Our model considers a new element in forest fire modeling, namely the dynamics of a forest animal, intimately linked to the trees. We show that animals and trees react differently to different types of fire. A high probability of fire initiation results in several small fires, which do not allow for a large fuel accumulation and thus the destruction of many trees by fire, but is found to be generally devastating to the animal population at the same time. On the other hand, a low fire initiation probability allows for the accumulation of higher quantities of fuel, which in turn results in larger fires, more devastating to the trees than to the animals. Thus, we suggest that optimal fire management should take into account the relation between fire initiation and its different effects on animals and trees. Further, wildfires are often considered as prime examples for power-law-like frequency distributions, yet there is no agreement on the mechanisms responsible for the observed patterns. Our model suggests that instead of a single unified distribution, a superposition of at least two different distributions can be detected and this suggests multiform mechanisms acting on different scales. None of the discovered distributions are compatible with the power-law hypothesis.

Metapolybia wasps live in small societies (around one hundred adults) and rear their young in nests they construct on flat surfaces from plant materials. For processing nest paper, they must gather plant materials and process it into pulp with water. The water is collected by water foragers and is transferred to pulp foragers indirectly via a "common stomach." The common stomach, or social crop, is formed by generalist wasps called laborers. These wasps can engage in water exchange, store water in their crops, and may become specialist foragers or builders. We provide an alternative model for regulating task partitioning in construction behavior by using an agent based modeling framework parameterized by our field observations. Our model predicts that assessing colony needs via individual interactions with the common stomach leads to a robust regulation of task partitioning in construction behavior. By using perturbation experiments in our simulations, we show that this emergent task allocation is able to dynamically adapt to perturbations of the environment and to changes in colony-level demands or population structure. The robustness of our model stems from the fact that the common stomach is both a strong buffer and a source of several feedback mechanisms that affect the individual wasps. We show that both the efficiency and the task fidelity of these colonies are dependent upon colony size. We also demonstrate that the emergence of specialist wasps (individuals with high task fidelity) does not require any special initial conditions or reinforcement at the individual level, but it is rather a consequence of colony-level workflow stability. Our model closely mimics the behavior of Metapolybia wasps, demonstrating that a regulation mechanism based on simple pair-wise interactions through a common stomach is a plausible hypothesis for the organization of collective behavior.

Forager honey bees exhibit a robust time memory, based on an endogenous circadian clock, enabling them to schedule their flights to coincide with the nectar presentation of known food sources. They retain this time memory for several consecutive days even in the absence of nectar rewards. Recent work has identified 2 classes of forager: "persistent" foragers that reconnoiter a known food source to ascertain its status and "reticent" foragers that apparently wait in the hive for a waggle dance confirming source availability. Surprisingly, a foraging group contains 40-90% persistent foragers, depending on experience at the source. What is the benefit in sending so many foragers to investigate a source when only a few foragers are required to reactivate the entire group? We used an agent-based software model to test the energetics underlying several different ratios of persistent and reticent individuals in the foraging group while varying 6 ecological factors: forager group size, source distance, source sucrose concentration, source availability in hours, number of days the source is known to the colony, and the rate at which new unemployed foragers appear on the dance floor. Our model demonstrates 2 primary explanations. First, a large number of persistent foragers are needed to ensure that at least some foragers will reconnoiter their source early in its availability, thus enabling the group to effectively exploit the source. Second, the cost of a reconnaissance flight is negligible compared with even a single successful foraging trip.

Small unmanned aircraft systems (sUAS) will become a disruptive force on the modern battlefield. In recent years, sUAS size and cost have decreased while their capability has increased. They have forced a reconsideration of the air superiority paradigm held since the First World War. Perhaps their most attractive, and worrisome, feature is the huge range of combat roles that they might fulfill. The presence of sUAS on future battlefields is certain, but the role they will play and their impact on those battlefields are not. This work presents a decision support framework for sUAS deployment in small infantry units. The framework is designed to explore and evaluate multiple sUAS-small-unit deployment concepts' impact on small unit effectiveness in a combat scenario of interest. The framework helps decision makers identify high-level sUAS deployment principles for testing and validation in physical experiments before sUAS are implemented on the battlefield. The decision support framework comprises the following: 1) a definition of the sUAS-small-unit deployment concept design space and combat scenario, 2) an agent-based computer model for exploring sUAS deployment concepts, 3) a set of analysis tools for evaluating sUAS deployment impact on combat effectiveness, and 4) suggestions for synthesizing high-level sUAS deployment principles from the analysis. In this work, the decision support framework for sUAS-small-unit deployment is used to explore and evaluate the impact of deploying an infantry platoon with between one and nine unmanned aerial vehicles (UAV) operating in a reconnaissance role while executing one of several sUAS patrol pattern variants. In a scenario in which a defending platoon uses sUAS to intercept and aid in indirect fires targeting against a platoon of attacking infantry, the sUAS were shown to markedly improve the defending platoon's combat effectiveness. The framework is used to synthesize several key principles for sUAS deployment in the scenario. It shows that, when fewer UAVs are deployed, short-range sUAS patrols improve defender combat effectiveness. Conversely, when more UAVs are deployed, long-range sUAS patrols improve the defenders' ability to target attacking units with indirect fires, increasing the firepower concentrated against opponents. The analysis also shows that increasing the number of deployed UAVs improves the likelihood of defending warfighters surviving the engagement and the defenders' ability to detect and engage the attackers with indirect fires. Finally, the framework shows that sUAS can force alterations in attacker behavior, removing them from combat by non-violent, but highly effective, means.

