The understanding of gene movement in plant species is critical to the management of both plant and animal species reliant on that plant. Pollen is the mechanism by which plants pass their genetic material from one generation to the next. Pollen dispersal studies have focused primarily on purely random diffusion processes, while this may be a good assumption for species pollinated mainly by abiotic means, such as wind, it is most likely an over simplification for species that are pollinated by biotic means, such as insects [3]. Correlated random walk (CRW) models are a model of animal movement [10] and have been successfully used to explore the movement of animals in varying ecological contexts [1]. An agent-based model (ABM) is developed to describe pollen movement via insects as a correlated random walk (CRW). This model is used to explore how insect path lengths and pollen distribution are affected by the varying turning angle and plant density.

La co-construction et la réutilisation de modèles font l'objet de plusieurs travaux dans le domaine de la simulation. Cependant, dans le domaine plus spécifique de la Simulation Orientée Agent (SOA), nous pouvons constater un manque sur ces deux points malgré un besoin fort de la part des thématiciens. 
La co-construction est essentielle pour optimiser la mise en commun du savoir de différents experts, mais nous faisons souvent face à des divergences de points de vue. Les méthodologies existantes pour la co-construction en SOA ne permettent qu'un faible niveau de collaboration entre thématiciens durant la phase initiale de modélisation, ainsi qu'entre les des thématiciens avec les modélisateurs ou les modélisateurs-informaticiens... Pour faciliter cette co-construction, nous proposons de suivre une méthodologie de conception favorisant cette collaboration. 
La réutilisation de modèle octroie un gain de temps significatif, une amélioration du modèle et l'apport de nouvelle connaissance. Les méthodologies en SOA dans ce domaine existent. Cependant, dans le spectre de réutilisation, elles sont souvent limitées au niveau du modèle complet ou de l'agent avec l'impossibilité de "descendre" plus bas. 
L'expérience de EDMMAS, un cas concret d'un modèle issu de trois réutilisations successives, nous a permis de constater une nouvelle complexité qui découle de la démultiplication des comportements des agents et crée un décalage conséquent entre le modèle opérationnel et le modèle conceptuel. Notre objectif est de promouvoir la réutilisation aussi bien des modèles, que des agents et de leurs comportements.Pour répondre à ces questionnements, nous proposons dans ce manuscrit une manière de codifier et d'intégrer la connaissance provenant de disciplines différentes dans le modèle, tout en utilisant des modules "composables" qui facilitent la réutilisation. Nous proposons (i) une nouvelle architecture Agent (aMVC), appliquée dans un cadre multidynamique (DOM), avec l'appui (ii) d'une approche méthodologique (MMC) basée sur la décomposition et réutilisation des comportements. 
Cet ensemble de propositions, (i) et (ii), permet de conduire un projet pluridisciplinaire de SOA avec un grand nombre d'acteurs, facilitant la co-construction des modèles grâce à l'instauration de nouvelles synergies entre les différents acteurs participant à la modélisation. Les concepteurs pourront travailler de manière autonome sur leur dynamique et la plateforme fera l'intégration de ces dernières en assurant la cohésion et la robustesse du système. Nos contributions offrent la capacité de créer les briques élémentaires du système de manière indépendante, de les associer et de les combiner pour former des agents, selon des dynamiques conformément à l'approche DOM. Elles permettent ainsi de comparer la logique selon différentes possibilités pour une même dynamique et d'ouvrir la perspective d'étudier un grand nombre d'alternatives de modélisation d'un même système complexe, et de les analyser ensuite à une échelle très fine. / Co-building and reuse of models are at the center of several studies in the field of simulation. However, in the more specific field ofMulti-Agent Based Simulation (MABS), there is a lack of methodology to resolve these two issues, despite a strong need by experts.Model co-building is essential to optimize knowledge sharing amongst different experts, but we often face divergent viewpoints. Existing methodologies for the MABS co-building allow only a low level of collaboration among experts during the initial phase of modeling, and between domain experts with modelers or computer scientists... In order to help this co-building, we propose and follow a methodology to facilitate this collaboration. Model reuse can provide significant time savings, improve models’ quality and offer new knowledge. Some MABS methodologies in this area exist. However, in the spectrum of reuse, they are often limited to a full model’s reuse or agent’s reuse with the impossibility of reusing smaller parts such as behaviors. The EDMMAS experiment was a concrete case of three successive model reuses. It allowed us to observe new complexity arising from the increase of agents’ behaviors. This creates a gap between operational model and conceptual model.Our goal is to promote the reuse of models, agents and their behaviors.To answer these questions, we propose in this thesis a new way to codify and integrate knowledge from different disciplines in the model, while using "composable"modules that facilitate reuse.We propose (i) a new agent architecture (aMVC), applied to a multidynamical approach (DOM), with the support (ii) of a methodology (MMC) based on the decompositionand reuse of behaviors.Proposals (i) and (ii) allow us to lead a multidisciplinary MABS project with a large number of actors, helping the co-building of models through the introduction of synergies among the different actors involved in the modeling. They can work independently on their dynamics and the platformwill integrate those, ensuring cohesion and robustness of the system. Our contributions include the ability to create the building blocks of the system independently, associate and combine them to formagents. This allows us to compare possibilities for the same dynamic and open the prospect of studyingmany alternate models of the same complex system, and then analyze at a very fine scale.

