First generation ethanol production has experienced rapid expansion but is now at a crossroads facing impending industry transformation. While Saskatchewan’s ethanol industry has benefited from demand and policy instruments that have guided substantial growth in recent years, changing policy and market dynamics present new challenges which are compelling the industry to adjust. This thesis examines three factors that are suspected to influence ethanol plant locational decisions. The development of an agent-based simulation model in this thesis will ascertain how transportation networks, market synergies, and subsidization influence location stability for an ethanol plant. The long term interaction of these factors is unknown, therefore do tradeoffs exist between these factors or is it conditional for all to be present? Modeling factors that affect location stability through an agent-based approach creates a dynamic framework to understand how location attributes impact an ethanol agent’s longevity. It was found that location stability is affected by an ethanol agent’s distance to both primary transportation networks as well as product markets. Surprisingly, distance to DDGS (dried distillers grain with solubles) markets, a low value by-product of ethanol production, has a profound effect on location stability. Policy instruments and industry subsidization are considered key ethanol development drivers and the surge in ethanol industry growth brought hopes of rural revitalization. In Saskatchewan, policy was developed to support small ethanol plants, those 25 Mmly (million litres per year) or smaller, aimed at increasing farmer investment and alternative markets for wheat. Measuring the effect of subsidization on location stability was fundamental to understanding how a post subsidized ethanol industry may look. The research found that subsidization of Saskatchewan’s ethanol industry dramatically affected economies of scale and location decisions, which left ethanol agents unable to compete in an increasingly competitive ethanol industry.

An agent based-model of mobility of pastoralists was developed and applied to the semi-arid rangeland region extending from southern Ethiopia to northern Kenya. This model was used to investigate temporal adaptation of pastoralists to the spatial heterogeneity of their environment. This dissertation describes the development, structure, and corroboration process of the simulation model, Pastoral Livestock Movement Model (PLMMO). PLMMO is a spatially explicit, individual-based pastoralists-animal foraging and movement model. It simultaneously simulates the foraging and movement behavior of individual pastoralists and their livestock in a rangeland ecosystem. Pastoralists?? herd mobility patterns and other measures of movement were compared to data from field studies. Predictions of the model correspond to observed mobility patterns across seasons. The distances moved were found to be significantly correlated (r2 = 0.927 to 0.977, p<0.0001) to drought and non-drought climatic regimes. The PLMMO model therefore proved to be a useful tool for simulating general movement patterns of pastoralists relative to movement range sizes in the pastoral rangelands of southern Ethiopia and northern Kenya. We then used the PLMMO model to explore the impact of emerging changes in rangeland use in the study area. The ways in which pastoralists?? mobility patterns adapt to emerging challenges in the study area were explored by simulating the following four scenarios: 1) climate change with concomitant reduction in forage yield, 2) climate change with concomitant improvement and higher variability in forage yield, 3) increased livestock population densities and 4) improved access to water. The climate induced change scenario with increased and more variable forage production resulted in the shortest distances moved by pastoralists in comparison to all other scenarios. The total search distances under this scenario were only 20% of normal season distances. The improved water access scenario also returned a significant (p=0.017) drop in distances moved. There was, however, no significant impact on either increase in livestock numbers or reduction in available forage on mobility. We judged the agent-based model PLMMO developed here as a robust system for emulating pastoral mobility in the rangelands of eastern Africa and for exploring the consequences of climate change and adaptive management scenarios.

Nowadays, facility managers and related staffs are facing with much maintenance requests every day. The more complicated building system generates the more diverse and complex maintenance issues. With the limited budget and staff, not all the maintenance requests can be solved immediately. To schedule the maintenance work, facility managers first consider the impact of requested problem on system failure and life safety. Besides these two factors, the author proposed the importance of considering the energy efficiency and occupant satisfaction based on the former research for sustainability. This paper firstly tries to quantify the occupant satisfaction for normal daily maintenance requests which will provide the facility managers with suggestions on work prioritization. For a long time, it is a difficult task to quantify the occupant satisfaction, even though there are enough researches concerning the people satisfaction. In this research, author first designed a structured questionnaire including normal maintenance issues and they are measured by different factors such as thermal impact, acoustic impact, and so on. Then based on the classical disconfirmation theory, a framework was built to prioritize numerous works based on occupant satisfaction. For energy efficiency, due to the limitation of collecting real measured data, this paper referred the work from Lawrance Lab. They conducted the research to simulate the daily HVAC faults to quantify the energy impact through EnergyPlus, which provided the data of energy increase for some daily HVAC faults. An agent based model is proposed to both consider these two factors. Simulation was used to verify the framework and the result showed that the total satisfaction level and energy efficiency can be increased by 30% and 97% respectively.

