Land use and cover change (LUCC) is increasingly recognised as one of the most visible impacts of humans on nature. In rural areas, most of the observed LUCC is associated with agricultural activities. This has traditionally been attributed to the interplay of the socio-economic and political milieu, and the opportunities and constraints arising from the climatic conditions and physical attributes of land. Although there is no doubt that these factors influence farmer decisions, the mosaic of farming systems suggests that farmers do not always behave uniformly, even in areas with comparable socio-economic and environmental conditions. While the multi-facetted and varying nature of farmer decision-making is considered to be established knowledge in rural sociology, it is often neglected in LUCC models that typically describe it as homogeneous and rational in economic terms. This thesis presents an application of mixed-method social survey which aims at improving the representation of the diversity and complexity of farmer decision-making process in LUCC models. Different data collection methods (in-depth, semi-structured interviews, questionnaire) and analyses (thematic analysis, principal components analysis, cluster analysis, choice-based conjoint analysis) were used complementarily to identify the factors that facilitate or constrain farmer participation in environmental management practices (a), to identify the dominant farmer profiles (b) and to assess farmer preferences that influence land use decisions (c). Data collection was conducted in a study area located in the Canton of Aargau, Switzerland, where there is limited knowledge about farmer decision-making drivers and actions. Research findings were used to empirically inform an agent-based model that simulates farmer decisions. Paremeterised storylines were used to explore farmer decisions in alternative futures. An advanced and context-specific representation of human agents in modeling frameworks can make LUCC models valuable tools both for landscape analysis and policy making. In the face of new policy reforms, this thesis contributes to the achievement of this objective, by presenting an approach to explore and organize the heterogeneity of farmer behaviour and to make this usable in agent-based modeling frameworks.

Applying Agent-Based Modeling and Simulation (ABMS) methodology, this paper analyzes the impact of alternative production-sales policies on the diffusion of a new product and the generated NPV of profit. The key features of the ABMS model, that captures the marketplace as a complex adaptive system, are: (i) supply chain capacity is constrained; (ii) consumers' new product adoption decisions are influenced by marketing activities as well as positive and negative word of mouth (WOM) between consumers; (iii) interactions among consumers taking place in the context of their social network are captured at the individual level; and (iv) the new product adoption process is adaptive. Conducting over 1 million simulation experiments, we determined the "best" productionsales policies under various parameter combinations based on the NPV of profit generated over the diffusion process. The key findings are as follows: (1) on average, the build-up policy with delayed marketing is the preferred policy in the case of only positive WOM as well as the case of positive and negative WOM. This policy provides the highest expected NPV of profit on average and it also performs very smoothly with respect to changes in build-up periods. (2) It is critical to consider the significant impact of negative word-of-mouth on the outcomes of alternative production-sales policies. Neglecting the effect of negative word-of-mouth can lead to poor policy recommendations, incorrect conclusions concerning the impact of operational parameters on the policy choice, and suboptimal choice of build-up periods. (authors' abstract)

This paper studies the dynamics of an asset pricing model based on simple deterministic agents. Traders are heterogeneous with respect to their time horizon, prediction function and trade interval. Concerning the trade interval we distinguish between intraday traders and end-of-day traders. Intraday traders update their portfolio every period, whereas end-of-day traders adjust their positions only at the closing price of each trading day. The parameter values of the model were partially determined by an adapted Markov chain Monte Carlo sampling method. We analyse the properties of the time series and find that they exhibit low autocorrelation of the returns, volatility clustering and fat tails. Particularly heterogeneous trade intervals seem to be an important factor for generating time series showing "stylized facts". (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Agent-based simulation of smart environment finds its application in studying people’s movement to help the design of a variety of applications such as energy utilization, HAVC control and egress strategy in emergency situation. Traditionally, agent-based simulation is not dynamic data driven, they run offline and do not assimilate real sensor data about the environment. As more and more buildings are equipped with various sensors, it is possible to utilize real time sensor data to inform the simulation. To incorporate the real sensor data into the simulation, we introduce the method of data assimilation. The goal of data assimilation is to provide inference about system state based on the incomplete, ambiguous and uncertain sensor data using a computer model. A typical data assimilation framework consists of a computer model, a series of sensors and a melding scheme. The purpose of this dissertation is to develop a data assimilation framework for agent-based simulation of smart environment. With the developed data assimilation framework, we demonstrate an application of building occupancy estimation which focuses on position estimation using the framework. We build an agent based model to simulate the occupants’ movement s in the building and use this model in the data assimilation framework. The melding scheme we use to incorporate sensor data into the built model is particle filter algorithm. It is a set of statistical method aiming at compute the posterior distribution of the underlying system using a set of samples. It has the benefit that it does not have any assumption about the target distribution and does not require the target system to be written in analytic form .To overcome the high dimensional state space problem as the number of agents increases, we develop a new resampling method named as the component set resampling and evaluate its effectiveness in data assimilation. We also developed a graph-based model for simulating building occupancy. The developed model will be used for carrying out building occupancy estimation with extremely large number of agents in the future.

