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This thesis investigates the causes of the long wait-time for patients in Emergency department (ED) of Victoria General Hospital, and suggests changes for improvements. Two prominent simulation techniques have been used to replicate the ED in a simulation model. These are Discrete-event simulation (DES) and Agent-based modeling (ABM). While DES provides the basic modeling framework ABM has been used to incorporate human behaviour in the ED. The patient flow in the ED has been divided into 3 phases: input, throughput, and output. Model results show that there could be multiple interventions to reduce time taken to be seen by the doctor for the first time (also called WTBS) either in the output phase or in the input phase. The model is able to predict that a reduction in the output phase would cause reduction in the WTBS but it is not equipped to suggest how this reduction can be achieved. To reduce WTBS by making interventions in the input phase this research proposes a strategy called fast-track treatment (FTT). This strategy helps the model to dynamically re-allocate resources if needed to alleviate high WTBS. Results show that FTT can reduce WTBS times by up-to 40%.

This dissertation investigates selected empirical and theoretical aspects of land-use and land-cover change (LUCC) in exurban areas. Two challenges – observation and monitoring of LUCC, and spatially explicit modeling, are addressed using three main approaches – measuring, reviewing and agent-based modeling (ABM). All of these approaches focus on LUCC at the individual household level, investigating how micro-scale elements interact to influence macro-scale functional patterns—bottom-up analysis. First, the temporal change of the quantity and pattern of land-cover types within exurban residential parcels in three townships in the southeastern Michigan is examined using landscape metrics and local indicators of spatial association at the parcel and parcel-neighborhood level respectively. The results demonstrate that the number and area of exurban residential parcels increased steadily from 1960 to 2000, and different land-cover types have distinctive temporal changes over time. The results also indicate that there is a convergence process at the neighborhood level through which the quantity and pattern of land cover in parcels conform with the neighborhood appearance. Second, 51 urban residential choice models based on ABM are reviewed. The results divide these models into three categories (i.e. models based on classical theories, models focusing on different stages of urbanization process; and integrated ABM and microsimulation models). This review also compares the differences among these models in their representations of three essential features brought by the technique of ABM: agent heterogeneity, the land market and output measurement. Challenges in incorporating these features, such as the trade-off between the simplicity and abstraction of model and the complexity of urban residential system, interactions of multiple features and demands for data at individual level, are also discussed. Third, the effects of agent heterogeneity on spatial and socioeconomic outcomes under different levels of land-market representations are explored through three experiments using a stylized agent-based land-market model. The results reveal that budget heterogeneity has prominent effects on socioeconomic outcomes, while preference heterogeneity is highly pertinent to spatial outcomes. The relationship between agent heterogeneity and macro-measures becomes more complex as more land-market mechanisms are represented. The results also imply that land-market representation (e.g., competitive bidding) is indispensable to reproduce the results of classical urban land market models (e.g., monocentric city model) in a spatial ABM when agents are heterogeneous.

Truck transportation is considered as a favourable mode by shippers to carry freight at most ranges of distance as it has more flexibility in fleet size, capacity, scheduling, routing, and access. Although truck is considered as the popular mode for freight transportation, road-rail intermodal freight transportation becomes an attractive alternative to road only mode since the latter has no longer assured a reliable service due to traffic congestion problem. It also raises public concern in environmental and road safety impacts. Intermodal freight transportation is defined as a system that carries freight from origin to destination using two or more transportation modes where transfers between modes occur at an intermodal freight terminal. Success of the terminal depends on four major factors, namely: location, efficiency, financial sustainability, and rail level of service. Among these, the location is one of the most crucial success factors and needs to be considered carefully as it has direct and indirect impacts on a number of stakeholders including terminal users, terminal operators, transport network infrastructure providers, and community. Limitations of previous terminal location evaluation models in representing individual preference and behaviour as well as accommodating negotiation and communication between the players bring in an opportunity to develop a new model which is more flexible and capable of providing a solution that is not necessary to be optimal, but acceptable for every player without requiring explicit trade-offs. This thesis is aimed at demonstrating the feasibility of applying an agent-based approach to the evaluation of intermodal freight terminal location and investigating terminal effectiveness against stakeholder equity and some important aspects arising from the different stakeholders’ viewpoints. Agent technologies were introduced to model the stakeholders as individual agents. The agent concept was adopted to develop a decentralised location evaluation system that is able to balance the terminal effectiveness with the stakeholder equity. The proposed agent-based location evaluation model was modelled as a hierarchical control system that comprises three decision levels: local level, stakeholder level and policy level. Policy level is the highest decision level, which is represented by a policy maker. Apart from the policy level, the rest can be viewed as operational decision levels. Local level is the lowest control level. At this level, each stakeholder was classified into stakeholder groups based on their characteristics and interest. The terminal scenarios were then evaluated based on benefit maximisation criteria. Stakeholder control is the higher control level than the local level. It represents the control level where negotiations and decisions between groups of people (stakeholders) with different point of views are made. At this level, negotiation process was used to determine terminal location based on preference and equity of stakeholders. The determined terminal site was then used in the evaluation against constraints to ensure that all agents are satisfied. The terminal location decision for South East Queensland (SEQ) was applied as a case study of this thesis. The SEQ strategic freight transport model was developed, calibrated, and validated to assist in providing inputs for the evaluation of terminal location. The results indicated that for the developed agent-based location evaluation model, Yatala was selected as the most appropriate terminal location that results in the highest effectiveness and equity (as measured by level of satisfaction and Gini coefficient, respectively). Other location evaluation models were also used in comparison with the developed agent-based location evaluation model. Those include P-Median, P-Centre, and maximum covering models. It was found that the agent-based location evaluation model outperformed the other location evaluation models. Finally, a sensitivity analysis was conducted in order to evaluate the consistency of model outputs against the uncertainties in the input parameters. In most cases, the terminal location decisions obtained from the developed agent-based location evaluation model was not sensitive to the changes in those parameters. However, the results suggested that when a unit cost of truck travel delay increased, the impact on the final terminal location decisions was observed. This thesis demonstrated the feasibility of applying a decentralised approach to terminal location decision problem using a multi-agent concept and evaluating it against other well-known location problems. A new framework and methodology for the planning of intermodal terminal location evaluation was also formulated. Finally, the problems of terminal location evaluation and optimisation of intermodal freight terminal operation were integrated into a single evaluation model.

