Biological emergency response planning plays a critical role in protecting the public from possible devastating results of sudden disease outbreaks. These plans describe the distribution of medical countermeasures across a region using limited resources within a restricted time window. Thus, the ability to determine that such a plan will be feasible, i.e. successfully provide service to affected populations within the time limit, is crucial. Many of the current efforts to validate plans are in the form of live drills and training, but those may not test plan activation at the appropriate scale or with sufficient numbers of participants. Thus, this necessitates the use of computational resources to aid emergency managers and planners in developing and evaluating plans before they must be used. Current emergency response plan generation software packages such as RE-PLAN or RealOpt, provide rate-based validation analyses. However, these types of analysis may neglect details of real-world traffic dynamics. Therefore, this dissertation presents Validating Emergency Response Plan Execution Through Simulation (VERPETS), a novel, computational system for the agent-based simulation of biological emergency response plan activation. This system converts raw road network, population distribution, and emergency response plan data into a format suitable for simulation, and then performs these simulations using SUMO, or Simulations of Urban Mobility, to simulate realistic traffic dynamics. Additionally, high performance computing methodologies were utilized to decrease agent load on simulations and improve performance. Further strategies, such as use of agent scaling and a time limit on simulation execution, were also examined. Experimental results indicate that the time to plan completion, i.e. the time when all individuals of the population have received medication, determined by VERPETS aligned well with current alternate methodologies. It was determined that the dynamic of traffic congestion at the POD itself was one of the major factors affecting the completion time of the plan, and thus allowed for more rapid calculations of plan completion time. Thus, this system provides not only a novel methodology to validate emergency response plans, but also a validation of other current strategies of emergency response plan validation.

The stock market is a highly complex adaptive system as different entities interact, operate and change states due to a specific trading behavior they follow. For that reason, the dynamics that can be found there change over time due to these actions. However, when systematic risks like COVID-19 take place these dynamics are altered. In this master thesis, an agent-based modelis constructed that simulates the interaction between investors and five companies with the purpose of investigating how its dynamics are affected. This agent-based model is constructed by using the environment of Python. Additionally, another purpose of this study is to compare a financial index like OMSX30 to a constructed financial index given by the agent-based model. Lastly, according to the findings of this study, a constructed index made with the corresponding model does not fully capture the dynamics of OMSX30. However, there are some similarities in terms of trends.

The adoptation of environmentally friendly behaviour is rather slow, although the climate crisis is pressing. This thesis aims to understand the slow adoption of environmentally friendly behaviour, specifically focusing on vegetarianism and veganism, by employing social network analysis. By simulating interactions within an agent-based model, the study explores different mechanisms that hinder the diffusion of these behaviours. The research findings highlight the significance of the complexity of the contagion in shaping the speed and extent of the diffusion process. While minimally complex contagions are able to infect half of the network on average, vegetarianism and veganism do not spread, due to their complexity. Additionally, the initial number of vegetarians/ vegans was found to be the main driver of infection speed, besides inter-connectedness. The study also explores the possibility of a social tipping point, a critical threshold at which the diffusion process accelerates or reaches a critical mass. However, the research did not observe a tipping point in the adoption of vegetarianism and veganism. By examining the slow adoption of vegetarianism and veganism as a complex contagion, this research contributes to the comprehension of concrete network effect. The findings provide valuable insights for designing interventions and strategies to promote the widespread adoption of vegetarianism, veganism, and other environmentally friendly practices.