Dans la zone nord des bassins versants de la Thaïlande, l'augmentation de la dégradation des ressources du bassin hydrographique résultant de la combinaison de l’augmentation de la population et de la croissance économique ont conduit à la création de contrôles divers par différents agents. Le gouvernement thaïlandais a fait des efforts considérables pour responsabiliser et impliquer les populations locales dans la gouvernance des ressources, pour éradiquer le problème et atténuer les conflits d'intérêts. Toutefois, la participation de la population ne progresse pas au-delà des niveaux d'information et de consultation. Afin de promouvoir la bonne gouvernance des ressources, la question de recherche proposée ici est de savoir comment utiliser la modélisation d'accompagnement (ComMod) qui est un processus de médiation outillé pour promouvoir la compréhension mutuelle et l'apprentissage adaptatif chez les intervenants afin d’améliorer la gestion collective des bassins versants. Les principales méthodes de recherche mises en œuvre dans cette étude sont le jeu de rôle (RPG), l'observation participante et la modélisation multi-agents. L'analyse préliminaire du cas du bassin versant Maehae a révélé un risque de conflit entre les agriculteurs et les forestiers. Deux sessions de jeu de rôle (RPG) ont été menées afin de mieux comprendre comment ces acteurs utilisent et gèrent des terres et des forêts malgré des conflits d'intérêts. […]En résumé, le réseau villageois, comme le réseau local, crée des liens divers entre deux ethnies et communautés. Il lie des individus, des groupes et des réseaux plus politiques et se pose en tant qu'intermédiaire informel politique pour co-gérer les ressources du bassin hydrographique et atténuer les tensions éventuelles entre les parties prenantes. La fonction du réseau villageois représente un processus d'évolution culturelle par le biais de l'apprentissage social et permet d’accroitre les préoccupations environnementales, et par conséquent, accroît la capacité d'adaptation la résilience. Cette étude […] souligne l'importance de la participation des principales parties prenantes, la confiance entre le chercheur et les acteurs, la position neutre du chercheur. La prochaine étape dans la modélisation d'accompagnement serait nécessaire, pour partager le plan de gestion collective locale avec les réseaux politiques plus interconnectés, grâce à la simulation du modèle, et passer à d'autres co-planifications et co-décisions en matière de gouvernance durable des ressources des bassins versants. / In the northern watershed area of Thailand, the increase in watershed resources degradation due to the combination of population and economic growths led to diverse controls and responsible agents. Thai government has put substantial effort to empower and involve local people in resource governance, to eradicate the problem and mitigate the conflict of interest. However, the people participation does not progress beyond informative and consultative levels. The Maehae is one of the complex watershed management cases where intensive vegetable cultivated lands located in restricted watershed area, multi-level stakeholders involved in watershed resources management existed. To promote good resource governance, the research questions proposed here is how to employ companion modeling (ComMod) process and mediating tools to promote mutual and adaptive learning among stakeholders to enhance collective watershed management. The main field research methods implemented in this study are roleplaying game (RPG), stakeholders observation and multi-agent based model (MABM). Preliminary system analysis of the Maehae revealed a potential conflict among the farmers and the forester. Two land-forest role-playing game (RPG) sessions were conducted in order to gain a better understanding on how these stakeholders use and manage land and forest under conflict of interests. […]This scenarios exploration showed that the San was only determinant factor in the “business as usual” scenarios. The San […] could reduce forest disturbance and promoted total farm productivity. […] In summary, The Maehae village network, as the local network, bridges diverse ties both ethnicities and communities. It links individual, groups and higher policy network; performs as intermediary informal political network to co-manage the watershed resources and mitigate possible tensions among stakeholders. The village network function represents a cultural evolution process through social learning and gaining of environmental concerns, therefore, enhances adaptive capacity and increase resilience. This study […] recommends the important of key stakeholders’ involvement, the trust between researcher and the stakeholders, the neutral position of the researcher. Further stage of companion modelling would be required, to share collective local management plan with larger interconnected policy networks, through the model simulation, and move to further co-planning and codecision making for sustainable watershed resource governance.