<p>The aging water infrastructure system in the United States has posed considerable hindrance to policy-makers as they seek to provide safe, reliable, and clean drinking water for communities. The deterioration of the physical water infrastructure negatively affects the economics of water utilities and can lead to increases in water rates for consumers, so that utilities can recover the financial losses. However, the dynamics emerging from the interactions among changes in water service reliability, water-rates, consumer behavior (with respect to water consumption and willingness to support water-rate changes in response to changes in water rates, and water utility economics, are still unknown factors in the management of water infrastructure systems. </p> <p>The overarching objective of this dissertation is the creation and demonstration of the dynamics of coupled human and water infrastructure systems under conditions of water main breaks and water-rate increases. First, using water-main break data for a 21-year period from two U.S. cities in the Great Lakes region, the dissertation demonstrates a methodology to estimate the system-wide monthly frequency of water main breaks as a function of a number of explanatory variables. Using a random-parameters negative-binomial approach, the statistical estimations show that pipe diameters, average pipe age, distribution of pipe age, pipe material, time of year, and mean monthly temperature all have a significant impact on monthly water main break frequencies. The results can assist asset managers in quantifying the effect of factors may have on the likelihood of water main breaks, as well as in making cost-effective decisions regarding pipe renewal.</p> <p>Next, by incorporating qualitative survey data and using quantitative econometric methods, consumer behaviors in responses to the water-rate increases, and based on perceptions of water service reliability and quality in a Midwestern U.S. city was evaluated. Using a multivariate binary probit approach, the results provide insights as to how individuals are likely to respond to water-rate increases based on the reliability of current water services and the quality of the supplied water. The outputs of the econometric enable utility managers to better understand the behavior of consumers under different rate conditions and help water utilities in their long-term and short-term financial analyses.</p> <p>Finally, the aforementioned two components are integrated into the interdependency analysis to evaluate the interactive effects of features of the physical water infrastructure (pipeline characteristics, water and associated energy losses, and the revenue loss for water utilities) and the behavior of stakeholders (water utilities and consumers). The developed hybrid system dynamics and agent-based model examines interdependencies between the physical water infrastructure, the water utility, and the water consumers to explore possible emergent behavior patterns of water users during water rate increases over time. The model is demonstrated over the 2001–2010 period on a case study city with a large water distribution system that includes 4,000 miles of pipeline and nine water treatment plants serving a population of 863,000. This model was then verified and validated throughout the development of simulation models and included the following steps: 1) data validity, 2) conceptual model validity, 3) computerized model validity, and 4) operational validity. The results suggest the simulated behavior of the model was reasonable and the output of the simulation model regrading water main break frequency, amount of water and associated energy losses, generated revenue, and payoff periods for implementing proactive maintenance strategies had the accuracy required for the model’s intended purpose. </p> <p>The framework developed in this doctoral study can be applied to different size classifications of cities, as well as different classifications of utility companies (such as electricity and gas) by updating the parameters in the model to reflect the characteristics of the infrastructure system components. The distinctive methodological approach in this doctoral work could capture the emergent behaviors of human-water infrastructure interactions such as the impact of increasing water-rates on residential consumers, the impact of water price elasticity cascading into the water utility revenue, and the impact of residential consumers’ water consumption on water utility revenues. In conclusion, the results of this doctoral research can assist asset managers in understanding their systems, identify pathways for growing revenue through reducing non-revenue water and increasing water-rates, and implementing a proactive pipeline asset management program towards the provision for safe, reliable, and clean drinking water.</p>

Game balance has been a challenge for game developers since the time games have become more complex. There have been a handful of proposals for game balancing processes outside the manual labor-intensive play testing methods, which most game developers often are forced to use simply due to the lack of better methods. Simple solutions, like restrictive game play, are limited because of their inability to provide insight on interdependencies among the mechanisms in the game. Complex techniques framed around the potential of AI algorithms are limited by computational budgets or cognition inability to assess human actions. In order to find a middle ground we investigate Game Theory and Mechanism Design concepts. Both have proven to be effective tools to analyse strategic situations among interacting participants, or in this case `players'. We test the feasibility of using these techniques in an Real Time Strategy (RTS) game domain to understand game balance. MicroRTS, a small and simple execution of an RTS game is employed as our model. The results provide promising insight on the effectiveness of the method in detecting imbalances and further inspection to find the cause. An additional benefit out of this technique, besides detecting for game imbalances, the approach can be leveraged to create imbalances. This is useful when the designer or player desires to do so.

