergent nature of supply networks. This thesis uses the results from a series of carefully designed computer experiments to elucidate how supply networks respond to a variety of market structures and permitted agent behaviours. Market structures define the vertical (between tier) and horizontal (within tier) levels of price differentiation. Within each structure agents are permitted to autonomously modify their prices (constrained by market structure) and collaborate by sharing demand information. By examining how supply networks respond to different permitted agent behaviours in a range of market structures this thesis makes 4 contributions. Firstly, it extends NAT by incorporating the adaptive nature of supply network constituents. Secondly it extends supply chain management by specifying supply networks as dynamic not static phenomena. Thirdly it extends supply chain risk management through developing an understanding of the impact different permitted behaviour combinations on the networks vulnerability to disruptions in the context of normal operations. Finally by developing the understanding how normal operations impact a supply networks vulnerability to disruptions it informs the practice of supply chain risk management.

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Doctor of Philosophy (PhD) / The aim of this thesis is to understand what drives the evolution of industrial networks and how such understanding can be used to stimulate sustainable development. A complex adaptive systems perspective has been adopted to analyse the complex interaction between organisational behaviour and industrial network evolution. This analysis has formed the basis for the development of a modelling approach that allows for quantitative exploration of how different organisational perceptions about current and future uncertainty affect their behaviour and therefore the network evolution. This analysis results in a set of potential evolutionary pathways for an industrial network and their associated performance in terms of sustainable development. Subsequently, this modelling approach has been used to explore the consequences of interventions in the network evolution and to identify robust interventions for stimulating sustainable development of industrial networks. The analysis, modelling approach and development of interventions has been developed in the context of a bioenergy network in the region of KwaZulu-Natal in South Africa. Industrial networks are an important aspect of today’s life and provide many goods and services to households and individuals all over the world. They consist of a large number of autonomous organisations, where some organisations contribute by transforming or transacting natural resources, such as oil, agricultural products or water, while other organisations contribute to networks by providing information or setting regulation or subsidies (local or national governments) or by influencing decision making processes of other organisations in networks (advocacy groups). Throughout the process from natural resource to product or service, industrial networks have important economic, environmental and social impacts on the socio-economic and biophysical systems in which they operate. The sum of complex interactions between organisations affects the rate in which natural resources are used, environmental impacts associated with transformation and transaction of resources and social impacts on local communities, regions or countries as a whole. The aim of this thesis is to understand how industrial networks evolve and how they can be stimulated towards sustainable development. The first question that has been addressed in this thesis is how to understand the complex interaction between organisational behaviour and industrial network evolution. Organisational behaviour is affected by many functional and implicit characteristics within the environment in which the organisation operates, while simultaneously the environment is a function of non-linear relationships between individual organisational actions and their consequences for both the function and structure of the network. This thesis has identified four different characteristics of industrial networks that affect organisational behaviour: 1) Functional characteristics 2) Implicit behavioural characteristics 3) Implicit relational characteristics 4) Implicit network characteristics. Functional characteristics are those characteristics that are formally recognised by all organisations within an industrial network and which affect their position within the network. Examples of functional characteristics are the price and quantity of resources available, the location and distance of organisations within a network, infrastructure availability or regulation. Implicit characteristics, on the other hand, are those characteristics that impact the decision making process of organisations, but which are not formally part of the network. From an organisational perspective, implicit characteristics are the rules, heuristics, norms and values that an organisation uses to determine its objectives, position and potential actions. Implicit relational characteristics, most importantly trust and loyalty, affect an organisations choice between potential partners and implicit network characteristics are those social norms and values that emerge through social embeddedness. Collectively, these functional and implicit characteristics and their interactions determine the outcome of organisational decisions and therefore the direction of the industrial network evolution. The complex interaction between these large numbers of characteristics requires quantitative models to explore how different network characteristics and different interactions result in different network evolutions. This thesis has developed an agent-based simulation model to explore industrial network evolutions. To represent the multi-scale complexity of industrial networks, the model consists of four scales. Each scale represents different processes that connect the functional and implicit characteristics of an industrial network to each other. The two basic scales represent the strategic actions of the organisations on the one hand and the industrial network function and structure on the other. The third scale represents the processes that take place within the mental models of organisations describing how they make sense of their environment and inform their strategic decision making process. The fourth scale represents the social embeddedness of organisations and how social processes