In a peer-to-peer shared ride system, transportation clients with traffic demand negotiate with transportation hosts offering shared ride services for ad-hoc ridesharing in a continuously changing environment, using wireless geosensor networks. Due to the distinctive characteristic of this system—a complex and non-deterministic transportation network, and a local peer-to-peer communication strategy—clients will always have limited transportation knowledge, both from a spatial and a temporal perspective. Clients hear only from nearby hosts, and they do not know the future availability of current or new hosts. Clients can plan optimal trips prior to departure according to their current knowledge, but it is unlikely that these trips will be final optimal trip due to continuously changing traffic conditions. Therefore, it is necessary to evaluate the trip quality in this dynamic environment in order to assess different communication and wayfinding strategies. (For complete abstract open document)

This thesis aims to develop an alternative expectations model to the Rational Expectations Hypothesis (REH) and adaptive-expectations models, which provides more accurate temporal predictive performance and more closely reflects recent advances in behavioural economics, the ‘science of complexity’ and network dynamics. The model the thesis develops is called Adaptive Interactive Expectations (AIE), a subjective dynamic model of the process of expectations formation. To REH, the AIE model provides both an alternative and a complement. AIE and REH complement one another in that they are diametrically opposite in the following five dimensions, agent intelligence, agent interaction, agent homogeneity, equilibrium assumptions and the rationalisation process. REH and AIE stress the importance of hyper-intelligent agents interacting only via a price signal and near zero-intelligent agents interacting via a network structure, respectively. The complementary nature of AIE and REH provide dual perspectives that enhance analysis. The Dun & Bradstreet (D&B 2008) profit expectations survey is used in the thesis to calibrate AIE and make predictions. The predictive power of the AIE and REH models is compared. The thesis introduces the ‘pressure to change profit expectations index’, px. This index provides the ability to model unknowns within an adaptive dynamic process and combine the beliefs from interactive-expectations, adaptive-expectations and biases that include pessimism, optimism and ambivalence. AIE uses networks to model the flow of interactive-expectations between firms. To overcome the uncertainty over the structure of the interactive network, the thesis uses model-averaging over 121 network topologies. These networks are defined by three variables regardless of their complexity. Unfortunately, the Bayesian technique’s use of the number of variables as a measure of complexity makes it unsuitable for model-averaging over the network topologies. To overcome this limitation in the Bayesian technique, the thesis introduces two model-averaging techniques, ‘runtime-weighted’ and ‘optimal-calibration’. These model-averaging techniques are benchmarked against ‘Bayes-factor model-averaging’ and ‘equal-weighted model-averaging’. In addition to the aggregate called all–firms, the D&B (2008) survey has four divisions, manufacturing durables, manufacturing non–durables, wholesale and retail. To make use of the four divisions, the thesis introduces a ‘link-intensity matrix’ based upon an ‘input-output table’ to improve the calibration of the networks. The transpose of the table is also used in the thesis. The two ‘link-intensity matrices’ are benchmarked against the default, a ‘matrix of ones’. The aggregated and disaggregated versions of AIE are benchmarked against adaptive-expectations to establish whether the interactive-expectations component of AIE add value to the model. The thesis finds that AIE has more predictive power than REH. ‘Optimal-calibration model-averaging’ improves the predictive performance of the better-fitting versions of AIE, which are those versions that use the ‘input-output table’ and ‘matrix of ones’ link-intensity matrices. The ‘runtime-weighted model-averaging’ improves the predictive performance of only the ‘input-output table’ version of AIE. The interactive component of the AIE model improves the predictive performance of all versions of the AIE over adaptive-expectations. There is an ambiguous effect on prediction performance from introducing the ‘input-output table’. However, there is a clear reduction in the predictive performance from introducing its transpose. AIE can inform the debate on government intervention by providing an Agent-Based Model (ABM) perspective on the conflicting mathematical and narrative views proposed by the Greenwald–Stiglitz Theorem and Austrian school, respectively. Additionally, AIE can provide a complementary role to REH, which is descriptive/predictive and normative, respectively. The AIE network calibration uses an ‘input-output table’ to determine the link-intensity; this method could provide Computable General Equilibrium (CGE) and Dynamic Stochastic General Equilibrium (DSGE) with a way to improve their transmission mechanism. Furthermore, the AIE network calibration and prediction methodology may help overcome the validation concerns of practitioners when they implement ABM.