Rising concerns related to the effects of traffic congestion have led to the search for alternative transportation solutions. Advances in battery technology have resulted in an increase of electric vehicles (EVs), which serve to reduce the impact of many of the negative consequences of congestion, including pollution and the cost of wasted fuel. Furthermore, the energy-efficiency and quiet operation of electric motors have made feasible concepts such as Urban Air Mobility (UAM), in which electric aircraft transport passengers in dense urban areas prone to severe traffic slowdowns. Electrified transportation may be the solution needed to combat urban gridlock, but many logistical questions related to the design and operation of the resultant transportation networks remain to be answered. This research begins by examining the near-term effects of EV charging networks. Stationary plug-in methods have been the traditional approach to recharge electric ground vehicles; however, dynamic charging technologies that can charge vehicles while they are in motion have recently been introduced that have the potential to eliminate the inconvenience of long charging wait times and the high cost of large batteries. Using an agent-based model verified with traffic data, different network designs incorporating these dynamic chargers are evaluated based on the predicted benefit to EV drivers. A genetic optimization is designed to optimally locate the chargers. Heavily-used highways are found to be much more effective than arterial roads as locations for these chargers, even when installation cost is taken into consideration. This work also explores the potential long-term effects of electrified transportation on urban congestion by examining the implementation of a UAM system. Interdependencies between potential electric air vehicle ranges and speeds are explored in conjunction with desired network structure and size in three different regions of the United States. A method is developed to take all these considerations into account, thus allowing for the creation of a network optimized for UAM operations when vehicle or topological constraints are present. Because the optimization problem is NP-hard, five heuristic algorithms are developed to find potential solutions with acceptable computation times, and are found to be within 10% of the optimal value for the test cases explored. The results from this exploration are used in a second agent-based transportation model that analyzes operational parameters associated with UAM networks, such as service strategy and dispatch frequency, in addition to the considerations associated with network design. General trends between the effectiveness of UAM networks and the various factors explored are identified and presented.