create and destroy social institutions. The model has been developed such that it allows for exploring how changes in different network characteristics or processes affect the evolution of the network as a whole. The second question that has been addressed in this thesis is how to evaluate sustainable development of different evolutionary pathways of industrial networks. First of all, a systems approach has been adopted to explore the consequences of an industrial network to the larger socio-economic and biophysical system in which the network operates. Subsequently, a set of structural indicators has been proposed to evaluate the dynamic performance of industrial networks. These four structural indicators reflect the efficiency, effectiveness, resilience and adaptiveness of industrial networks. Efficiency and effectiveness relate to the operational features by which industrial networks provides a particular contribution to society. Resilience and adaptiveness relate to the system’s capacity to maintain or adapt its contribution to society while under stress of temporary shocks or permanent shifts, respectively. Finally, different multi-criteria decision analysis (MCDA) tools have been applied to provide a holistic evaluation of sustainable development of industrial networks. The third important question that is addressed in this thesis is how to systematically explore the potential evolutionary pathways of an industrial network, which has led to the development of agent-based scenario analysis. Agent-based scenario analysis systematically explores how industrial network evolutions might evolve depending on the perceptions of organisations towards the inherent uncertainty associated with strategic decision making in networks. The agent-based scenario analysis consists of two steps. Firstly, analysts develop a set of coherent context scenarios, which represents their view on the context in which an industrial network will operate within the future. For a bioenergy network, for example, this step results in a set of scenarios that each represent a coherent future of the socio-economic system in which the network might evolve. The second step is the development of a set of ‘agent scenarios’. Each agent-based scenario is based on a different ‘mental model’ employed by organisations within the network about how to deal with the inherent ambiguity of the future. The organisational perspective towards uncertainty is of major importance for the evolution of industrial networks, because it determines the innovative behaviour of organisations, the structure of the network and the direction in which the network evolves. One the one hand, organisations can ignore future ambiguity and base their actions on the environment that they can observe in their present state. On the other extreme, organisations can adopt a view that the future is inherently uncertain and in which they view social norms and values more important than functional characteristics to make sense of their environment. The mental models are differentiated according to two dimensions: 1) different mental representation of the world and 2) different cognitive processes that can be employed to inform strategic actions. Along these dimensions, different processes can be employed to make sense of the environment and to inform decision making. The thesis has shown that by systematically exploring the different perceptions possible, an adequate understanding of the different evolutionary pathways can be gained to inform the evaluation and development of interventions to stimulate sustainable development. The final part of this thesis has applied the analysis and methodology developed throughout this thesis to a bioenergy network in the province of Kwazulu-Natal in South Africa. The bioenergy network consists of a set of existing sugar mills with large quantities of bagasse, a biomass waste product, available. Bagasse is currently burned inefficiently to produce steam for the sugar mills, but can potentially be used for the production of green electricity, biodiesel, bioethanol or gelfuel. All of these products have important consequences for the region in terms of associated reductions in CO2 emissions, electrification of and/or energy provision for rural households and local economic development of the region. This thesis has modelled strategic decisions of the sugar mills, the existing electricity generator, potential independent energy producers, local and national governments and how their actions and interactions can lead to different evolutionary pathways of the bioenergy network. The agent-based scenario analysis has been used to explore how different perceptions of organisations can lead to different network evolutions. Finally, the model has been used to explore the consequences of two categories of interventions on stimulating sustainable development. The conclusions are that both categories of interventions, financial interventions by national government and the introduction of multi-criteria decision analysis (MCDA) tools to aid strategic decision making, can have both positive and negative effects on the network evolutions, depending on what ‘mental models’ are employed by organisations. Furthermore, there is no single intervention that outperforms the others in terms of stimulating both functional and structural features of sustainable development. The final conclusion is that instead of focusing on individual or collective targets, emphasis should be placed on the development of interventions that focus on evolutionary aspects of industrial networks rather than functional performance criteria. This thesis has also highlighted interesting research questions for future investigation. The methodology developed in this thesis is applied to a single case study, but there are still many questions concerning how different industrial networks might benefit from different organisational perceptions towards uncertainty. Furthermore, the role between the mental models and sustainable development requires further investigation, especially in the light of globalisation and the interconnectiveness of industrial networks in different countries and continents. Finally, this methodology has provided a platform for investigating how new technologies might be developed that anticipate needs of future generations. This thesis has provided a first and important step in developing a methodology that addresses the complex issues associated with sustainable development, benefiting both academics and practitioners that aim to stimulate sustainable development.

