The world continues to face outbreaks of disease due to natural causes as well as the threat of biological warfare. Mathematical modeling provides an avenue by which to predict and ultimately prevent widespread outbreaks. A wide variety of modeling tools have been used in the study of the spread of diseases, including Ordinary Differential Equations, Partial Differential Equations, and Difference Equations. In this study, an agent-based model is used to study the spread and control of epidemics and is based on Sirakoulis, et al. [1]. The computer program NetLogo [2] is used for simulation. The development and set-up procedures for this model are fully discussed. The model is used to study the effectiveness of vaccination and quarantine as methods of epidemic control. It is determined that the most effective means of controlling an epidemic is to quarantine individuals with symptoms. In addition, the effect of the adjacent contact coefficient in the model is examined and further development and uses of the model are discussed.

Optimal Foraging Theory is a set of mathematical models used in the field of behavioral ecology to predict how animals should weigh foraging costs and benefits in order to maximize their food intake. One popular model, referred to as the Optimal Diet Model (ODM), focuses on how individuals should respond to variation in food quality in order to optimize food selection. The main prediction of the ODM is that low quality food items should only be accepted when higher quality items are encountered below a predicted threshold. Yet, many empirical studies have found that animals still include low quality items in their diet above such thresholds, indicating a sub-optimal foraging strategy. Here, we test the hypothesis that such ‘partial preferences’ are produced as a consequence of incomplete information on prey distributions resulting from memory limitations. To test this hypothesis, we used agent-based modeling in NetLogo to create a model of flower choice behavior in a virtual bumblebee forager (SimBee). We program virtual bee foragers with an adaptive decision-making algorithm based on the classic ODM, which we have modified to include memory. Our results show that the probability of correctly rejecting a low quality food item increases with memory size, suggesting that memory limitations play a significant role in driving partial preferences. We discuss the implications of this finding and further applications of our SimBee model in research and educational contexts.

Optimal Foraging Theory is a set of mathematical models used in the field of behavioral ecology to predict how animals should weigh foraging costs and benefits in order to maximize their food intake. One popular model, referred to as the Optimal Diet Model (ODM), focuses on how individuals should respond to variation in food quality in order to optimize food selection. The main prediction of the ODM is that low quality food items should only be accepted when higher quality items are encountered below a predicted threshold. Yet, many empirical studies have found that animals still include low quality items in their diet above such thresholds, indicating a sub-optimal foraging strategy. Here, we test the hypothesis that such ‘partial preferences’ are produced as a consequence of incomplete information on prey distributions resulting from memory limitations. To test this hypothesis, we used agent-based modeling in NetLogo to create a model of flower choice behavior in a virtual bumblebee forager (SimBee). We program virtual bee foragers with an adaptive decision-making algorithm based on the classic ODM, which we have modified to include memory. Our results show that the probability of correctly rejecting a low quality food item increases with memory size, suggesting that memory limitations play a significant role in driving partial preferences. We discuss the implications of this finding and further applications of our SimBee model in research and educational contexts.

This research complements and combines market microstructure theory and mechanism design to optimize the market structure of financial markets systematically. We develop an agent-based model featuring near-zero-intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices, which orders are executed as well as a range of parameters regarding market intervention by market makers and the presence of informed traders. The market structure which generates the best market performance is determined by applying the search technique Population-based Incremental Learning, guided by a number of performance measures, including maximizing trading volume or price, minimizing bid-ask spread or return volatility. We investigate the credibility of our model by observing the trading behavior of near-zero-intelligence traders with stylized facts in real markets. Based on computer simulations, we conform that the model is capable to reproduce some of the most important stylized facts found in financial markets. Thereafter, we investigate the best found market structure using both single-objective optimization and multi-objective optimization techniques. Our results suggest that the best-found combination of trading rules used to enhance trading volume may not be applied to achieve other objectives, such as reducing bid-ask spread. The results of single-objective optimization experiments show that significantly large tick sizes appear in the best market structures in most cases, except for the case of maximizing trading volume. The tick size is always correlated with the selection of multi-price rules. Though there is no particular combination of priority rule and multiprice rule achieving the best market performance, the time priority rule and the closest multi-price rule are the most frequently obtained rules. The level of market transparency and the extend of market maker intervention show ambiguous results as their representative parameter values change in a wide range. We also nd that the results of multi-objective optimization experiments are much similar to those obtained in the single-objective optimization experiments, except for the market transparency represented by the fraction of informed trader, which shows a clear trend in the multi-objective optimization. Using the results obtained from this research we can derive recommendations for exchanges and regulators on establishing the optimal market structure; for securities issuers on choosing the best exchange for their listing; and for investors on choosing the most suitable exchange for trading.