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd Easton / History has shown that epidemics can occur at random and without warning — devastating the populations which they impact. As a preventative measure, modern medicine has helped to reduce the number of diseases that can instigate such an event, nevertheless natural and man-made disease mutations place us continuously at risk of such an outbreak. As a second line of defense, extensive research has been conducted to better understand spread patterns and the efficacy of various containment and mitigation strategies. However, these simulation models have primarily focused on minimizing the impact to groups of people either from an economic or societal perspective and little study has been focused on determining the utility maximizing strategy for an individual. Therefore, this work explores the decisions of individuals to determine emergent behaviors and characteristics which lead to increased probability of survival during an epidemic. This is done by leveraging linear program optimization techniques and the concept of Agent Based Simulation, to more accurately capture the complexity inherent in most real-world systems via the interactions of individual entities. This research builds on 5 years of study focused on rural epidemic simulation, resulting in the development of a 4,000-line computer code simulation package. This adaptable simulation can accurately model the interactions of individuals to discern the impact of any general disease type, and can be implemented on the population of any contiguous counties within Kansas. Furthermore, a computational study performed on the 17 counties of northwestern Kansas provides game theoretical based insights as to what decisions increase the likelihood of survival. For example, statistically significant findings suggest that an individual is four times more likely to become infected if they rush stores for supplies after a government issued warning instead of remaining at home. This work serves as a meaningful step in understanding emergent phenomena during an epidemic which, subsequently, provides novel insight to an individual’s utility maximizing strategy. Understanding the main findings of this research could save your life.

<p>The future success of agriculture in arid and semi-arid areas globally will be highly dependent on the ability of farmers and agricultural systems to adapt to climate change. Most of these areas, though tremendously productive, suffer from the same limiting resource: water. As that resource becomes more scarce and availability more difficult to predict, water managers and farmers will be forced to implement new, creative solutions to water supply challenges. This anticipated exposure suggests that an improved understanding of agricultural adaptation to water stress in such areas is critical to successful outcomes in these regions under a changing climate. This work focuses specifically on the adaptation strategies employed by farmers, strategies which are determined by farmers’ assessment of their exposure and sensitivity to a stressor as well as their capacity to implement changes. This process of implementing change to limit vulnerability is broadly referred to as adaptation. </p> <p> This project focuses on the Eastern Snake Plain of southeastern Idaho as a case study in agricultural adaptation to increased water stress. The Eastern Snake Plain (ESP) is a diverse and productive agricultural basin in the inter-mountain region of the American West. The region’s primary products are potatoes, sugar beets, barley, and alfalfa, as well as a significant volume of livestock dominated by dairy cattle, and each of these products forms a significant share of the total US market for that crop. More than 74% of this agricultural land is irrigated, inextricably tying both the future of agriculture and the future of the Idaho economy to water in the state. In the mid-2000’s, legislators and water managers from across the plain came together to negotiate a new water rights settlement, now known as the Eastern Snake Plain Aquifer Comprehensive Aquifer Management Plan (CAMP). The negotiations came in response to years of litigation involving groundwater and surface water conjunctive management in the region, and the resulting plan was designed to accomplish three goals: stabilize reach gains in the lower Eastern Snake Plain, replenish Eastern Snake Plain Aquifer (ESPA) levels, and ensure sustainable water resources for agricultural, industrial, and domestic users across the basin. Though the water settlement was not directly caused by climate change, it is likely that water shortages will become more frequent under climate change, and this settlement represents a simulation of just such a shortage.</p> <p>Broadly, this work and the work of collaborators hope to understand adaptation and decision-making of groundwater farmers throughout the Eastern Snake Plain as they adapt to the on-average 12.9% reduction in water availability. This thesis is divided into three primary sections (Chapters 2, 3, and 4). </p> <p>Chapter 2 investigates tradeoffs in adaptation decision making, employing semi-structured interviews to learn more about tradeoffs as a framework for understanding adaptation more broadly. In particular, the work seeks to understand the types of tradeoffs present in ESP adaptation and when and how tradeoffs are implicitly or explicitly acknowledged. Findings indicate that tradeoffs occur both at the individual and regional scale and that shifts in crop patterns and irrigation water sourcing may have important implications for adaptation policy moving forward. </p> <p>Chapter 3 employs a household survey and statistical analysis to investigate the iterative and complex relationships between exposure, adaptive capacity, sensitivity, and vulnerability. As an early attempt to examine these relationships quantitatively in the context of US agriculture and water stress, the works focuses on laying out a clear theoretical and methodological framework for continued exploration of adaptation and vulnerability in this context. Findings indicate that under-theorized components of adaptive capacity like linking capacity and exposure to simultaneous stressors may play important roles in