ENGLISH ABSTRACT: It is very seldom that a decision-making problem concerns only a single value or objective. The process of simultaneously optimising two or more con icting objectives is known as multi-objective optimisation (MOO). A number of metaheuristics have been successfully adapted for MOO. The aim of this study was to investigate the feasibility of applying an agent-based modelling approach to MOO. The (s; S) inventory problem was chosen as the application eld for this approach and Anylogic used as model platform. Agents in the model were responsible for inventory and sales management, and had to negotiate with each other in order to nd optimal reorder strategies. The introduction of concepts such as agent satisfaction indexes, aggression factors, and recollection ability guided the negotiation process between the agents. The results revealed that the agents had the ability to nd good strategies. The Pareto front generated from their proposed strategies was a good approximation to the known front. The approach was also successfully applied to a recognised MOO test problem proving that it has the potential to solve a variety of MOO problems. Future research could focus on further developing this approach for more practical applications such as complex supply chain systems, nancial models, risk analysis and economics. / AFRIKAANSE OPSOMMING: Daar is weinig besluitnemingsprobleme waar slegs 'n enkele waarde of doelwit ter sprake is. Die proses waar twee of meer doelwitte, wat in konflik staan met mekaar, gelyktydig optimiseer word, staan bekend as multi-doelwit optimisering (MOO). 'n Aantal metaheuristieke is al suksesvol aangepas vir MOO. Die doelwit van hierdie studie was om ondersoek in te stel na die lewensvatbaarheid van die toepassing van 'n agent gebasseerde modelerings benadering tot MOO. As toepassingsveld vir hierdie benadering was die (s; S) voorraad probleem gekies en Anylogic was gebruik as model platform. In die model was agente verantwoordelik vir voorraad- en verkope bestuur. Hulle moes onderling met mekaar onderhandel om die optimale bestelling strategiee te verkry. Konsepte soos agentbevrediging, aggressie faktore en herinneringsvermoens is ingestel om die onderhandeling tussen die agente te bewerkstellig. Die resultate het gewys dat die agente oor die vermoe beskik om met goeie strategiee vorendag te kom. Die Pareto fronte wat gegenereer is deur hul voorgestelde strategiee was 'n goeie benadering tot die bekende front. Die benadering was ook suksesvol toegepas op 'n erkende MOO toets-probleem wat bewys het dat dit oor die potensiaal beskik om 'n verskeidenheid van MOO probleme op te los. Toekomstige navorsing kan daarop fokus om hierdie benadering verder te ontwikkel vir meer praktiese toepassings soos komplekse voorsieningskettingstelsels, finnansiele modelle, risiko-analises en ekonomie.

abstract: This dissertation examines the various factors and processes that have been proposed as explanations for the spread of agriculture in the west Mediterranean. The expansion of the Neolithic in the west Mediterranean (the Impresso-Cardial Neolithic) is characterized by a rapid spread of agricultural subsistence and material culture from the southern portion of the Italian peninsula to the western coast of the Iberian peninsula. To address this unique case, four conceptual models of Neolithic spread have been proposed: the Wave of Advance, the Capillary Spread Model, the Maritime Pioneer Colonization Model and the Dual Model. An agent-based model, the Cardial Spread Model, was built to simulate each conceptual spread model in a spatially explicit environment for comparison with evidence from the archaeological record. Chronological information detailing the arrival of the Neolithic was used to create a map of the initial arrival of the Neolithic (a chronosurface) throughout the study area. The results of each conceptual spread model were then compared to the chronosurface in order to evaluate the relative performance of each conceptual model of spread. These experiments suggest that both the Dual and Maritime Pioneer Colonization models best fit the available chronological and spatial distribution of the Impresso-Cardial Neolithic. For the purpose of informing agent movement and improving the fit of the conceptual spread models, a variety of paleoenvironmental maps were tested within the Cardial Spread Model. The outcome of these experiments suggests that topographic slope was an important factor in settlement location and that rivers were important vectors of transportation for early Neolithic migration. This research demonstrates the application of techniques rare to archaeological analysis, agent-based modeling and the inclusion of paleoenvironmental information, and provides a valuable tool that future researchers can utilize to further evaluate and fabricate new models of Neolithic expansion. / Dissertation/Thesis / Doctoral Dissertation Anthropology 2016

