Floods are one of the most costly natural hazards worldwide, affecting millions of people every year. Flood risk management is of global concern, and a deeper understanding of dynamic flood risk development is needed. Currently,vulnerability and exposure are often assumed to be constant in quantitative flood risk assessments, which does not reflect patterns observed in real life. In fact, flood protection measures on individual and community level can induce changes in both vulnerability and exposure, as well as alter river and floodplain hydraulics. The human-flood system is complex, incorporating two-way interactions between both subsystems. To build up these dynamics from the bottom up with a focus on the role of the individual, an agent-based model was combined with a hydraulic model. It was shown that this coupled model is capable of replicating levee and adaptation effects which are commonly knownto occur in the context of river floods and flood protection measures. Moreover,the new modeling approach can explicitly simulate the spatial distribution of flood risk which allowed for an analysis of conflicting interests in urban and rural areas. Here, model outcomes suggest that a shift of flood risk from high-value urban to lower-value rural areas can reduce system-wide flood losses. However, decreasing flood awareness in the city will push population growth rates, and discontent in rural areas might nally induce a shift of higher floodrisk back to the urban area. In the end, one low-probability high-intensity event might cause a disastrous outcome.

This dissertation consists of three articles about colorism and its relevance to public administration (PA), with specific focus on local policing. The overarching arguments are: 1) our lack of focus on the nuanced factors related to race have hindered our ability to adequately respond to biased criminal justice (CJ) outcomes; and 2) there are hidden patterns of biased behaviors that originate at the street-level, and these patterns have the propensity to impact every aspect of CJ. Colorism could serve as a more comprehensive approach to addressing racial bias. Colorism is a system of disadvantage and privilege based on skin color, with a bias for lighter skin. Article I introduces colorism to the field of PA, and it uses data on workplace colorism complaints to illustrate how colorism currently intersects with PA. In addition, the article uses scenarios to demonstrate the potential impact colorism might have at the street-level. Article II builds upon the descriptions of colorism provided in Article I by simulating a conceptual model of colorism and local policing. The major finding of this study is that, counter to the expectations of the experiment, those in the middle of the skin color spectrum experienced higher rates of incarceration when aggressive steps were taken to counter colorism. The major contributions from this project include a conceptual model that describes the relationship between the distinct levels of colorism�"individual, interactive and institutional. In Article III, I explore two conceptual models of interactive colorism in a local policing context. In the first model, bias behaviors are less likely to receive a challenge. In the second model, biased behaviors are likely to be challenged by counter-behaviors (e.g., fair policing). Subject-matter experts and non-subject-matter experts were used to select the model that most accurately depicts the phenomenon. I used online focus groups and phone interviews with police officers, theorists (e.g., sociologists, psychologists), and non-subject-matter experts to gather feedback. Majority of the informants recommended that future research on interactive colorism be framed as a bidirectional phenomenon. The informants provided additional considerations for future research, such as the variation in police culture across police departments. / PHD

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Ecological systems are difficult to understand, let alone predict. The reason is their enormous complexity that arises from numerous organisms interacting with each other and their environment in a multitude of ways. However, this understanding is crucial to secure a plentitude of services that are provided by ecological systems. A substantial proportion of these services are carried out by microorganisms such as bacteria, fungi, and archaea. For example, microorganisms contribute to degradation of organic matter, water purification, and even regulation of the global climate. Therefore, a thorough understanding of the ecology of microorganisms is particularly relevant for our future well-being. While microorganisms are comparatively well-suited for experimental studies, owing also to recent technological advances in molecular biology, it is necessary to apply theory and modeling in order to fully benefit from the empirical data. A widely used theoretical method in microbial ecology is individual-based modeling, in which population or community dynamics emerge from the behavior and interplay of individual entities that are simulated according to a predefined set of rules. However, existing individual-based models of microbial communities are often specialized on particular research questions or require proficiency in specific programming languages or software. These limitations can be hampering for a broad and systematic application of individual-based modeling in microbial ecology. For this thesis, McComedy, a framework and software tool for the creation and running of individual-based models of microbial consumer-resource systems, was developed. It allows for fast and user-friendly model development by flexibly combining pre-implemented building blocks that represent physical, biological, and evolutionary processes. The ability of McComedy to capture the essential dynamics of microbial consumer-resource systems was demonstrated by reproducing and furthermore adding to the results of two distinct studies from the literature. McComedy was furthermore applied to study the evolution of metabolic interactions between bacteria. More specifically, it was assessed whether cooperative exchange of costly metabolites can evolve in bacterial multicellular aggregates. The results indicate that this is in principle possible, however, it depends on the mechanism by which the metabolites are exchanged. If metabolites are exchanged via diffusion through extracellular space, cooperation is not expected to evolve. On the other hand, if metabolites are transferred by contact-dependent means, for instance via intercellular nanotubes, cooperation is likely to evolve. Overall, contributions from this thesis comprise, first, a user-friendly modeling tool that can be used by microbial ecologists, second, insights into the evolution of metabolic interactions in bacterial systems, and, third, awareness of how the mechanistic consideration of a process can drastically affect the outcome of a modeling study.