determining farmer vulnerability in the context of policy-induced water scarcity. </p> <p>Chapter 4 is designed to investigate and develop a novel tool for exploratory work in adaptation, examining the feasibility and predictive accuracy of an agent-based model of agricultural adaptation driven by social-psychological decision-making theories and parameterized using both secondary data sources and primary fieldwork. Findings indicate that such models may have the potential to produce well-informed macro-level patterns based on theoretically-informed micro-level inputs. This has important implications for the broader agent-base modeling community, and the work concludes with a call for further collaboration between agent-based modelers and social science theorists. </p> <p>Collectively, this work seeks to inform theory on agricultural adaptation and vulnerability, as well as explore the potential role of theoretically-informed agent-based modeling in investigating such dynamics. In doing so, it lays the groundwork for future exploration of these ideas in the Eastern Snake Plain and throughout the arid American West. </p>

<div>In recent years, there has been considerable growth in the adoption and technology development of electric vehicles (EV), autonomous vehicles (AV), and ride sharing (RS). These technologies have the potential to improve transportation sustainability. Many studies have evaluated the environmental impacts of these technologies but the existing literature has three major gaps: (1) the adoption of these three technologies need to be evaluated considering their impact on each other, (2) many existing models do not evaluate systems on a common ground, and (3) the heterogeneous preferences of riders towards these emerging technologies are not fully incorporated. To address these gaps, this work studies and quantifies the environmental and efficiency gains that can be gained through these emerging transportation technologies by developing a Parameterized Preference-based Shared Autonomous Electric Vehicle (PP-SAEV) agent-based model. The model is then applied to a case study of New York City (NYC) taxis to evaluate the system performance with increasing AV, EV, and RS adoption.</div><div><br></div><div>The outputs from the PP-SAEV model show that replacing taxi cabs in NYC with AVs along with RS potentially can reduce CO\textsubscript{2} emissions by 866 metric Tones per day and increase average vehicle occupancy from 1.2 to 3 persons in vehicles with passenger seating capacity of 4. A prediction model based on the PP-SAEV output recommends that 6000 vehicles are needed to maintain the current level of service with 100\% AV and RS adoption using capacity 4 taxis. Taxi fleets with capacity 4 with high RS and low AV adoption are also found to have the least CO\textsubscript{2} emissions. Because the heterogeneous sharing preferences of riders have shown as the major limiting factor to ride sharing, these heterogeneous sharing preferences are further modelled. The results show that high service levels are achieved when all the riders are open to sharing, and the maximum service level is reached when 30\% of riders will only accept shared rides and 70\% of the riders are either indifferent to sharing or prefer to use ride sharing over riding alone. Additionally, the service level and waiting time of riders that are inflexible (will accept only shared or non-shared rides) are greatly impacted by varying mix of riders with different sharing preference. Finally, an optimization model was built to site charging stations in a system with continually increasing EV adoption. Using the best charging station locations, transforming a fleet of autonomous or traditional vehicles to electric vehicles does not significantly change the system service level. The results show that increasing the EV adoption in fleets with 100\% RS and AV adoption reduced the daily CO\textsubscript{2} emissions by about 861 Tones and transforming a fleet of traditional taxi cabs to electric taxi cabs reduced the daily CO\textsubscript{2} emissions by 1100 Tones.</div><div><br></div><div>In summary, this dissertation evaluates the potential growth of autonomous vehicles, ride sharing, and electric vehicles in systems where riders may have heterogeneous sharing preferences, from a system performance`s perspective and assesses the environmental impacts. The developed model and the insights gained from this study can inform policy makers to develop sustainable transportation systems incorporating the emerging transportation technologies.</div>

Student Number : 9801145J - MSc dissertation - School of Electrical and Information Engineering - Faculty of Engineering / In this document, a paper is presented that reports on the optimisation of a system that assists in time series prediction. Daily closing prices of a stock are used as the time series under which the system is being optimised. Concepts of machine learning, Artificial Neural Networks, Genetic Algorithms, and Agent-Based Modeling are used as tools for this task. Neural networks serve as the prediction engine and genetic algorithms are used for optimisation tasks as well as the simulation of a multi-agent based trading environment. The simulated trading environment is used to ascertain and optimise the best data, in terms of quality, to use as inputs to the neural network. The results achieved were positive and a large portion of this work concentrates on the refinement of the predictive capability. From this study it is concluded that AI methods bring a sound scientific approach to time series prediction, regardless of the phenomena that is being predicted.