Agent-based asset exchange models serve as an interesting and tractable means by which to study the emergence of an economy's wealth distribution. Although asset exchange models have reproduced certain features of real-world wealth distributions, previous research has largely neglected the effects of economic growth and network connectivity between agents. In this work, we study the effects of globalization on wealth inequality in the Growth, Exchange, and Distribution (GED) model [Liu et al, Klein et al] on a network or lattice that connects potential trading partners. We find that increasing the number of trading partners per agent results in higher levels of wealth inequality as measured by the Gini coefficient and the variance of the agent wealth distribution. However, if globalization is accompanied by a proportionate increase in the economic growth rate, the level of inequality can be held constant. We present a mean-field theory to describe the GED model based on the Fokker-Planck equation and derive the stationary wealth distributions of the network GED model. For large Ginzburg parameter for which mean-field theory is applicable, the wealth distributions for the fully connected model are found to be Gaussian; however, for sparse trade networks, a non-Gaussian "hyperequal" phase is found even for large Ginzburg parameter. It is shown that several networks (Erdos-Renyi, Barabsi-Albert, one-dimensional and two-dimensional lattices) display mean-field critical exponents when the Ginzburg parameter is large and held constant and the system parameters are scaled properly.

Home to the world’s longest coastline, Canada has experienced devastating economic and social from coastal flooding events. While there have been a variety of mitigation methods employed over the years to increase a community’s resistance to coastal hazards, it is unrealistic to think that there exists a solution to guarantee a community’s safety under all possible flood hazards. Instead, the community’s efforts to raise their resistance to flood hazards should be augmented with careful planning and management to increase a community’s resilience to flood hazards, allowing them to recover quickly after a natural disaster. The first step in elevating a community’s resilience is to better understand the expected hazards that it may experience. This thesis presents two unique case studies to better understand the flooding hazards present on the Canadian coastline. A large-scale numerical model that accounts for the presence of ice was developed to investigate storm surges in Canada’s western Arctic. It was found that the quality of the climatic forcing data used, ERA5, was poor in capturing peak wind speeds, but could be compensated for by using elevated wind drag coefficients. The use of non-traditional high-water marks such as driftwood lines were validated and were shown to significantly alter expected flood return periods compared to the return periods estimated from only the incomplete tide gauge measurements present on the Arctic coastline. The second case study extends the results of a tsunami hydrodynamic simulation on Canada’s Pacific coastline through a life safety assessment. The performance between an agent-based and GIS-based approach to modelling tsunami evacuation were directly compared and were shown to yield different magnitudes in fatality rate and facility demand, but similar trends. Both models agreed on a mitigation option that can significantly reduce the loss of life during a tsunami.

In North America, the grizzly bear (Ursus arcos) is one of many species increasingly threatened by the consequences of human-wildlife conflict, with human-bear encounters on the rise due to increased human activity near or in bear habitat. As a result, a growing number of bears are subjected to management measures such as translocations in which animals are moved to areas with lower risk of human conflict, although these measures are not always successful. Previous research has attempted to understand factors associated with translocation success, but new methods are needed to address the continuous and complex nature of issues related to how animals move and learn about their surroundings as well as how they adapt to novel environments. The objective of my MSc thesis is to develop and employ a novel agent-based computer simulation model to analyze how grizzly bears learn and respond following a translocation event. This modelling effort attempts to capture how bears make decisions based on multiple factors, and represent how grizzly bears interact with their environment and make movement decisions based on learned behaviours. First, an agent-based movement model was developed for female grizzly bears using GPS-location data for bears within a region in west-central Alberta, Canada. The model, which incorporates multi-scale decision-making and machine learning, generated movement patterns similar to those observed in radio-collared females in the study area. Home range sizes and movement metrics produced by the model were consistent with those observed in female grizzly bears in the area. The model was then used to simulate translocation events in which bears with varying “exploration” propensities were translocated to habitats with familiar or novel landscape characteristics. In general, bears translocated to habitats with similar landscape features to their original habitat were more likely to use high-quality habitat than bears moved to areas with very different landscape features. However, while increased exploration led to greater use of high-quality habitat in the long run, exploratory behaviour was found to be mostly detrimental during the first years following a translocation, the period considered critical for translocation success. Model results were found to be scale-dependent with results varying both in time and space, highlighting the need for a multi-scale approach to animal movement studies. The findings presented here also emphasize the need to account for behavioural traits of wildlife and habitat characteristics of the capture and release sites when selecting suitable translocation locations. This work highlights the potential for agent-based modelling as a tool to study animal movement as a continuous and complex process and evaluate conservation policies. / Graduate / 2021-08-24

