In this article, a performance assessment of the evacuation system is established for educational buildings. Structural and geotechnical information of the building is collected and introduced into a database. A similar procedure was realized for the information related to the occupants. Using this information, a) the structural fragility and localized collapse were determined and b) the interaction of the person with the partial collapse was established. For the first aspect, nonlinear time history was used, and for the second, the agent-based modeling was applied to recreate the reaction of people that face the micro collapse. Therefore, the important results of this evaluation are: 1) To localize collapsed beans and columns that make inoperable evacuation routes, 2) to localize bottleneck areas that people concentration during evacuation, and 3) quantification of affected people, in terms of persons caught up in the building that cannot evacuate.

Master thesis elaborates on agent-based modelling (ABM, computer simulation method) founded on the concept of analytical sociology and its use in empirical sociology. The use is demonstrated by creation of a model based on the principal of environmental sociology studying the influence of social factors on the environment. Thesis works with the empirical- theoretical concept New ecological paradigm (NEP) measuring the values and opinions on the environment. The origin of the paper was motivated by the absence of the projects combining the method of empirically calibrated agent-based modelling and sociological grounds, particularly in the Czech context, but also abroad. Based on the environmental module of Czech data ISSP 2010 and research question "How parameters of social network influence the willingness to sort waste?" model was created and analyzed. Relationship between both types of agents (sorting and not sorting waste during the whole simulation) and their neighbourhood was found. The higher the number of neighbours, the more agents with this particular type of behaviour. The likelihood of bond creation with long-distance agent at the expence of bond abolition with close neighbour was without any influence on the number of non/sorting agents. It seems the agents tend to replicate behaviour...

Municipalities increasingly have less financial resources to spend on implementation of transport strategies and plans. This situation is putting pressure on transport professionals to minimize wasteful expenditure on projects that do not deliver high transport service improvements. As such, the need for efficient, pragmatic decision making on policy direction, infrastructure expenditure, or any transport interventions is becoming very critical. Thus, transport professionals are increasingly in need of tools to help them predict with increased accuracy the outcomes of their intended transport interventions. The City of Cape Town has a Bus Rapid Transport system called MyCiTi. Current MyCiTi operations are incurring losses. The service is kept running on the back of subsidies from the federal government. There is a need for rationalization of the system. However, with strained resources, the interventions on the system have to guarantee improvements. Overemphasis on the ability of MyCiTi BRT service to support transportation during the 2010 soccer world cup event heavily influenced the design of the network. As a result, network appraisal is one area that can be done on the system to identify areas of improvement. In this thesis, decision making support will be demonstrated using a network design appraisal process for the MyCiTi BRT system in Cape Town. The existing MyCiTi network will undergo network improvement using heuristic node insertion technique leading to multiple network scenarios in a modeling environment. Agent-Based demand mobility behavior simulation will be used on each of the network scenarios to come up with network performance indicators. These network performance indicators will be used in the multi-criteria decision analysis (MCDA) model to come up with a ranking of the network scenarios and help in deciding on the optimum network improvement intervention. Overall, findings of this research show the importance of weighting of the performance indicators. Where networks that score well in the performance indicator with the high weights also rank high. In conclusion, the study has demonstrated the importance of decision making support in interventions on complex systems like bus systems. Recommendations on the possible avenues of research stemming from this thesis have also been outlined.

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.