<p>In the U.S., bioenergy accounts for about 50% of the total renewable energy that is generated. Every stage in the life cycle of using bioenergy (e.g., growing biomass, harvesting biomass, transporting biomass, and converting to fuels or materials) has consequences in terms of the three dimensions of sustainability: economy, environment, and society. An integrated sustainability model (ISM) using system dynamics is developed for a bioenergy system to understand how changes in a bioenergy system influence environmental measures, economic development, and social impacts.<br></p><p><br></p><p>Biomass may be used as a source of energy in a variety of ways. The U.S. corn ethanol system forest residue system for electricity generation, and cellulosic ethanol system have been investigated. Predictions, such as greenhouse gas (GHG) savings, soil carbon sequestration, monetary gain, employment, and social cost of carbon are made for a given temporal scale. For the corn ethanol system, the annual tax revenue created by the ethanol industry can offer a significant benefit to society. For the forest residue system for electricity generation, different policy scenarios varying the bioenergy share of the total electricity generation were identified and examined via the ISM. The results of the scenario analysis indicate that an increase in the bioenergy contribution toward meeting the total electricity demand will stimulate the bioenergy market for electricity generation. For the cellulosic ethanol system, the compliance of cellulosic ethanol can be achieved under the advanced bioconversion technologies and the expansion of energy crops. However, nitrate leaching and biodiversity change should be considered when expanding energy crops on marginal land, pasture, and cropland. Moreover, three bioenergy systems reduce GHG emissions significantly, relative to fossil fuel sources that are displaced, and create economic benefits (e.g., GDP and employment). Additionally, a spatial agent-based modeling is developed to understand farmers’ behaviors of energy crop adoption and the viability of cellulosic biofuel commercialization.<br></p>

The fungal microbiome, or “mycobiome” of plants is diverse and important to host health, but the fluxes of fungi among plant hosts and with the surrounding environment are poorly understood. In chapter two, we employed sterile culture techniques and spatial sampling to examine leaves as possible vectors for transfer of their endophytic fungi from the canopy to substrate on the forest floor, as predicted by the Foraging Ascomycete hypothesis. Some foliar endophytic fungal species are also present as wood-decomposing fungi on the forest floor, that transfer of mycelium across these two life history stages can occur, that endophytic life history stages are buffered from environmental conditions in comparison to wood-decomposing fungi, and that spatial linkages between the two life history stages can be observed. In another study, described in chapter 3, wood and leaf wood endophytes were sampled across a 25 ha plot, to explore landscape patterns of mycobiomes, and to explore the concept of a core microbiome in aerial plant tissues. We found that core microbiomes may be observed in a real ecological setting, but that the concept of core must be carefully defined and that some level of buffering from disturbance may be necessary to allow core microbiomes to assemble. In chapter four, we return to examine some of the assumptions and implications of the Foraging Ascomycete hypothesis, with an agent-based model. We model the conditions under which dispersal through falling leaves may represent a fitness-enhancing dispersal strategy for fungi, and that deforestation as is currently underway throughout the world may have impacts on fungi that rely upon a canopy- inhabiting life stage for dispersal. In chapter five, some challenges associated with environmental sampling of microbes using illumina© MiSeq sequences are critically examined. We find that biases introduced by random sampling at various stages of IVenvironmental DNA extraction and illumina© MiSeq sequencing are not well corrected by currently accepted bioinformatic algorithms. In addition, information loss from differential extraction, PCR amplification, and sequencing success, requires that users of MiSeq read libraries to interpret read abundances carefully. This dissertation includes previously published, co-authored material.

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