This research addresses the design and development of a decision support tool to provide healthcare policy makers with insights and feedback when evaluating proposed patient flow and infection mitigation and control strategies in the emergency department (ED). An agent-based modeling (ABM) approach was used to simulate EDs, designed to be tuneable to specific parameters related to specification of topography, agent characteristics and behaviours, and the application in question. In this way, it allows for the user to simulate various ‘what-if’ scenarios related to infection spread and patient flow, where such policy questions may otherwise be left “best intent open loop” in practice. Infection spread modeling and patient flow modeling have been addressed by mathematical and queueing models in the past; however, the application of an ABM approach at the level of an institution is novel. A conjecture of this thesis is that such a tool should be augmented with Machine Learning (ML) technology to assist in performing optimization or search in which patient flow and infection spread are signals or variables of interest. Therefore this work seeks to design and demonstrate a decision support tool with ML capability for optimizing ED processes. The primary contribution of this thesis is the development of a novel, flexible, and tuneable framework for spatial, human-scale ABM in the context of a decision support tool for healthcare policy relating to infection spread and patient flow within EDs . The secondary contribution is the demonstration of the utility of ML for automatic policy generation with respect to the ABM tool. The application of ML to automatically generate healthcare policy in concert with an ABM is believed to be novel and of emerging practical importance. The tertiary contribution is the development and testing of a novel heuristic specific to the ML paradigm used: Genetic Programming (GP). This heuristic aids learning tasks performed in conjunction with ABMs for healthcare policy. The primary contribution is clearly demonstrated within this thesis. The others are of a more difficult nature; the groundwork has been laid for further work in these areas that are likely to remain open for the foreseeable future.

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%.

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.

This research addresses the design and development of a decision support tool to provide healthcare policy makers with insights and feedback when evaluating proposed patient flow and infection mitigation and control strategies in the emergency department (ED). An agent-based modeling (ABM) approach was used to simulate EDs, designed to be tuneable to specific parameters related to specification of topography, agent characteristics and behaviours, and the application in question. In this way, it allows for the user to simulate various ‘what-if’ scenarios related to infection spread and patient flow, where such policy questions may otherwise be left “best intent open loop” in practice. Infection spread modeling and patient flow modeling have been addressed by mathematical and queueing models in the past; however, the application of an ABM approach at the level of an institution is novel. A conjecture of this thesis is that such a tool should be augmented with Machine Learning (ML) technology to assist in performing optimization or search in which patient flow and infection spread are signals or variables of interest. Therefore this work seeks to design and demonstrate a decision support tool with ML capability for optimizing ED processes. The primary contribution of this thesis is the development of a novel, flexible, and tuneable framework for spatial, human-scale ABM in the context of a decision support tool for healthcare policy relating to infection spread and patient flow within EDs . The secondary contribution is the demonstration of the utility of ML for automatic policy generation with respect to the ABM tool. The application of ML to automatically generate healthcare policy in concert with an ABM is believed to be novel and of emerging practical importance. The tertiary contribution is the development and testing of a novel heuristic specific to the ML paradigm used: Genetic Programming (GP). This heuristic aids learning tasks performed in conjunction with ABMs for healthcare policy. The primary contribution is clearly demonstrated within this thesis. The others are of a more difficult nature; the groundwork has been laid for further work in these areas that are likely to remain open for the foreseeable future.

This paper proposes a new model for studying the new product development process in an artificial environment. We show how connectionist models can be used to simulate the adaptive nature of agents' learning exhibiting similar behavior as practically experienced learning curves. We study the impact of incentive schemes (local, hybrid and global) on the new product development process for different types of organizations. Sequential organizational structures are compared to two different types of team-based organizations, incorporating methods of Quality Function Deployment such as the House of Quality. A key finding of this analysis is that the firms' organizational structure and agents' incentive system significantly interact. We show that the House of Quality is less affected by the incentive scheme than firms using a Trial & Error approach. This becomes an important factor for new product success when the agents' performance measures are conflicting. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Demand for intercity transportation has increased significantly in the past decades and is expected to continue to follow this trend in the future. In the meantime, concern about the environmental impact and potential climate change associated with this demand has grown, resulting in an increasing importance of climate impact considerations in the overarching issue of sustainability. This results in discussions on new regulations, policies and technologies to reduce transportation's climate impact. Policies may affect the demand for the different transportation modes through increased travel costs, increased market share of more fuel efficient vehicles, or even the introduction of new modes of transportation. However, the effect of policies and technologies on mobility, demand, fleet composition and the resulting climate impact remains highly uncertain due to the many interdependencies. This motivates the creation of a parametric modeling and simulation environment to explore a wide variety of policy and technology scenarios and assess the sustainability of transportation. In order to capture total transportation demand and the potential mode shifts, a multimodal approach is necessary. The complexity of the intercity transportation System-of-Systems calls for a hybrid Agent-Based Modeling and System Dynamics paradigm to better represent both micro-level and macro-level behaviors. Various techniques for combining these paradigms are explored and classified to serve as a hybrid modeling guide. A System Dynamics approach is developed, that integrates socio-economic factors, mode performance, aggregated demand and climate impact. It is used to explore different policy and technology scenarios, and better understand the dynamic behavior of the intercity transportation System-of-Systems. In order to generate the necessary data to create and validate the System Dynamics model, an Agent-Based model is used due to its capability to better capture the behavior of a collection of sentient entities. Equivalency of both models is ensured through a rigorous cross-calibration process. Through the use of fleet models, the fuel burn and life cycle emissions from different modes of transportation are quantified. The radiative forcing from the main gaseous and aerosol species is then obtained through radiative transfer calculations and regional variations are discussed. This new simulation environment called the environmental Ground and Air Mode Explorer (eGAME) is then used to explore different policy and technology scenarios and assess their effect on transportation demand, fleet efficiencies and the resulting climate impact. The results obtained with this integrated assessment tool aim to support a scenario-based decision making approach and provide insight into the future of the U.S. transportation system in a climate constrained environment.