The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways. / Doctor of Philosophy / The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.

As organizations utilize less hierarchical forms of leadership, the study of how leadership emerges within teams continues to grow in importance. Despite many theoretical perspectives used to study leadership emergence, little is understood about the actual process by which a collective structure emerges. In the current work, I address two of the primary limitations within this literature: imprecise theoretical perspectives and methodological challenges in studying emergence. Specifically, although there are many conceptual works that describe the leadership emergence process, these descriptions do not have enough precision to be able to design a model with formal rules, a necessary requirement for studying emergence. Additionally, studying leadership emergence requires the study of newly formed teams frequently over time, which is challenging to accomplish using existing methods. To address the two above limitations, in the current work, I translate two dominant process-oriented perspectives of leadership emergence (social interactionist and social cognitive) into formal theories that include a series of testable hypotheses. In doing so, these theories outline the essential elements and process mechanisms of each theoretical perspective. Next, I use these theories to design two agent-based models to simulate the process by which leadership emerges within teams, under each perspective. Using the software NetLogo, I simulate 500 newly formed teams over the initial period of 500 dyadic interactions (i.e., hours). Finally, after simulating these models, I use the resulting data to test the predictions from each theoretical perspective. In addition to testing the hypotheses from each model, I also utilize agent-based modeling to systematically test the relative importance of the unique individual-level elements and process mechanisms from each model. From this entire process, I generate results about (1) how well the agent-based models represent the respective perspectives, and (2) the relative influence each perspective's unique elements and mechanisms have on team outcomes. Overall, results generally supported the core concepts from each perspective, but also identified areas where each perspective needs to revisit for theory on leadership emergence to advance. Specifically, the results illustrated that certain individual-level elements were most influential for leadership emergence. For the social interactionist perspective, it was the comparison between implicit leadership theories and self-prototypical leadership characteristics. For the social cognitive perspective, it was leader self-schemas. Additionally, results indicated that future work may need to revisit the conceptualization of both leadership structure schemas, as well as the dynamic process of weighting implicit leadership theories. Finally, predictions about the rate of leadership emergence over time within the social cognitive perspective were the only predictions that were not supported. From these results, I present multiple themes as a conceptual road map for the advancement of leadership emergence theory. I argue that the lack of support regarding leadership emergence trajectories presents opportunities for a reconceptualization of emergence at the event level, as well as new modeling procedures to capture emergence as it occurs. I also present future study ideas that can directly test the competing assumptions from each perspective. In total, I argue that this work advances the study of leadership emergence by adopting a method that helped integrate two dominant perspectives of leadership emergence, possibly laying the groundwork for the development of a combined formal theory. / Doctor of Philosophy / The purpose of this dissertation was to understand how specific individuals in teams become viewed as a leader, when there is no formal hierarchy. This represents the process of leadership emergence. Most research studying leadership in teams focuses on who becomes a leader. As a result, little is known about the exact process by which certain individuals emerge as a leader. Fortunately, there are theories that represent potential ideas for how this process occurs. However, these theories are difficult to test, as this type of research requires the study of newly formed teams over time, a great methodological challenge. In my dissertation, I attempt to address this challenge by simulating newly formed teams over time using a form of computer simulation called Agent-Based Modeling (ABM). In using ABM, I aimed to learn how two theoretical perspectives both compare and contrast to one another, in how they both explain the process of leadership emergence. In my primary analysis, I simulated 500 teams, working together over a period of hours. After using this data to test a series of predictions, I found that most predictions were supported across each theoretical perspective. This provided evidence that the simulations represented each theoretical perspective. However, the results also showed that certain parts of each theoretical perspective need more research. In recognizing the weaknesses in each perspective in modeling leadership emergence, I introduce multiple opportunities for theoretical integration, in that ideas from both models can be combined into one. Therefore, the findings from this research lay the groundwork for the development of one single theory for how leadership emerges in groups. Ultimately, this could help understand how leadership in teams occurs, which can lead to new interventions to improve team leadership and performance.