We develop a computational framework for risk mitigation in high population density events. With increased global population, the frequency of high population density events is naturally increased. Therefore, risk-free crowd management plans are critical for efficient mobility, convenient daily life, resource management and most importantly mitigation of any inadvertent incidents and accidents such as stampedes. The status-quo for crowd management plans is the use of human experience/expert advice. However, most often such dependency on human experience is insufficient, flawed and results in inconvenience and tragic events. Motivated by these issues, we propose an agent-based mathematical model describing realistic human motion and simulating large dense crowds in a wide variety of events as a potential simulation testbed to trial crowd management plans. The developed model incorporates stylized mindset characteristics as an internal drive for physical behavior such as walking, running, and pushing. Furthermore, the model is combined with a visualisation of crowd movement. We develop analytic tools to quantify crowd dynamic features. The analytic tools will enable verification and validation to empirical evidence and surveillance video feed in both local and holistic representations of the crowd. This work addresses research problems in computational modeling of crowd dynamics, specifically: understanding and modeling the impact of a collective mindset on crowd dynamics versus mixtures of heterogeneous mindsets, the effect of social contagion of behaviors and decisions within the crowd, the competitive and aggressive pushing behaviors, and torso and steering dynamics.

The Southern Swedish hospital region is the home of nearly 2 million people, inwhich 5,684 individuals were diagnosed by stroke during 2016, according tostatistics from the hospitals in the region. With this large number ofstroke-diagnosed patients across the region, an effective stroke transport policy isinevitably important to provide fast treatment for these patients.In this thesis, we developed an agent-based simulation model for evaluating theperformance of transport logistics policies. We followed the Design ScienceResearch methodology in order to design and develop the model. Using the model,we assessed two transport logistics policies for the Southern Swedish hospitalregion. We used a synthetic set of stroke patients, which we generated using Montecarlo simulation, for the processes of developing the model and assessing our twostroke transport logistics policies.We argue that the assessment of transport logistics policies is important for theability to improve the planning process, for example, when choosing hospital fordiagnosis of patients showing stroke symptoms. The optimization of the strokelogistics process aims to ensure the quality and operational efficiency of the hospitalsector as well as to increase the chance of survival of stroke patients.

Achieving sustainable development to meet the needs of current and future generations is currently on top of the global agenda, both in scientific research as well as global politics. However, achieving sustainable development is still a grand challenge, not least because it is embedded in the context of global change that affects most resource use systems worldwide in multiple ways. Even though many approaches to sustainable management do consider the connection between human activity and environmental dynamics, the role of human behavior as a main driver of system dynamics in coupled human and natural systems is often only poorly addressed. In this thesis, we aim to contribute to an improved understanding under which conditions human resource use decisions lead to sustainable outcomes, with regard to global change. For this, we will take the perspective of human decision-making and its social, ecological and economic consequences in two different resource use contexts, namely a) pastoralism in drylands and b) disaster risk management with respect to floods. We explicitly consider individual human decision-making as driver of social-ecological system dynamics, investigate the feedbacks between system components, as well as the impact of global change on resource use. To analyze such complex system dynamics, simulation models have proven to be helpful analysis tools. Particularly agent-based modeling represents a flexible and powerful analysis tool, as it allows us to model the decisions and interactions of individual agents at the micro level, while at the same time observing the outcome of their behavior on a system level. Within three case studies, we develop agent-based simulation models that capture the dynamics and feedbacks of the social-ecological system under consideration in a spatially explicit way. The first study analyzes the performance of disaster management organizations under change. In the second study, we aim to detect the drivers for polarization in a pastoral system in Morocco. The last study investigates behavioral change of pastoralist households and its impact on social, ecological and economic outcome measures. By analyzing a range of scenarios in each study, we determine both the long-term impact of different decision regimes on the state of the social-ecological system as well as the dimensions of change that have the most profound impact on the system dynamics and the sustainability of resource use. Main results that could be obtained from the modeling experiments include the identification of key resources that have a high influence on the long-term system dynamics. We are also able to show that under the influence of global change, access to certain resources gains in importance, as resources can act as buffer mechanisms to mitigate the adverse effects of global change. Through the operationalization of behavioral theories in model rules and the explicit representation of heterogeneous agent decision making, we could determine under which conditions a more refined representation of human decision making matters, and when a change in behavioral strategies leads to different social-ecological outcomes. Furthermore, all three modeling studies demonstrate the usefulness of stylized agent-based models to gain insights into complex systems. Overall, this thesis contributes to social-ecological systems research by developing appropriate simulation models to address the problem of sustainable resource use under global change.