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Yoak, Andrew JamesDisease Control through Fertility Control: Explorations in Two Urban Systems
Doctor of Philosophy, The Ohio State University, 2015, Evolution, Ecology and Organismal Biology
In many areas, wildlife populations have increased substantially in their local density because of a loss of natural controls or some artificially supplemented resource. These populations are often managed to avoid harmful effects on other wildlife species and human-wildlife conflicts. Many species are managed using lethal population reduction, but in those that are resistant to these means or where the method is unpalatable due to public concern, fertility control is becoming increasingly common. This method seeks to reduce the population size of some target problem species by capturing, sterilizing, and releasing individuals back into their habitat. Fertility control is often paired with vaccination programs because each has synergistic effects. Sterilization reduces the population size, making it easier to achieve a higher vaccination proportions for herd immunity. However, these programs have uncertain effects on both the basic biology, population demographics, and disease epidemiology. The current literature makes strongly countered species-specific conclusions. It is also unclear if fertility control is an effective method at reducing the population size in an economically viable way, compared to lethal removal. Here I use computer simulations, cross sectional surveys, and long-term monitoring of two populations, the street dogs (Canis lupus familiaris) of Rajasthan, India, and the raccoons (Procyon lotor) of the Columbus Zoo and Aquarium, to investigate what impact fertility control makes on the populations it targets. In Chapter 2, I exposed replicate simulated populations to various control schemes to see which most lowered the population size and increased vaccination coverage. In Chapter 3, I report the results of surveys of dogs from several real world Indian cities with varied histories of fertility control for several diseases. In Chapters 4 and 5, I report the results of a randomized control study on raccoons, which measured differences in parasite load and survival among control, vaccinated and vaccinated/sterilized individuals. My work demonstrates that fertility control programs can be more effective than lethal control, although the methods used to locate sexually intact individuals for treatment can significantly affect the results. In Chapter 3, I found that intact dogs living in cities with more fertility control had significantly lower prevalence of several diseases compared to those dogs living in cities with less fertility control. This is especially significant because the interventions only vaccinated against rabies, meaning that the fertility control affected local disease epidemiology. This indicates that the sterilization program buffered treated individuals’ ability to resist or spread disease enough to lower exposure to non-treated individuals. I found that sterilization and vaccination in raccoons did not affect the apparent monthly survival rates, but lowered parasite prevalence in males. However, female raccoon parasite prevalence was negatively affected by sterilization. I suggest that the sterilization method used does not eliminate hormone production, causing females to increase the length or intensity of their reproductive seasons. As a whole, this work highlights the importance of understanding the secondary effects of intervention policies. I show that altering reproductive behavior can cause dramatic changes to population dynamics and epidemiology.

Committee:

Ian Hamilton, Dr (Advisor); Stanley Gehrt, Dr. (Advisor); Rebecca Garabed, Dr. (Committee Member); Liza Comita, Dr. (Committee Member)

Subjects:

Animal Diseases; Ecology; Organismal Biology; Parasitology; Veterinary Services; Wildlife Management; Zoology

Keywords:

lethal control; fertility control; procyon lotor; raccoon; domestic dog; free roaming dog; street dog; canis lupus familiaris; agent based modeling; individual based modeling; dog population management;

Rajendran, Balakumar3D Agent Based Model of Cell Growth
MS, University of Cincinnati, 2009, Engineering : Computer Engineering
This research aims at modeling the cell cycle regulation as a three dimensional Agent Based Model. Agent Based Modeling (ABM) is a computational model for simulating autonomous entities in a network. ABM is emerging as a powerful tool in the field of systems biology. ABM is preferred over traditional modeling methods, as it takes the spatial dynamics of the interactions into account. The system is modeled as a collection of autonomous decision making entities called agents. Each of these agents has its own set of characteristics and behavioral rules that are highly adaptive to its surrounding environment. Since the agents have their own sets of characteristics and rules, they can be implemented using the Object Oriented Programming (OOP) concept. OOP is a powerful programming technique that can be used to model and design applications effectively. Previously our lab implemented an ABM model of the phage lambda virus and used this model to study the behavior of phage lambda under various conditions. Building on our experience, we have developed a more complex agent based model in a Java based ABM platform. The cell cycle model is modeled in MASON, a very robust and efficient ABM platform that supports strong 3D visualization and parallelization. The model shows how the cells interact among each other at an intercellular level. The stages in the cell cycle are implemented and the characteristics of cell growth are studied. This particular model has also been extended to simulate a wound condition and its growth characteristics are again observed. The model is run for certain base parameter values and the system can be simulated for other conditions by changing these parameters. Tissue Engineering is an important and growing field, and computational models such as the one developed in this thesis can be very useful tools for biologists and tissue engineers.