Freight transport is the output of an economic market, which converts commodity flows into vehicle flows. Interactions in this market influence vehicle flows and since freight market characteristics (product differentiation and economies of scale/scope) violate perfect competition conditions, the output of this market cannot be predicted directly, unless these interactions are represented in the forecasting models. Traditional freight modelling frameworks do not consider these interactions and consequently they may provide inaccurate freight flow forecasts. In this dissertation, a freight modelling framework is proposed using simulation of freight agent interactions in the economic market to forecast freight flows. The framework is named FREMIS (FREight Market Interactions Simulation). The FREMIS framework consists of two demand models to represent shipper decisions (bundling of shipments and carrier selection) in the market and functions based on profit maximizing behaviour to simulate carrier proposals for contracts. Besides that, learning models are proposed to simulate agent learning processes based on their interactions. The framework was developed aiming to create a realistic representation of freight markets using feasible data collection methods. To illustrate the feasibility of the data collection, a customized web survey was implemented with shippers and carriers in a freight market. Two probabilistic models were developed using the data. The first model, a shipment bundling model was proposed combining a probabilistic model and a vehicle routing algorithm. The results of the probabilistic model are presented in this dissertation, where the locations of shipments (origin and destination) influence the probability of bundling them. Second, three carrier selection models were developed aiming to analyse the nonresponse bias and non-attendance problem in the survey. All of these models assumed heteroskedasticity (different scale or variance) in shipper behaviour. In all models, the hypothesis of agents’ heteroskedasticity cannot be rejected. Besides that, nonresponse bias and non-attendance problem were identified in the survey. In conclusion, the models obtained from the survey were consistent with their behavioural assumptions and therefore they can be adopted during FREMIS implementation.

The ill-effects of stress due to fatigue significantly impact the welfare of individuals and consequently impact overall corporate productivity. This study introduces a simplified model of stress in the workplace using agent-based simulation. This study represents a novel contribution to the field of evolutionary computation. Agents are encoded initially using a String Representation and later expanded to multi-state Binary Decision Automata to choose between work on a base task, special project or rest. Training occurs by agents inaccurately mimicking behaviour of highly productive mentors. Stress is accumulated through working long hours thereby decreasing productivity performance of an agent. Lowest productivity agents are fired or retrained. The String representation for agents demonstrated near average performance attributed to the normally distributed tasks assigned to the string. The BDA representation was found to be highly adaptive, responding robustly to parameter changes. By reducing the number of simplifications for the model, a more accurate representation of the real world can be achieved.

Over the last two decades, sanitation policy and development has undergone a paradigm shift away from heavily-subsidized, supply-driven approaches towards behavioral-based demand-driven approaches. These current approaches to increase sanitation demand are multi-faceted, requiring multiple stakeholders with varying degrees of interest, knowledge, and capacity. Although efforts exist to increase sanitation access by incorporating engineering design principles with implementation planning approaches, these groups generally work independently without strong connections, thus reducing the potential of their impact. As a result, the design of appropriate sanitation technology is disengaged from the implementation of acceptable technology into communities, disconnecting user preference integration from sanitation technology design and resulting in fewer sanitation technologies being adopted and used. To address these challenges in developing successful interventions, this research examined how user preferences for specific attributes of appropriate sanitation technologies and their respective implementation arrangements influence their adoption and usage. Data for the study included interviews of 1002 sanitation users living in a peri-urban area of South Africa; the surveyed respondents were asked about their existing sanitation technology, their preferences for various sanitation technology design attributes, as well as their perspectives on current and preferred sanitation implementation arrangements. The data revealed that user acceptability of appropriate sanitation technology is influenced by the adoption classification of the users. Through the identification of motives and barriers to sanitation usage that were statistically significant, it exhibited the need to differentiate users who share private sanitation from those use communal sanitation facilities. Results also indicated that user acceptability of appropriate sanitation systems is dependent on the technical design attributes of sanitation. The development of utility functions detailed the significance of seven technical design attributes and determined their respective priorities. An agent-based simulation examined how user preferences for sanitation technology design and implementation influence its adoption and usage. Findings suggest that user acceptability of sanitation technology is dependent on both the technology design and the implementation arrangement being preferred.