Customer retention is an important issue for any business, especially in mature markets such as the UK mobile market where new customers can only be acquired from competitors. Different methods and techniques have been used to investigate customer retention including statistical methods and data mining. However, due to the increasing complexity of the mobile market, the effectiveness of these techniques is questionable. This study proposes Agent-Based Modelling and Simulation (ABMS) as a novel approach to investigate customer retention. ABMS is an emerging means of simulating behaviour and examining behavioural consequences. In outline, agents represent customers and agent relationships represent processes of agent interaction. This study follows the design science paradigm to build and evaluate a generic, reusable, agent-based (CubSim) model to examine the factors affecting customer retention based on data extracted from a UK mobile operator. Based on these data, two data mining models are built to gain a better understanding of the problem domain and to identify the main limitations of data mining. This is followed by two interrelated development cycles: (1) Build the CubSim model, starting with modelling customer interaction with the market, including interaction with the service provider and other competing operators in the market; and (2) Extend the CubSim model by incorporating interaction among customers. The key contribution of this study lies in using ABMS to identify and model the key factors that affect customer retention simultaneously and jointly. In this manner, the CubSim model is better suited to account for the dynamics of customer churn behaviour in the UK mobile market than all other existing models. Another important contribution of this study is that it provides an empirical, actionable insight on customer retention. In particular, and most interestingly, the experimental results show that applying a mixed customer retention strategy targeting both high value customers and customers with a large personal network outperforms the traditional customer retention strategies, which focuses only on the customer‘s value.

Agent-based models (ABM) are an increasingly important research tool for describing and predicting interactions among humans and their environment. A key challenge for such models is the ability to faithfully represent human decision making with respect to observed behaviour. This thesis aims to address this challenge by developing a methodology for empirical measurement and simulation of decision making in humanenvironment systems. The methodology employs the Beliefs-Desires-Intentions (BDI) model of human reasoning to directly translate empirically measured decision data into artificial agents, based on sound theoretical principles. A common simulated decision environment is used for both eliciting human decision making behaviour, and validating artificial agents. Using this approach facilitates the collection of decision making narratives by way of participatory simulation, and promotes a fair comparison of real and modelled decision making. The methodology is applied in two case studies: One to carry out a trial involving human subjects solving an abstract land-use problem, and another to examine the feasibility of up-scaling the methodology to a real agricultural scenario—dairy farming. Results from the experiments indicate that the BDI-based methodology achieved reasonably direct encoding of decision making behaviour from elicited human narratives. The main limitations found with the technique are: (1) the significant use of subjects’ time required to elicit their decision making behaviour; (2) the significant programming effort required; and (3) the challenge of aggregating behaviour from multiple subjects into a generalised decision making model. In spite of its limitations, BDI has shown its strengths as a tool for empirical analysis and simulation of decision making in research of human-environment systems.

Understanding and simulating the relationship between urban land-use configuration and patterns of human spatial interaction has been the subject of multi-disciplinary research. Conceptually, it is recognized that the location decisions of several urban actors including individuals, households, firms and public sector institutions, collectively determine the spatial distribution of land-use activities; the emergent land-use patterns, in turn, provide the structural conditions within which flows and interactions between locations occur daily and respond to each other over time. Over the past six decades, various theories and concepts from urban economics, social-physics, transportation studies, and the complexity sciences have underpinned empirical research and development of state-of-the-art simulation models to explore the land-use and travel nexus. Using a case study design and selecting the Kumasi Metropolis, a medium-size metropolis of nearly two-million inhabitants in Ghana, West Africa as the case study area, two main objectives, which reflect research trends and gaps in both the empirical literature and simulation model development have been addressed in this thesis. The first objective was to examine empirically, the location choice behaviour of households and individuals with respect to their residential and job locations, and the mobility patterns associated with the observed home-work location combinations within the metropolis. The second objective was to develop an integrated geospatial and agent-based model to simulate how the residential and job location choice behaviour of heterogeneous households and individuals co-emerge with mobility patterns in the metropolis. The empirical studies presented in this thesis contributes to a deeper understanding of how location-defining attributes at multiple spatial-scales interact with socio-demographic attributes of heterogeneous households and individuals to determine their residential location choice, job location choice and mobility characteristics. The development of the Metropolitan Location and Mobility Patterns Simulator (METLOMP-SIM)—an integrated geospatial and agent-based model also demonstrates how the encoded micro-scale behaviour of purposive households and individuals, interacting with each other and their environment dynamically, could reproduce macro-scale urban location patterns, property market price formation and evolution, and patterns and attributes of spatial flows and interactions anchored on the population’s residential-job location combinations.