An examination of the effectiveness of transmission control measures for COVID-19 in a supermarket setting, factoring for the inclusion of Super-Spreaders, must extend beyond the direct effects the control measure has on transmission in order to account for the indirect effects changes in human movement dynamics have on the spread of disease. The analysis makes use of Agent-Based Modelling simulation techniques to model changes in customer movement and disease transmission dynamics resulting from the isolated and combined implementation of COVID-19 transmission control measures. The bottom-up approach of agent-based modelling allows for the inclusion of heterogeneous, individual-level chances of infectiousness, compliance, and consumer behaviours, allowing for a more realistic representation of real-world behaviours. The model used for analysis is built entirely in the NetLogo environment, designed to be interactive, adaptable to user-varied inputs, and visually engaging. This allows for the model to adapt to changes in disease parameters and easily communicate model effects in a manner accessible to users in and out of the field. Control measures considered include: Vaccinations, Capacity Limiting, Social Distancing, Staff COVID-19 Testing, and the use of Sanitizers. Results indicate high levels of effectiveness for the use of Vaccinations at reducing transmission with minimal impact on customer dynamics. The results also highlight the negative effects changes in customer dynamics can have on transmission, indicated by increased shop-queue transmissions resulting from the use of Capacity Limiting or other measures slowing customer entrance to the shop. The positive effects of interactions between control measures are highlighted by the additional implementation of Social Distancing in reducing these increases. The implications of these findings involve the need to factor for changes in human movement dynamics when assessing the effectiveness of transmission control measures implemented in any environment. The findings further reinforce the benefits of implementing social distancing practises in conjunction with mechanisms that reduce the flow of movement, as well as the benefits of increased vaccination coverage in the population. Lastly, the findings provide an effective comparison of the control measures considered, allowing for the direct assessment of their implementation and the resulting effects on transmission and customer dynamics.

Beginning in 2019 and quickly spreading internationally, the Coronavirus disease Covid-19 became the first pandemic that many people have witnessed firsthand along with the severe disruption to their daily lives. A key field of research for Covid-19 that is studied by epidemiologists, biologists, and computer scientists alike is modeling the spread of Covid-19 in order to better predict future outbreaks of the pandemic and evaluate potential strategies to reduce infections, hospitalizations, and deaths. This thesis proposes a method of modeling Covid-19 spread and interventions for local environments based on different levels of perspective. The goal for this thesis is to be able to present a model of Covid-19 in terms of surrounding areas in San Luis Obispo including the unique mobility dynamic currently held in the global pandemic. Furthermore, we use our model to explore different methods of ensuring a low infection rate such as isolation methods and mobility restrictions.

This dissertation is a collection of several models to understand airline and passenger dynamics in the National Airspace System (NAS). Agent-based modeling is one of the most widely used modeling simulation-analysis approaches to understanding the dynamic behavior of complex systems. The usefulness of agent-based modeling has been demonstrated by simulating the complex interactions between airlines, travelers, and airports of a small-scale transportation system. Three airlines, one low cost and two network airlines are simulated to examine how each airline behaves over time to maximize their profit margins for a given passenger demand and operation cost structure. Passenger mode choice and itinerary choice sub modules are embedded in the framework to characterize traveler agent's response to the evolved airline schedule. An airport delay model was implemented to estimate the average delay at each airport. The estimated delay fed into the mode choice and itinerary choice models to update the travel time related variables. International passenger demand is a very important component of the air transportation system in the United States. The proportion of international enplanements relative to total enplanements increased from 8% in 1990 to 11% in 2008. Nine linear regression models are developed to forecast the enplanements from the United States to each of nine international regions. The international enplanements from the CONUS to each world region are modeled as a function of GDP and GDP per capita of both the United States and the specific region. A dummy variable is also used to account for the effects of September 11, 2001. The total number of international enplanements is forecast to increase from 74.7 million in 2008 to 184.4 million in 2028. The average annual growth rate is expected to be 4.7%. The European Union – United States Open Skies Agreement, which became effective March 30, 2008. Mathematical models are developed to forecast the effect of EU-US Open Skies Agreement on commercial airline passenger traffic over the North Atlantic Ocean. Nine econometric models were developed to forecast passenger traffic between the United States and nine selected European countries between 2008 through 2020. 68 new nonstop flights between the United States airports and the European airports are predicted by the model in 2020 using the airport pair passenger demand forecast. London, Heathrow is demonstrated as an example for rerouting the excess air travel passengers from one airport to other airports when the airport operational capacity is exceeded. The proportion of international enplanements relative to total enplanements within CONUS increased from 8% in 1990 to 11% in 2008. 51% of the sampled international and U.S. territories passengers served by U.S. carriers had at least one domestic coupon in 2007. The number of DOI passengers through airport-pairs in each of the historical years (1990-2007) is estimated based on the adjusted 100% international itineraries including pure international itineraries plus the non-CONUS itineraries. The total number of DOI enplanements is estimated to grow from 37.3 million in 1990 to 79.4 million in 2007. 193 CONUS airports are estimated to have at least 10,000 DOI enplanements in 2007. The number of DOI enplanements is forecast to grow from 79.4 million in 2007 to 206.2 million in 2030 with average growth rate of 4.2% per year. In recent years, there has been an increasing use of secondary airports both in Europe and the U.S. Regional airports have long been considered as a possible source of relief to reduce airport congestion at the hub airport and to efficiently accommodate future air travel demand. The conditions under which the secondary airports develop in a metropolitan area are examined. Fifteen multi-airport systems including 19 Operational Evolution Plan airports and 25 active secondary airports are identified in the National Airspace System. Diverse trends of traffic distribution among airports in the same metropolitan area are observed. We observed that the number of markets served at the secondary airports is less than that at the primary airport in the same metropolitan area. Most of the secondary airports are currently dominated by the low-cost carriers. The share of seats supplied by the low-cost carriers at the secondary airports has increased during the period 1990-2008. Full service carriers concentrate their service mainly on the primary airport in all the multi-airport systems analyzed. The average seat capacity per aircraft at the secondary airports is higher than that of primary airports in most of the multi-airport systems. The secondary airports mainly serve the domestic O&D passengers. / Ph. D.