De grandes transformations écologiques et climatiques sont aujourd'hui à l’œuvre. Elles sont sources d’instabilité environnementale, à l’image d’évènements climatiques extrêmes devenus plus fréquents, plus intenses, et touchant de nouvelles régions du globe. A défaut de pouvoir empêcher ces changements, comment les sociétés humaines pourraient-elles s'y adapter ? Pour beaucoup de chercheurs et de décideurs, c’est par la résilience qu’elles y parviendront. Ce concept semble renfermer des solutions nouvelles, adaptées à un monde turbulent et incertain. Par définition, les systèmes résilients sont capables de rebondir face à des chocs inattendus, d’apprendre rapidement et de s'adapter à des conditions inédites. Malgré l’intérêt suscité par cette notion, les processus qui permettent à une société d’être résiliente restent encore mal connus. Cette thèse développe un cadre conceptuel nouveau permettant, via la modélisation mathématique, d'explorer les liens théoriques entre mécanismes économiques et résilience. Ce cadre repose sur une analyse critique de la résilience en écologie — domaine d’origine du concept — et en économie — notre champ d’application. Nous l’appliquons aux systèmes de production économique, modélisés comme des réseaux de firmes et analysés à travers la théorie des systèmes dynamiques. Cette thèse évalue l’aptitude de tels modèles, dits multi-agents, à générer des profils de bifurcations, étape incontournable de l’analyse mathématique de la résilience. Nous étudions pour cela une dynamique proie–prédateur très générale en écologie et en économie. Ensuite, cette thèse s'attaque à un facteur majeur qui entrave la résilience : les fortes interdépendances entre activités économiques, par lesquelles les retards et interruptions de production se propagent d’une entreprise à l’autre. En utilisant des réseaux de production réalistes, nous montrons comment les délais d'approvisionnement, lorsque intégrés dans des topologies particulières, démultiplient ces phénomènes de propagation. Ensuite, grâce à un modèle évolutionnaire, nous mettons en lumière l’existence d’un risque systémique : les cascades d’incidents ont lieu alors même que tous les agents possèdent des inventaires adaptés au niveau de risque. Ce phénomène s’amplifie lorsque les chaînes d'approvisionnement se spécialisent et se fragmentent. Ces résultats théoriques ont une valeur générale, et pourront servir à orienter de futures recherches empiriques. Cette thèse fait en outre avancer les connaissances sur des méthodes et objets mathématiques très récents, comme les équations booléennes à retard formant un réseau complexe, et les dynamiques évolutionnaires sur les graphes. Les modèles et le cadre conceptuel proposés ouvrent de nouvelles perspectives de recherche sur la résilience, en particulier sur l’impact des rétroactions environnementales sur l'évolution structurelle des réseaux de production. / A wide range of climatic and ecological changes are unfolding around us. These changes notably manifest themselves through an increased environmental variability, such as shifts in the frequency, intensity, and spatial distribution of weather-related extreme events. If human societies cannot mitigate these transformations, to which conditions should they adapt? To many researchers and stakeholders, the answer is resilience. This concept seems to subsume a variety of solutions for dealing with a turbulent and uncertain world. Resilient systems bounce back after unexpected events, learn novel conditions and adapt to them. Theoretical models, however, to explore the links between socioeconomic mechanisms and resilience are still in their infancy. To advance such models, the present dissertation proposes a novel conceptual framework. This framework relies on an interdisciplinary and critical review of ecological and economic studies, and it is based on the theory of dynamical systems and on the paradigm of complex adaptive systems. We identify agent-based models as crucial for socioeconomic modeling. To assess their applicability to the study of resilience, we test at first whether such models can reproduce the bifurcation patterns of predator–prey interactions, which are a very important factor in both ecological and economic systems. The dissertation then tackles one of the main challenges for the design of resilient economic system: the large interconnectedness of production processes, whereby disruption may propagate and amplify. We next investigate the role of delays in production and supply on realistic economic networks, and show that the interplay between time delays and topology may greatly affect a network’s resilience. Finally, we investigate a model that encompasses adaptive responses of agents to shocks, and describes how disruptions propagate even though all firms do their best to mitigate risks. In particular, systemic amplification gets more pronounced when supply chains are fragmented. These theoretical findings are fairly general in character and may thus help the design of novel empirical studies. Through the application of several recent ideas and methods, this dissertation advances knowledge on innovative mathematical objects, such as Boolean delay equations on complex networks and evolutionary dynamics on graphs. Finally, the conceptual models herein open wide perspectives for further theoretical research on economic resilience, especially the study of environmental feedbacks and their impacts on the structural evolution of production networks.

Educational systems are complex adaptive systems (CAS). The macroeffects of an educational policy emerge from and depend on individual students' reactions to the policy. However, educational policymakers traditionally rely on equation-based models, which are deficient in reflecting the work of microbehaviors. Using inappropriate tools to make policies may be a reason why there were many unintended educational consequences in history. A proper methodology to design and analyze policies for complex educational systems is agent-based modeling (ABM). Grounded in the theories of CAS and computational irreducibility, ABM is capable of connecting microbehaviors with macropatterns. The purpose of this study was to contribute to the application of ABM in educational policy analysis by constructing an agent-based overlapping generations model with hypothesized inputs to qualitatively represent the environment of the Taipei School District. Four research questions explored the effects of Taipei's 2016 student-assignment mechanism and its free tuition policy on educational opportunity and school quality under different assumptions of students' school-choice strategies. The simulated outputs were analyzed using descriptive statistics and paired samples t tests. The findings, which could hardly be revealed by traditional models, showed that the effects were complex and depended on students' strategies along with the number of choices students were allowed to make; the assignment outcomes for elite students were robust to the mechanism, and the free tuition policy worsened school quality. Although exploratory, these findings can serve as hypotheses and a guide for Taipei's policymakers to collect empirical data in evaluating their 2016 mechanism and tuition policy.

