In a roadway network, infrastructure conditions determine efficient network operation and traveler safety, and thus roadway engineers need a sophisticated plan to monitor and maintain network performance. Developing a comprehensive maintenance and rehabilitation (M&R) strategy for an infrastructure system, specifically a roadway network, is a complicated process because of the system uncertainties and multiple parties involved. Traditional approaches are mostly top-down, and restrict the decision-making process. In contrast, agent-based models, a bottom-up approach, could well simulate and analyze the autonomy of each party and their interactions in the infrastructure network. In this thesis, an agent-based model prototype was developed to simulate the operations of a small roadway network with a high degree of simplification. The objective of this study is to assess the applicability of agent-based modeling for infrastructure management problems through the following four aspects: (1) to simulate the user route selection process in the network; (2) to analyze the impact of users’ choices on the congestion levels and structural conditions of roadway sections; (3) to help the engineer to determine M&R strategies under a certain budget; and (4) to investigate the impact due to different fare rates of the toll road section on the infrastructure conditions in the network. This prototype detected traffic flow, and gave appropriate M&R advice to each roadway segment. To improve this model, more investigation should be conducted to increase the level of sophistication for the interaction rules between agents, the route selection, and the budget allocation algorithm. Upon completion, this model can be applied to existing road networks to assist roadway engineers in managing the network with an efficient M&R plan and toll rate. / text

Social-ecological systems (SES) may become “locked in” particular states or configurations due to various constraints on adaptability imposed by feedback mechanisms or by processes designed to incentivize certain behavior. While these locked-in states may be desirable and robust to disturbances over relatively short time periods, limits on system adaptations may diminish the longer-term resilience of these states, and potentially of the system itself. The agricultural SES in the Iowa-Cedar River Basin in eastern Iowa is one such system. While highly productive, culturally important, and essential to local economies, the system is facing significant economic and environmental challenges. This dissertation presents the results of a project designed to survey the adaptability of farmers in the ICRB, model their actions subject to constraints, and plot potential future states under scenarios of climate change, policy, and market conditions. We utilize a coupled agent-based model (ABM) to examine the specified resilience of the system to future climate, leveraging the ability of ABMs to integrate heterogeneous actors, dynamic couplings of natural and human systems, and processes across spatiotemporal scales. We find that farmer behavior is primarily constrained by economic factors, including federal crop insurance subsidies and the financial risk of implementing different crops or practices. Finally, we generate alternative system trajectories by modeling twenty-one scenarios, identifying actionable adaptations and pathways for transforming the system to alternative, more sustainable states.

With the proliferation of new technological alternatives to the traditional classroom, it becomes increasingly important understand the role that innovative technologies play in learning. Computer environments for learning to teach have the potential to be innovative tools that improve the skill and effectiveness of pre-service and in-service teachers. There is a tacit sense in such environments that “realism” is best created through, and associated with, a kind of pictorial literalism. I designed a computer model (the Direct Instruction tool) that, though simple, appears realistic to many users and thus contradicts that sense of literalism. I also propose a theoretical classification of computer representations based on the relationship (or lack thereof) between perceived usefulness or relevance and realism. In this study, I investigate two questions: 1) What are the kinds of claims or insights that respondents generate in relation to using the DI tool to organize their experiences? 2) How do the functionalities of the DI tool fit with or support what respondents see as meaningful? Results indicate that a model can be seen as relevant and useful even if it is not internally consistent. Two major themes that were meaningful to study participants were the simultaneously positive and negative role of “difficulty” in the classroom, and the balance between past performance and future potential. The DI tool seems to promote a shared focus on these themes despite the diversity of past educational experiences among study participants. Responses to this model suggest that extremely abstracted representations of teaching are able to influence the claims and insights of users, affording a glimpse into the internal realities of pre-service teachers. This in turn creates an opportunity to articulate these alternative realities without judgment, describe them with respect, and make them an object of consideration rather than a hidden force. The results of this study contribute to a theory of computer environments for learning to teach that can shape the effective use of these tools in the present, as well as accommodate new models that may be developed as technologies change in the future. / text