This thesis aims to develop an alternative expectations model to the Rational Expectations Hypothesis (REH) and adaptive-expectations models, which provides more accurate temporal predictive performance and more closely reflects recent advances in behavioural economics, the ‘science of complexity’ and network dynamics. The model the thesis develops is called Adaptive Interactive Expectations (AIE), a subjective dynamic model of the process of expectations formation. To REH, the AIE model provides both an alternative and a complement. AIE and REH complement one another in that they are diametrically opposite in the following five dimensions, agent intelligence, agent interaction, agent homogeneity, equilibrium assumptions and the rationalisation process. REH and AIE stress the importance of hyper-intelligent agents interacting only via a price signal and near zero-intelligent agents interacting via a network structure, respectively. The complementary nature of AIE and REH provide dual perspectives that enhance analysis. The Dun & Bradstreet (D&B 2008) profit expectations survey is used in the thesis to calibrate AIE and make predictions. The predictive power of the AIE and REH models is compared. The thesis introduces the ‘pressure to change profit expectations index’, px. This index provides the ability to model unknowns within an adaptive dynamic process and combine the beliefs from interactive-expectations, adaptive-expectations and biases that include pessimism, optimism and ambivalence. AIE uses networks to model the flow of interactive-expectations between firms. To overcome the uncertainty over the structure of the interactive network, the thesis uses model-averaging over 121 network topologies. These networks are defined by three variables regardless of their complexity. Unfortunately, the Bayesian technique’s use of the number of variables as a measure of complexity makes it unsuitable for model-averaging over the network topologies. To overcome this limitation in the Bayesian technique, the thesis introduces two model-averaging techniques, ‘runtime-weighted’ and ‘optimal-calibration’. These model-averaging techniques are benchmarked against ‘Bayes-factor model-averaging’ and ‘equal-weighted model-averaging’. In addition to the aggregate called all–firms, the D&B (2008) survey has four divisions, manufacturing durables, manufacturing non–durables, wholesale and retail. To make use of the four divisions, the thesis introduces a ‘link-intensity matrix’ based upon an ‘input-output table’ to improve the calibration of the networks. The transpose of the table is also used in the thesis. The two ‘link-intensity matrices’ are benchmarked against the default, a ‘matrix of ones’. The aggregated and disaggregated versions of AIE are benchmarked against adaptive-expectations to establish whether the interactive-expectations component of AIE add value to the model. The thesis finds that AIE has more predictive power than REH. ‘Optimal-calibration model-averaging’ improves the predictive performance of the better-fitting versions of AIE, which are those versions that use the ‘input-output table’ and ‘matrix of ones’ link-intensity matrices. The ‘runtime-weighted model-averaging’ improves the predictive performance of only the ‘input-output table’ version of AIE. The interactive component of the AIE model improves the predictive performance of all versions of the AIE over adaptive-expectations. There is an ambiguous effect on prediction performance from introducing the ‘input-output table’. However, there is a clear reduction in the predictive performance from introducing its transpose. AIE can inform the debate on government intervention by providing an Agent-Based Model (ABM) perspective on the conflicting mathematical and narrative views proposed by the Greenwald–Stiglitz Theorem and Austrian school, respectively. Additionally, AIE can provide a complementary role to REH, which is descriptive/predictive and normative, respectively. The AIE network calibration uses an ‘input-output table’ to determine the link-intensity; this method could provide Computable General Equilibrium (CGE) and Dynamic Stochastic General Equilibrium (DSGE) with a way to improve their transmission mechanism. Furthermore, the AIE network calibration and prediction methodology may help overcome the validation concerns of practitioners when they implement ABM.

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.

Univ., Diss.--Louvain-la-Neuve, 2007.

Eidgenössische Techn. Hochsch., Diss.--Zürich, 2007. / Zsfassung in dt. Sprache.