At the Rio Conference in 1992, the sustainable development agenda promised a new era for natural resource management, where the wellbeing of human society would be enhanced through a more sustainable use of natural resources. Several decades on, economic growth continues unabated at the expense of natural capital – as evidenced by natural resource depletion, biodiversity loss, climate change and further environmental issues. Why is this happening and what can be done about it? This research examines what socio-economic and governance factors affect sustainability in complex coupled social-ecological systems. Furthermore, it analyses the role of power relations and imbalances between economic and conservation forces with regard to sustainable development. The original contribution to knowledge of this thesis is based on one conceptual and two empirical (Agent-Based) models. These explore, through several case-studies, the potential of different future scenarios in fostering synergies and win-win contexts of ecosystem services and socio-economic indicators. Overall, the research showed the complex and interconnected relationship between the economy and natural systems, and between economic and conservation forces, in coupled social-ecological systems. Addressing complex sustainability issues requires the use of integrative, holistic and interdisciplinary approaches, in addition to considering the particular socio-economic, cultural, political and environmental contexts of the social-ecological system being analysed. The models demonstrated that the current economic system requires an ever-increasing use of natural resources, and that the economy does not protect the natural capital on which it depends. This is based on a disjunction of the economic and conservation elements upon which the sustainable development paradigm is founded. Furthermore, several socio-economic and governance factors appeared to be key for diminishing sustainability in coupled social-ecological systems; namely, the type of economic and production systems, the particular use of monetary debt, technological development, and weak conservation forces (both top-down and bottom-up). However, results also showed alternative scenarios where these same factors could be redirected to enhance social-ecological sustainability. This dual role supports the argument that the current economic system is not inherently (i.e. by definition, per se) unsustainable. Rather, the specific use of economic mechanisms and behaviour of economic entities, as well as their decisions and relationships with the environment, show a tendency to increase unsustainability. Hence, short- and medium-term sustainability can be enhanced by developing mechanisms that start shifting capitalist forces to support environmental conservation; here, the role of Payments for Ecosystem Services will be essential. Enhancing long-term sustainability, however, may require a further paradigm change – where economic and production systems integrate, and fully account for, externalities and the value of natural capital, thus human society is embedded within the wider, and more important, natural environmental system.

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.

Urbanisation is driven by the complex interactions of many physical and human factors where human actions and decisions, individually and collectively, ultimately shape the patterns of urban landscapes. Agentbased modelling is an emerging technique in land use science that is designed to study multiple heterogeneous and locally interacting active entities within a system. An example of a local interaction is the request made by residents to planners for building permits. The decisions of planners in response to this request leads to emergent properties at an aggregate level such as city growth, assuming no equilibrium conditions. This thesis develops a framework for investigating in space and in time future residential land use change over a polycentric region using a case study of East Anglia, UK. Conceptually, the framework views the complexity of housing development in a system of cities (macrogeographical level) as the visible and concrete outcome of interactions between household demand for new dwellings (micro-geographical level) and the supply of building permits by local planners (meso-geographical level). Demand and supply are driven by household location preferences, as well as local planning, and evolve over time, leading to future land use change at speci c locations. The IPCC socio-economic scenarios are adapted to describe plausible evolutions in these preferences and strategies in order to evaluate di erent urban land use change pathways and the associated potential consequences for people (e.g. ooding risks) and the environment (e.g. biodiversity loss from land fragmentation). Simulation of new housing scenarios is undertaken within the agent-based modelling paradigm using a new computer programme developed in NetLogo. Issues of sensitivity analysis, validation, calibration and system complexity are addressed throughout the thesis. The thesis contributes to the eld of landscape and urban ecology by exploring urban complexity with a spatio-dynamic model of residential location behaviour driven by human and natural variables. As land use and land cover change is known to strongly a ect ecological landscape functions and processes, understanding the relationships between social and natural systems within changing landscapes helps to highlight hotspots of potential pressure and their e ects on the natural environment as part of an assessment of the possible ecological impacts of new urban development.