This dissertation is composed of three essays. In the first essay, I construct a supply and demand model for crude oil markets. I then fit the model to historical price and quantity data to be able to project future oil prices. Ex-post forecasts using this model predict historical price trends more accurately than most oil forecasting models. The second essay incorporates the supply and demand model from the previous paper into a complex systems model that also includes oil futures markets. Adaptive-agent investors in futures markets choose from a set of rules for predicting future prices that includes the rational expectations equilibrium rule, as well as rules that rely on more short-term information. The set of available rules evolves following a genetic algorithm; agents choose which rules to follow based on their past performance. While outcomes vary depending on the specific assumptions made, under a plausible set of assumptions investors can fail to anticipate shortages properly, leading to significant price spikes that would not occur in the rational expectations equilibrium. The last essay addresses the impacts of carbon cap-and-trade policies on consumers. I calculate how higher carbon prices would affect the prices of different consumer goods, how consumers would respond to the price changes, and how the price changes, along with revenue recycling, would impact consumers of different income levels.

Transport simulation models play a crucial role in transportation planning, design, and operations, allowing for the replication of various scenarios through the incorporation of real-world data and parameters. Recently, agent-based transport models have gained prominence for their ability to simulate intricate metropolitan transport systems. These models take into account the distinct characteristics, decision-making processes, and interactions of individual agents. Among the array of agent-based transport models, MATSim stands out as a potent and adaptable tool for modeling transportation systems. A critical aspect of MATSim’s input preparation involves assigning activity location points using land use raster data. However, the characteristics of land use raster data present limitations in certain urban case studies such as Stockholm. In response, some researchers have turned their attention to buildings shapefile data, a commonly used geospatial data format. This study aims to improve the activity location assignment model by developing an evaluation workflow of model uncertainty for different geospatial input data in MATSim and empirically analyzing their impacts on simulation outcomes. Despite acknowledging data availability and activity representation limitations, the study’s results demonstrate that utilizingbuildings shapefiles as input data yields more consistent outcomes with reduced uncertainty. This suggests the promising potential of buildings shapefiles as a favorable data source for transportation modeling and planning within the studied scenarios.

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