This thesis is about the development of agent-based models that are a method of simulation of economic processes and environments using multi-agent systems. Agent-based modeling seems to be an unappreciated approach that is expected and has a potential for a much wider application than it actually has. The purpose of thiswork is to evaluate the reasons for such situation and to offer solutions. The following were identified among the reasons for a low utilization of the method: a wide gap between theory and practice in the field, doubtful reliability of the method, lowconfidence in its results, complexity, missing methodologies, problems with suitable development frameworks, limitations of computational performance, a lack of awareness among the public and certain other problems. Agentology; (i.e. a methodology for the development of agent-based models) was proposed in this thesis in order to address issues regarding the development of agent-based models. There are six defined roles of project participants in the methodology: expert, analyst, modeler, platform specialist, programmer and tester. The design and development process consists of four phases and nine steps beginning with task formulation, conceptual modeling, and platformspecific modeling to the development of the system. For the design phases, agent modeling language for agent-based models was derived.

Macroalgae are an important part of the coral reef ecosystem that has largely been overlooked. However, in the past few decades their abundances have increased and this has been attributed to combinations of coral mortality opening up space in the reef, decreased grazing and increased nutrient load in reefs. This dissertation illustrates a novel means of investigating the effect of various growth and disturbance factors on the dynamics of macroalgae at three different levels (individual, population and 3-species community). Macroalgae are modular and clonal organisms that have differing morphologies depending on the environment to which they are exposed. These traits were exploited in order to understand the factors that were acting on the dominant and common macroalgae in the Florida Reef Tract: Halimeda tuna, Halimeda opuntia and Dictyota sp. The agent-based model SPREAD (SPatially-explicit REef Algae Dynamics) was developed to incorporate the key morphogenetic characteristics of clonality and morphological plasticity. It revolves around the iteration of macroalgal module production in response to light, temperature, nutrients, and space availability, while fragmentation is the source for mortality or new individuals. These processes build the individual algae then the population. The model was parameterized through laboratory experiments, existing literature and databases and results were compared to independently collected field data from four study sites in the Florida Keys. SPREAD was run using a large range of light, temperature, nutrient and disturbance (fragmentation without survival) levels and yielded six morphological types for Halimeda tuna, and two each for Halimeda opuntia and Dictyota sp. The model morphological types that matched those measured in two inshore patch reefs (Cheeca Patch and Coral Gardens) and two offshore spur and groove reefs (Little Grecian and French Reef), were formed in conditions that were similar to the environmental (light, nutrient and disturbance) conditions in the field sites. There were also differences between species in the important factors that influenced their morphologies, wherein H. opuntia and Dictyota were more affected by disturbance than growth factors, while H. tuna morphology was affected by both. Allowing for fragmentation with survival in the model resulted in significantly higher population abundances (percent cover and density). The highest abundances were achieved under high fragment survival probabilities and a high disturbance level (but not large fragment sizes). Incorporating fragmentation with survival and simulating the variations in light, nutrients and disturbance between the inshore patch reefs and offshore spur and groove reefs in SPREAD led to comparable abundances of Halimeda in the virtual reef sites. Adding competition for space and light and epiphytism by Dictyota on the two Halimeda species suggests that it can regulate the populations of the three macroalgae. However, comparing model abundances to the field, competition may not be a strong regulating force for H. tuna in all the sites and H. opuntia in the patch reefs. H. opuntia in the offshore reefs is possibly competitively regulated. Although SPREAD was not able to capture the patterns in the population abundance of Dictyota, this points to the potential importance of other morphometrics not captured by the model, a variation in growth curves between reef habitats, or the differential contribution of sexual reproduction.

The agent-based micro-simulation modelling technique for transportation planning is rapidly developing and is being applied to practice in recent years. In contrast to conventional four-step modelling with static assignment theory, this emerging technique employs a dynamic assignment principle. Based on summary of various types of traffic assignment models and algorithms, the thesis elucidates in detail the theories of two models, MATSim and EMME/2, which represent two genres of traffic assignment, i.e., dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment. In the study, the two models are compared and validated to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results indicate that numerical outputs produced by MATSim are not only compatible to those by EMME/2 but more realistic from a temporal point of view. Therefore, agent-based micro-simulation models reflect a promising direction of next generation of transportation planning models.