Web-based service providers have long been required to deliver high quality services in accordance with standards and customer requirements. Increasingly, however, providers are required to think beyond service quality and develop a deeper understanding of their customers’ Quality of Experience (QoE). Whilst models exist that assess the QoE of Web Application, significant challenges remain in defining QoE factors from a Web engineering perspective, as well as mapping between so called ‘objective’ and ‘subjective’ factors of relevance. Specifically, the following challenges are considered as general fundamental problems for assessing QoE: (1) Quantifying the relationship between QoE factors; (2) predicting QoE as well as dealing with the limited data available in relation to subjective factors; (3) optimising and controlling QoE; and (4) perceiving QoE. In response, this research presents a novel model, called QoEWA (and associated software instantiation) that integrates factors through Key Performance Indicators (KPIs) and Key Quality Indicators (KQIs). The mapping is incorporated into a correlation model that assesses QoE, in particular, that of Web Application, with a consideration of defining the factors in terms of quality requirements derived from web architecture. The data resulting from the mapping is used as input for the proposed model to develop artefacts that: quantify, predict, optimise and perceive QoE. The development of QoEWA is framed and guided by Design Science Research (DSR) approach, with the purpose of enabling providers to make more informed decisions regarding QoE and/or to optimise resources accordingly. The evaluation of the designed artefacts is based on a build-and-evaluate cycle that provides feedback and a better understanding of the utilised solutions. The key artefacts are developed and evaluated through four iterations: Iteration 1 utilises the Actual Versus-Target approach to quantify QoE, and applies statistical analysis to evaluate the outputs. Iteration 2: utilises a Machine Learning (ML) approach to predict QoE, and applies statistical tests to compare the performance of ML algorithms. Iteration 3 utilises the Multi-Objective Optimisation (MOO) approach to optimise QoE and control the balance between resources and user experience. Iteration 4 utilises the Agent-Based Modelling approach to perceive and gain insights into QoE. The design of iteration 4 is rigorously tested using verified and validated models.

Deforestation and land use change are widespread in western Uganda. However, the spatial patterns and time-series of change and the reasons why it is occurring remain to be fully investigated. In this work a combination of satellite imagery and social surveys is used to quantify forest gains and loss over the last three decades in the region close to Lake Albert, whilst also providing an account of possible drivers of change. This area proves to be interesting as it covers regions with both formally protected areas (gazetted regions) and un-protected forest, the latter being largely under private ownership. Remote sensing data from the Landsat satellites were gathered for forest change detection, and were processed using standard remote sensing techniques, then quantified using GIS and regression methods. Fieldwork allowed these data to be ground truthed while gathering (quantitative) household surveys and (qualitative) key informant interviews. Quantitative surveys were analysed using Principal Components Analysis (PCA) and cluster analysis, and were compared qualitatively with the satellite analysis and stakeholder interviews. The results show that forest cover declined significantly outside gazetted areas at the expense of varying local?scale processes, although the protection of the gazetted forests was remarkably successful. In forest corridors outside gazetted regions, losses exceeded 90% (p<0.05). Survey data suggest that rural poor households were more likely to be situated in forested regions, and were more dependent on forest resources for their livelihoods. However, the drivers of change were spatially variable, with expansion of sugarcane farming being a likely driver in the northern areas, but small?scale agricultural expansion a significant factor in the more southern parts of the study region. While there is wide agreement within the data that the patterns of forest cover and land use changes are anthropogenically driven, more specific drivers are swamped by intricacies of the bio-physical and socio-economic preconditions that are inseparable in both space and time, although agricultural expansion and population growth were evident and pervasive. The analyses provide insights into complex anthropogenic processes at various spatial scales, and policy recommendations provided are widely applicable for developing countries struggling to conserve nature whilst boosting economic growth.