The Hohokam Water Management Simulation (HWM) is a computer simulation for exploring the operation of the Hohokam irrigation systems in southern Arizona. The simulation takes a middle road between two common kinds of archaeological simulation: large-scale, detailed landscape and environmental reconstructions and highly abstract hypothesis-testing simulations. Given the apparent absence in the Hohokam context of a central authority, the specific aim of the HWM is approaching the Hohokam as a complex system, using principles such as resilience, robustness, and self-organization. The Hohokam case is reviewed, and general questions concerning how the irrigation systems operated are shown to subsume multiple crosscutting and unresolved issues. Existing proposals about the relevant aspects of Hohokam society and of its larger long-term trajectory are based on widely varying short- and long-term processes that invoke different elements, draw different boundaries, and operate at different spatial and temporal scales, and many rely on information that is only incompletely available. A framework for pproaching problems of this kind is put forward. A definition of modeling is offered that specifies its epistemological foundations, permissible patterns of inference, and its role in our larger scientific process. Invoking Logical Positivism, a syntactic rather than semantic view of modeling is proposed: modeling is the construction of sets of assertions about the world and deductions that can be drawn from them. This permits a general model structure to be offered that admits hypothetical or provisional assertions and the flexible interchange of model components of varying scope and resolution. Novel goals for archaeological inquiry fall from this flexible approach; these move from specific reconstruction to a search for more universal and general dynamics. A software toolkit that embodies these principles is introduced: the Assertion-Based Computer Modeling toolkit (ABCM), which integrates simulation with the logical architecture of a relational database, and further provides an easy means for linking models of natural and social processes (including agent-based modeling). The application of this to the Hohokam context is described, and an extended example is presented that demonstrates the flexibility, utility and challenges of the approach. An attached file provides sample output.

Utilizing game theory, learning automata and reinforcement learning concepts, thesis presents a computational model (simulation) based on general equilibrium theory and classical monetary model. Model is based on interacting Constructively Rational agents. Constructive Ratio- nality has been introduced in current literature as machine learning based concept that allows relaxing assumptions on modeled economic agents information and ex- pectations. Model experiences periodical endogenous crises (Fall in both production and con- sumption accompanied with rise in unemployment rate). Crises are caused by firms and households adopting to a change in price and wage levels. Price and wage level adjustments are necessary for the goods and labor market to clear in the presence of technological growth. Finally, model has good theoretical background and large potential for further de- velopment. Also, general properties of games of learning entities are examined, with special focus on sudden changes (shocks) in the game and behavior of game's play- ers, during recovery from which rigidities can emerge. JEL Classification D80, D83, C63, E32, C73, Keywords Learning, Information and Knowledge, Agent-based, Reinforcement learning, Business cycle, Stochastic and Dynamic Games, Simulation, Modeling Author's e-mail...

Large lahars threaten communities living near volcanoes all over the world. Evacuations are a critical strategy for reducing vulnerability and mitigating a disaster. Hazard perceptions, transportation infrastructure, and transportation mode choice are all important factors in determining the effectiveness of an evacuation. This research explores the effects of population, whether individuals drive or walk, response time, and exit closures on an evacuation in a community threatened by a large lahar originating on Mount Rainier, Washington. An agent-based model employing a co-evolutionary learning algorithm is used to simulate a vehicular evacuation. Clearance times increase when the population is larger and when exits are blocked. Clearance times are reduced when a larger proportion of agents opt out of driving, and as the model learns. Results indicate evacuation times vary greatly due to spatial differences in the transportation network, the initial population distribution, and individual behaviors during the evacuation.