The agent-based micro-simulation modelling technique for transportation planning is rapidly developing and is being applied to practice in recent years. In contrast to conventional four-step modelling with static assignment theory, this emerging technique employs a dynamic assignment principle. Based on summary of various types of traffic assignment models and algorithms, the thesis elucidates in detail the theories of two models, MATSim and EMME/2, which represent two genres of traffic assignment, i.e., dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment. In the study, the two models are compared and validated to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results indicate that numerical outputs produced by MATSim are not only compatible to those by EMME/2 but more realistic from a temporal point of view. Therefore, agent-based micro-simulation models reflect a promising direction of next generation of transportation planning models.

This paper examines the possible contribution of evolutionary economics to macro-economic modelling of flood impacts to provide guidance for future economic risk modelling. Most macro-economic models start from a neoclassical economic perspective and focus on equilibrium outcomes, either in a static or dynamic way, and describe economic processes at a high level of aggregation. As a consequence, they typically fail to account for the complexity of social interactions and other behavioural responses of consumers and producers to disasters, which may affect the macroeconomic impacts of floods. Employing evolutionary principles and methods, such as agent-based modelling, may help to address some of the shortcomings of current macro-economic models. We explore and discuss the implications of applying consumer and producer heterogeneity, bounded rationality, network effects, social and technological learning, co-evolution and adaptive policy-making concepts into existing economic frameworks for the assessment of macro-economic impacts of floods. (authors' abstract)

Rising oil prices have been a concern for both developed and developing countries, especially in more recent years as it tends to have a crippling effect on production and transportation. Many countries have moved towards the development of fossil fuel alternatives as a means of achieving energy independence and achieving environmental targets (for example the Kyoto Protocol). Developments in both these types of energy markets (fossil fuel and renewable fuels) may impact Canadian Prairie agriculture. Most of Canadian prairie crops are exported. The Canadian prairies are land locked to some extent. The closest ocean access to the eastern portion of the prairies is the port of Churchill, but is closed during the winter season. Crops are therefore transported west through the Rocky Mountains or east through the Great Lakes to get to a port. This requires hundreds of kilometres of truck and rail transportation, which is fuel dependent. To a lesser extent, at the micro-level farmers depend on fossil fuels to operate machinery to facilitate efficient crop production. If oil prices continue on an upward trajectory, will farmers cropping behaviour change? Furthermore, the development of the bioethanol industry on the Canadian prairies has given wheat farmers another crop option. As oil prices increase, the price of ethanol increases as well. Also, demand is bolstered by renewable fuel standards and government tax exemptions or subsidies. This study seeks to put forward the notion that as oil prices increase, crop production and transportation costs also increase thereby reducing farmers’ gross margins. Also, ceteris paribus, as oil prices increase there will be an increased demand for, and an increase in the price of biofuels thereby increasing the price of biofuel feedstock. Higher feedstock prices are expected to increase the gross margins of farmers. Therefore higher oil prices drive increased crop competition between traditional cropping (cropping for food exports) and energy cropping. This thesis seeks to ascertain at what level of oil prices would farmers, in general, be willing to switch from producing wheat for traditional (hard/food wheat) purposes to bioenergy (soft/ biofuel wheat) cropping alternatives. Also under varying scenarios of oil price growth and government support to the biofuel industry, this thesis seeks to ascertain the impact of biofuel industry expansion on grain elevator pricing behaviour and the structure of the elevator industry, assuming elevators spatially compete with each other for farmers’ crops. An agent based model (ABM) is employed for this study. The model is selected over other types as the researcher wants to capture the increased complexity stemming from the competition between crops that belong to at least one distribution chain. Agent based networks allow for emergent behaviour that is obtained from the spatial competition of elevators. Finally, the agent based model allows for spatial heterogeneity in location of farmers in terms of soil quality and their proximity to an elevator, which affects crop productivity and transportation costs, respectively. The ABM (also called the FARMCHAIN model) is comprised of over 35000 farmer agents, 176 elevator agents, 6 canola crushing plant agents, 5 ethanol plant agents and 1 biodiesel plant agent located on the 20 census agricultural regions (CARs) of Saskatchewan. Farmers allocate land based on their expected gross margins. Farmers produce and truck crops to the designated distribution chain. Crops move through the chain and at every stage the associated costs are computed and apportioned to the farmer. At the end of the period, gross margins are computed and these gross margins are used in computing the expected gross margins for the subsequent period. It is found that real annual crude prices would have to be greater than $133 before farmers begin to switch to producing biofuel wheat (soft wheat) from food wheat (hard wheat). This would have to be approximately 30% higher than that of 2008 in which crude prices were at record levels. Also, if biofuel support is declining then it would take a considerably higher price to entice farmers, in aggregate, to switch.