Introduction: Tuberculosis (TB) is an infectious disease that causes significant morbidity and mortality. Despite the fact that the total burden of TB has decreased dramatically, the distribution of that burden across the Canadian population has not changed. A century ago, the Indigenous population of Canada had a significantly higher TB mortality than the non-Indigenous population. This gap still exists today. TB is a disease of poverty, and understanding the role of the social determinants of health (SDH) may provide insights into the causes of persistence of TB in the Indigenous population. Research questions: This thesis tackles three questions: 1) Can a TB outbreak that took place over a century ago be reconstructed? 2) What can we learn about the relationship between the disease, the population it afflicted, and the environment in which the outbreak took place? 3) How can reconstruction of a TB outbreak be used to evaluate policy interventions? Area studied: Analyses were limited to the Qu’Appelle Agency, located in Southeastern Saskatchewan. Methodology: An agent-based model of socioeconomic environment of the Qu’Appelle Agency was developed to study the relationship between TB and SDH. Data on TB mortality, demographics, agricultural production, material circumstances, and economic factors of production were used to study the relationship between TB and SDH at the aggregate level. Results: 1) Extensive aggregate data analyses were carried out and an agent-based model of TB transmission and of the socioeconomic environment of the Qu’Appelle Agency was developed. 2) Results of these analyses identify a number of important parameters responsible for the high TB mortality in the Agency. These parameters include biological factors, housing, social characteristics, agricultural output, and policies of the Department of Indian Affairs. Conclusions: This research demonstrates that reconstruction of an outbreak of an infectious disease that took place over a century ago is a complex undertaking that hinges on availability of data and significant expertise in a variety of fields, such as health sciences, economics, mathematics, and modelling approaches. The further one goes into the past, the more one is forced to rely on assumptions, which make the reconstructed web of relationships between agent, host, and environment that caused the outbreak less certain. Despite the inherent uncertainty, the process of outbreak reconstruction provides a deep and multi-faceted understanding of the interactions among the agent, the host, and the environment. The resulting model is a useful way of studying policy interventions that could be applied in other contexts as well – to other infectious diseases or TB outbreaks on other reserves. Keywords: [population health, epidemiology, tuberculosis, Indigenous peoples, agent-based modelling, social determinants of health]

The introduction of a liberalised electricity market has brought a new challenge to generating companies as well as system regulators. Under this more competitive environment, generating companies are exposed to various risks that might compromise their investment return. Moreover, the various risks in the market affect each type of generation technology in a different way; hence influence the technology choice. Furthermore, it is not yet clear whether the investment cycles in a liberalised electricity market will take place in an orderly fashion or whether 'boom and bust' cycles may arise. As a consequence various market designs, investment incentives and policies have been implemented by system regulators to try to ensure the security of supply. Investment decisions under a market with incentive mechanism are even more complicated to model because the generating company needs to forecast the revenue that the new investment will make from both the energy market and the mechanism. This thesis develops some models that could be used by system regulators to study the performance of market designs and by generating companies to assess a new investment under a liberalised electricity market. Three main models have been developed to serve these purposes. A generation expansion model has been developed using Agent-based modelling approach. In this model each generating company makes investment decision taking into account their competitors' investment strategies and the interactions between them. Several incentive mechanisms are also modelled to study their impacts on the generating companies' investment decision and the dynamic of the investments. A more comprehensive investment framework for a generating company to evaluate an investment in a new power plant has also been developed. The framework consists of two stages: 1) it first models the expected future investments and retirements from all the companies in the market and 2) then calculates the market prices and revenues of the new investment against the future system expansion obtained in the first stage. Two investment models have been developed using this framework. The first model is a probabilistic valuation model to assess investment considering risks and uncertainties. The second model is developed to evaluate investment in an oligopoly electricity market taking into account various risk characteristics of different technologies. The investment framework for a generating company to evaluate an investment is also extended so that the generating company can evaluate investments in a market with an incentive mechanism.

Why do states express support for norms that go against their underlying beliefs? Scholars of policy diffusion have identified four social mechanisms -- coercion, competition, emulation, and learning -- that can lead to the spread of a common practice, a norm, in the international system. I build a formal model of the four mechanisms and apply them to actual cases of norm diffusion. The formal models are anchored by three variables that capture fundamental aspects of international society: hierarchy, neighborhood, and identity. The four different diffusion mechanisms operate on these variables, creating distinct over-time trajectories. Three important dynamic patterns are compared across different model specifications: the shape of the adoption S-curve, the power distribution among expressers and non-expressers, and the degree of regional clustering. I find that the four mechanisms produce unique signatures under many conditions, but that changes to some parameters such as initial number of expressers can obscure the identification of the diffusion mechanism. In the first empirical chapter, I apply the framework to the diffusion of quotas for women's representation. I find that quotas are adopted by weak states, and that the likely source of inspiration for quota adoption are other weak states in the same neighborhood. The empirical pattern in terms of hierarchy, neighborhood, and identity point to competition as the mechanism that drove quota diffusion. Because competition is associated with norm internalization, this finding suggests that the world is really becoming more gender equal. In the second empirical chapter, I change substantive focus to the diffusion of human rights norms. Adoption of human rights treaties seems to be associated with worse human rights behavior, but why do states that ratify human rights treaties so often fail to uphold their obligations?. I find that the Convention Against Torture (CAT) treaty is adopted first by strong states in Europe, then to weaker states in a regionally-contingent pattern. This empirical pattern is most consistent with the emulation mechanism. This implies that the anti-torture norm is not associated with internalization, and solves the previously puzzling ratification-compliance paradox.