<div>Disaster debris management is critical to the success of disaster recovery systems. While there are multiple disaster mitigation strategies and post-disaster debris management plans, it is hard to implement because of: (i) the uniqueness of disaster incidents and randomness of its impacts; (ii) complexity of disaster debris removal operations, policy and regulations and (iii) interdependency of multiple infrastructure networks. Also, delayed debris removal operation affects following emergency response activities. Furthermore, uncontrolled debris removal activities can result in significant environmental and public health consequences. Therefore, there is a need for a systematic approach to optimizing post-disaster debris management systems. </div><div><br></div><div>This research is aimed to understand the complexity of debris management and associated emergent dynamics through the lens of an adaptive system-of-systems (SoS). To develop the adaptive decision support system, this research (a) identifies the interdependent infrastructure network within a community and its relative importance; (b) develops real-time GIS database to integrate the data associated with critical infrastructure and geographical characteristics in the community map; (c) designs and selects a TDMS network to analyze the required number, capacity and resources, based on engineering-technical, managerial, and social-political dynamics; (d) simulate the productivity of debris-management SoS based on the real-time GIS database to gain insight into the impact of the dynamical nature of a disaster-affected area; and (e) develop a visualized interactive GIS-based platform for debris management to communicate real-time debris clearance strategies and operations among different agencies and organizations.</div><div><br></div><div>To evaluate the proposed framework and decision support system, this research conducted a case study, debris removal operation in the city of Baton Rouge, after the 2016 Louisiana flood. The results demonstrated the influence of sub-systems such as TDMS locations and capacity, road network condition, available resources, existing regulations and policies, characteristics of community on the behavior of the entire disaster debris removal management as a whole. </div><div><br></div><div>The proposed decision support system for effective disaster debris management will be beneficial for emergency agencies and disaster-prone communities to evaluate and optimize their disaster debris management system. Also, the system can be systematically integrated with other emergency response systems to maximize the efficiency of the entire disaster responses during post-disaster situations. </div><div><br></div>

Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years. Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion. It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed. As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation.

No description available.

The scientific consensus has for long been that residential segregation is best conceived of as a multidimensional phenomenon that can exist on several geographical scales (Massey &amp; Denton, 1988; Lee et al., 2008; Reardon &amp; O’Sullivan, 2004; Reardon et al., 2008). Despite this deepened understanding of residential segregation and how to best measure it, theoretical models of segregation processes have tended to disregard the diversity of dimensions and scales of segregation. Moreover, while residential segregation is broadly defined as the spatial separation of people of different social groups (Timberlake &amp; Ignatov, 2014), the frequently used Schelling model is aspatial (Schelling, 1971). In contrast, the lesser-known Sakoda model incorporates a distance-decay effect and is thus explicitly spatial (Sakoda, 1971). The aim of this thesis was to evaluate two theoretical agent-based models of segregation processes—the Schelling- and Sakoda model—by measuring the segregation patterns they generate under different parameter settings across four dimensions and six spatial scales of segregation, ranging from the micro- to the macro-scale. Thus, providing an assessment of the capacity of these models to generate (grow) different forms of residential segregation. Results from simulation experiments showed that the popular Schelling model was limited in its capacity to generate different forms of segregation. In its standard configuration it could generate micro-segregation along two out of four dimensions: Evenness and Exposure. The spatially explicit Sakoda model was able to generate segregation patterns which varied substantially across all scales on the Evenness and Exposure dimensions. In addition, it was able to generate varied patterns of Concentration and Centralization under certain parameter settings. These findings contribute new insights to the possibilities afforded by these two models in modeling processes of residential segregation. If the goal for theoretical models is to generate segregation patterns which vary across all dimensions and scales of residential segregation, then the standard configuration of the Schelling model is not enough. This thesis suggest that the Sakoda model is a promising candidate for this purpose. In addition, this thesis shows the importance of using a comprehensive measurement framework in theoretical modeling of segregation processes.

Studying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans' actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors. Studies on norms are much older than the age of computer science, since normative studies have been a classic topic in sociology, psychology, philosophy and law. Various theories have been put forth about the functioning of social norms. Although an extensive amount of research on norms has been performed during the recent years, there remains a significant gap between current models and models that can explain real-world normative behaviors. Most of the existing work on norms focuses on abstract applications, and very few realistic normative simulations of human societies can be found. The contributions of this dissertation include the following: 1) a new hybrid technique based on agent-based modeling and Markov Chain Monte Carlo is introduced. This method is used to prepare a smoking case study for applying normative models. 2) This hybrid technique is described using category theory, which is a mathematical theory focusing on relations rather than objects. 3) The relationship between norm emergence in social networks and the theory of tipping points is studied. 4) A new lightweight normative architecture for studying smoking cessation trends is introduced. This architecture is then extended to a more general normative framework that can be used to model real-world normative behaviors. The final normative architecture considers cognitive and social aspects of norm formation in human societies. Normative architectures based on only one of these two aspects exist in the literature, but a normative architecture that effectively includes both of these two is missing.