Hurricane Sandy hit New York City on October 29, 2012 and greatly disrupted transportation systems, power systems, work, and schools. This research used survey data from 397 respondents in the NYC Metropolitan Area to develop an agent-based model for capturing commuter behavior and adaptation after the disruption. Six different recovery scenarios were tested to find which systems are more critical to recover first to promote a faster return to productivity. Important factors in the restoration timelines depends on the normal commuting pattern of people in that area. In the NYC Metropolitan Area, transit is one of the common modes of transportation; therefore, it was found that the subway/rail system recovery is the top factor in returning to productivity. When the subway/rail system recovers earlier (with the associated power), more people are able to travel to work and be productive. The second important factor is school and daycare closure (with the associated power and water systems). Parents cannot travel unless they can find a caregiver for their children, even if the transportation system is functional. Therefore, policy makers should consider daycare and school condition as one of the important factors in recovery planning. The next most effective scenario is power restoration. Telework is a good substitute for the physical movement of people to work. By teleworking, people are productive while they skip using the disrupted transportation system. To telework, people need power and communication systems. Therefore, accelerating power restoration and encouraging companies to let their employees' telework can promote a faster return to productivity. Finally, the restoration of major crossings like bridges and tunnels is effective in the recovery process. / Master of Science / Natural and man-made disasters, cause massive destruction of property annually and disrupt the normal economic productivity of an area. Although the occurrence of these disasters cannot be controlled, society can minimize the effects with post-disaster recovery strategies. Hurricane Sandy hit New York City on October 29, 2012 and greatly disrupted transportation systems, power systems, work, and schools. In this research, commuter behavior and adaptation after the hurricane were captured by using a survey data that asked questions from people living in NYC metropolitan area about their commuting behavior before and after Hurricane Sandy. An agent-based model was developed and six different recovery strategies were tested in order to find effective factors in returning people to normal productive life faster. In the NYC Metropolitan Area, transit is one of the common modes of transportation; therefore, it was found that the subway/rail system recovery is the top factor in returning to productivity. The next important factor is school and daycare closure. Parents are responsible for their children, therefore; they may not travel to work when school and daycares are closed. The third important factor is power restoration. To telework, people need power and communication systems. By teleworking, people are productive while they skip using the disrupted transportation system. The final important factor is the restoration of major crossings like bridges and tunnels.

Agent-based models are computer simulations in which entities (agents) interact with each other and their environment according to local update rules. Local interactions give rise to global dynamics. These models can be thought of as in silico laboratories that can be used to investigate the system being modeled. Optimization problems for agent-based models are problems concerning the optimal way of steering a particular model to a desired state. Given that agent-based models have no rigorous mathematical formulation, standard analysis is difficult, and traditional mathematical approaches are often intractable. This work presents techniques for the analysis of agent-based models and for solving optimization problems with such models. Techniques include model reduction, simulation optimization, conversion to systems of discrete difference equations, and a variety of heuristic methods. The proposed strategies are novel in their application; results show that for a large class of models, these strategies are more effective than existing methods. / Ph. D.

In this thesis, we present a novel approach to solving optimization problems that are defined on agent-based models (ABM). The approach utilizes concepts in genetic programming (GP) and is demonstrated here using an optimization problem on the Sugarscape ABM, a prototype ABM that includes spatial heterogeneity, accumulation of agent resources, and agents with different attributes. The optimization problem seeks a strategy for taxation of agent resources which maximizes total taxes collected while minimizing impact on the agents over a finite time. We demonstrate how our GP approach yields better taxation policies when compared to simple flat taxes and provide reasons why GP-generated taxes perform well. We also look at ways to improve the performance of the GP optimization method. / McAnulty College and Graduate School of Liberal Arts; / Computational Mathematics / MS; / Thesis;

Optimal Foraging Theory is a set of mathematical models used in the field of behavioral ecology to predict how animals should weigh foraging costs and benefits in order to maximize their food intake. One popular model, referred to as the Optimal Diet Model (ODM), focuses on how individuals should respond to variation in food quality in order to optimize food selection. The main prediction of the ODM is that low quality food items should only be accepted when higher quality items are encountered below a predicted threshold. Yet, many empirical studies have found that animals still include low quality items in their diet above such thresholds, indicating a sub-optimal foraging strategy. Here, we test the hypothesis that such ‘partial preferences’ are produced as a consequence of incomplete information on prey distributions resulting from memory limitations. To test this hypothesis, we used agent-based modeling in NetLogo to create a model of flower choice behavior in a virtual bumblebee forager (SimBee). We program virtual bee foragers with an adaptive decision-making algorithm based on the classic ODM, which we have modified to include memory. Our results show that the probability of correctly rejecting a low quality food item increases with memory size, suggesting that memory limitations play a significant role in driving partial preferences. We discuss the implications of this finding and further applications of our SimBee model in research and educational contexts.