We study the influence of technological efficiency and organizational inertia on the emergence of competition when firms decide myopically. Using an agent-based computer simulation model, we observe the competitive reaction of a former monopolist to the advent of a new competitor. While the entrant uses a new technology, the monopolist is free either to stick to his former technology or to switch to the new one. We find that?irrespective of details regarding the demand side?a change of industry leadership occurs only if the new (?disruptive?) technology is not too efficient and organizations are inert. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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Many practical problems raise the challenge of making decisions over time in the presence of both dynamic complexity and pronounced uncertainty regarding evolution of important factors that affect the dynamics of the system. In this thesis, we provide an end-to-end implementation of an easy-to-use system to confront such challenges. This system gives policy makers a new approach to take complementary advantage of decision analysis techniques and System Dynamics by allowing easy creation, evaluation, and interactive exploration of hybrid models. As an important application of this methodology, we extended a System Dynamic model within the context of West Nile virus transmission in Saskatchewan.

<p>Agent-based models (ABMs) are computational models used to simulate the behaviors, </p><p>actionsand interactions of agents within a system. The individual agents </p><p>each have their own set of assigned attributes and rules, which determine</p><p>their behavior within the ABM system. These rules can be</p><p>deterministic or probabilistic, allowing for a great deal of</p><p>flexibility. ABMs allow us to</p><p>observe how the behaviors of the individual agents affect the system</p><p>as a whole and if any emergent structure develops within the</p><p>system. Examining rule sets in conjunction with corresponding emergent</p><p>structure shows how small-scale changes can</p><p>affect large-scale outcomes within the system. Thus, we can better</p><p>understand and predict the development and evolution of systems of</p><p>interest. </p><p>ABMs have become ubiquitous---they used in business</p><p>(virtual auctions to select electronic ads for display), atomospheric</p><p>science (weather forecasting), and public health (to model epidemics).</p><p>But there is limited understanding of the statistical properties of</p><p>ABMs. Specifically, there are no formal procedures</p><p>for calculating confidence intervals on predictions, nor for</p><p>assessing goodness-of-fit, nor for testing whether a specific</p><p>parameter (rule) is needed in an ABM.</p><p>Motivated by important challenges of this sort, </p><p>this dissertation focuses on developing methodology for uncertainty</p><p>quantification and statistical inference in a likelihood-free context</p><p>for ABMs. </p><p>Chapter 2 of the thesis develops theory related to ABMs, </p><p>including procedures for model validation, assessing model </p><p>equivalence and measuring model complexity. </p><p>Chapters 3 and 4 of the thesis focuses on two approaches </p><p>for performing likelihood-free inference involving ABMs, </p><p>which is necessary because of the intractability of the </p><p>likelihood function due to the variety of input rules and </p><p>the complexity of outputs.</p><p>Chapter 3 explores the use of </p><p>Gaussian Process emulators in conjunction with ABMs to perform </p><p>statistical inference. This draws upon a wealth of research on emulators, </p><p>which find smooth functions on lower-dimensional Euclidean spaces that approximate</p><p>the ABM. Emulator methods combine observed data with output from ABM</p><p>simulations, using these</p><p>to fit and calibrate Gaussian-process approximations. </p><p>Chapter 4 discusses Approximate Bayesian Computation for ABM inference, </p><p>the goal of which is to obtain approximation of the posterior distribution </p><p>of some set of parameters given some observed data. </p><p>The final chapters of the thesis demonstrates the approaches </p><p>for inference in two applications. Chapter 5 presents application models the spread </p><p>of HIV based on detailed data on a social network of men who have sex with</p><p>men (MSM) in southern India. Use of an ABM</p><p>will allow us to determine which social/economic/policy </p><p>factors contribute to thetransmission of the disease. </p><p>We aim to estimate the effect that proposed medical interventions will</p><p>have on the spread of HIV in this community. </p><p>Chapter 6 examines the function of a heroin market </p><p>in the Denver, Colorado metropolitan area. Extending an ABM </p><p>developed from ethnographic research, we explore a procedure </p><p>for reducing the model, as well as estimating posterior </p><p>distributions of important quantities based on simulations.</p> / Dissertation