This dissertation investigates the evacuees' behavior under hurricane evacuation conditions and develops an agent-based travel demand model system for hurricane evacuation simulation using these behavioral findings. The dissertation econometrically models several important evacuation decisions including evacuate-stay, accommodation type choice, evacuation destination choice, evacuation mode choice, departure time choice, and vehicle usage choice. In addition, it explicitly considers the pre-evacuation preparation activities using activity-based approach. The models are then integrated into a two-module agent-based travel demand model system. The dissertation first develops the evacuate-stay choice model using the random-coefficient binary logit specification. It uses heterogeneous mean of the random parameter across households to capture shadow evacuation. It is found that the likelihood of evacuation for households that do not receive any evacuation notice decreases as their distance to coast increase on average. The distance sensitivity factor, or DSF, is introduced to construct the different scenarios of geographical extent of shadow evacuation. The dissertation then conducts statistical analysis of the vehicle usage choice. It identifies the contributing factors to households' choice of the number of vehicles used for evacuation and develop predictive models of this choice that explicitly consider the constraint imposed by the number of vehicles owned by the household. This constraint is not accommodated by ordered response models. Data comes from a post-storm survey for Hurricane Ivan. The two models developed are variants of the regular Poisson regression model: the Poisson model with exposure and right-censored Poisson regression. The right-censored Poisson model is preferred due to its inherent capabilities, better fit to the data, and superior predictive power. The multivariable model and individual variable analyses are used to investigate seven hypotheses. Households traveling longer distances or evacuating later are more likely to use fewer vehicles. Households with prior hurricane experience, greater numbers of household members between 18 and 80, and pet owners are more likely to use a greater number of vehicles. Income and distance from the coast are insignificant in the multivariable models, although their individual effects have statistically significant linear relationship. However, the Poisson based models are non-linear. The method for using the right-censored Poisson model for producing the desired share of vehicle usage is also provided for the purpose of generating individual predictions for simulation. The dissertation then presents a descriptive analysis of and econometric models for households' pre-evacuation activities based on behavioral intention data collected for Miami Beach, Florida. The descriptive analysis shows that shopping - particularly food, gasoline, medicine, and cash withdrawal - accounts for the majority of preparation activities, highlighting the importance of maintaining a supply of these items. More than 90% of the tours are conducted by driving, emphasizing the need to incorporate pre-evacuation activity travel into simulation studies. Households perform their preparation activities early in a temporally concentrated manner and generally make the tours during daylight. Households with college graduates, larger households, and households who drive their own vehicles are more likely to engage in activities that require travel. The number of household members older than 64 has a negative impact upon engaging in out-of-home activities. An action day choice model for the first tour suggests that households are more likely to buy medicine early but are more likely to pick up friends/relatives late. Households evacuating late are more likely to conduct their activities late. Households with multiple tours tend to make their first tour early. About 10% of households chain their single activity chains with their ultimate evacuation trips. The outcomes of this paper can be used in demand generation for traffic simulations. The dissertation finally uses the behavioral findings and develops an agent-based travel demand model system for hurricane evacuation simulation, which is capable of generating the comprehensive household activity-travel plans. The system implements econometric and statistical models that represent travel and decision-making behavior throughout the evacuation process. The system considers six typical evacuation decisions: evacuate-stay, accommodation type choice, evacuation destination choice, mode choice, vehicle usage choice and departure time choice. It explicitly captures the shadow evacuation population. In addition, the model system captures the pre-evacuation preparation activities using an activity-based approach. A demonstration study that predicts activity-travel patterns using model parameters estimated for the Miami-Dade area is discussed. The simulation results clearly indicate the model system produced the distribution of choice patterns that is consistent with sample observations and existing literature. The model system also identifies the proportion of the shadow evacuation population and their geographical extent. About 23% of the population outside the designated evacuation zone would evacuate. The shadow evacuation demand is mainly located within 3.1 miles (5 km) of the coastline. The output demand of the model system works with agent-based traffic simulation tools and conventional trip-based simulation tools. The agent-based travel demand model system is capable of generating activity plans that works with agent-based traffic simulation tools and conventional trip-based simulation tools. It will facilitate the hurricane evacuation management. / Ph. D.

Gillespie's stochastic simulation algorithm (SSA) has been a conventional method for stochastic modeling and simulation of biochemical systems. However, its population-based scheme faces the challenge from multi-state situations in many biochemical models. To tackle this problem, Morton-Firth and Bray's stochastic simulator (StochSim) was proposed with a particle-based scheme. The thesis first provides a detailed comparison between these two methods, and then proposes improvements on StochSim and a hybrid method to combine the advantages of the two methods. Analysis and numerical experiment results demonstrate that the hybrid method exhibits extraordinary performance for systems with both the multi-state feature and a high total population. In order to deal with the combinatorial complexity caused by the multi-state situation, the rules-based modeling was proposed by Hlavacek's group and the particle-based Network-Free Algorithm (NFA) has been used for its simulation. In this thesis, we improve the NFA so that it has both the population-based and particle-based features. We also propose a population-based method for simulation of the rule-based models. The bacterial chemotaxis model has served as a good biological example involving multi-state species. We implemented different simulation methods on this model. Then we constructed a graphical interface and compared the behaviors of the bacterium under different mechanisms, including simplified mathematical models and chemically reacting networks which are simulated stochastically. / Master of Science