Committee:

Carla Purdy, PhD (Committee Chair); Daria Narmoneva, PhD (Committee Member); Ali Minai, PhD (Committee Member)

Subjects:

Biology; Cellular Biology; Computer Science; Engineering

Keywords:

Agent based modeling; cell growth; three dimensional

Rice, Ketra LachellA Multi-Method Analysis of the Role of Spatial Factors in Policy Analysis and Health Disparities Research
Doctor of Philosophy, The Ohio State University, 2013, Public Policy and Management
The premise of this research rests on the idea that space has a significant influence on diet and health disparities and on the utilization of food assistance policies targeted towards minimizing those disparities. In the specific context of food deserts, this research integrates a multi-disciplinary conceptual approach and a multi-method approach to explore the relationships and interactions between people and spaces. The multi-disciplinary approach links spatial conceptual perspectives from rural sociology and health geography to provide the theoretical framework for the relevance of integrating space in policy analysis and diet and health disparities research. The multi-method approach provides a more comprehensive examination of diet and health disparities by allowing for the statistical testing of outcomes and the simulated exploration of policy interventions. This dissertation consists of a set of three interconnected essays. The first essay presents conceptual perspectives from a health geography and rural sociology lens and relates the perspectives to aggregate level participation rates for the Supplemental Nutrition Assistance Program (SNAP). Incorporating spatial econometric methods to measure county-level SNAP participation, the results from the analysis show variation in SNAP participation based on county characteristics. Understanding this geographic variation provides an opportunity to formulate SNAP policies and procedures which explicitly respond to and incorporate spatial differences across counties. The second essay expands the analysis of geographic variation by examining health outcomes at the individual-level in relationship to individual-level and county characteristics. This essay measures the consequences of lack of access to food by exploring the adverse health outcomes that can be attributed to the food environment. A hierarchical linear model is implemented and the results show that counties with a higher quality food environment predict higher levels of individual health status. The spatial disparity of the food environment on individual-level health outcomes coupled with the spatial disparity of SNAP participation suggested the need to further explore the complexity of the policy problem of diet and health disparities through an additional lens of simulation modeling. The third essay explores this using an agent-based simulation. I simulate a local food environment and observe changes in the environment as policy interventions are introduced. The results show that poor health status can persist in a poor food environment even with people-based interventions that increase low income consumers’ purchasing power. The place-based policy intervention that changed the environment subsequently changed consumption patterns and improved health outcomes. The understanding of spatial theory in policy context and the implementation of a multi-method approach for addressing the complexity of health disparities contributes to a perspective that the analysis of public policy and design of policy interventions requires a conceptual understanding of the spatial associations underlying the policies being investigated. Above all, this dissertation contributes to the body of knowledge of the complementarities of hierarchical linear modeling and agent-based modeling to examine the complexity of individual-level diet and health outcomes, and a recognition that geographic characteristics not only predict diet and health disparities but also predict the usage of assistance offered that seeks to minimize diet and health disparities.

Committee:

Anand Desai (Advisor); Rob Greenbaum (Committee Member); Linda Lobao (Committee Member)

Subjects:

Public Administration; Public Policy

Keywords:

Geography and Public Policy; Diet and Health Disparities; Food Deserts; Food Policy; Hierarchical Linear Modeling; Agent-Based Modeling

Chen, ZhuoAn Agent-Based Model for Information Diffusion Over Online Social Networks
MA, Kent State University, 2016, College of Arts and Sciences / Department of Geography
Nowadays, social networks services such as Facebook, Twitter, Instagram, etc. have become popular platforms for either celebrities, news media, organizations, governors or general public to express their ideas and opinions. They have created a great opportunity for researchers to explore how information spread through online social networks. Benefited from this, this thesis studies the efficient way of information diffusion on online social networks using the approach of agent-based modeling (ABM). A NetLogo ABM was created to conduct the experiments and analyses, along with the real network dataset retrieved from Twitter. It shows that with the same number of nodes and edges, the network having higher average path length or lower average clustering coefficient tends to have wider information diffusion. In addition, how to locate optimal early adopters in order to satisfy efficient information diffusion mainly depends on the network structure and propagation probabilities among individuals in the network. This thesis aims at contributing to studies of online social networks on information diffusion from the perspective of efficient diffusion with agent-based modeling and simulation. Application of this thesis could benefit those from business or government who want to disseminate advertisement/information in a fast and economic way. Outcomes from this study should also provide hints to the geography likely behind information diffusion in social networks.

Committee:

Jay Lee (Advisor); Xinyue Ye (Committee Member); Eric Shook (Committee Member)

Subjects:

Geography; Information Science; Information Technology

Keywords:

information diffusion, agent-based modeling, social network, Twitter, efficient information diffusion

Middleton, Victor EatonImperfect Situation Analysis: Representing the Role of Error and Uncertainty in Modeling, Simulation and Analysis
Doctor of Philosophy (PhD), Wright State University, 2014, Engineering PhD
Much of traditional modeling, simulation and analysis (MS&A) is supported by engineering models - deterministic, Newtonian physics-based representations of closed systems. Such approaches are not well-suited to represent the intricacies of human behavior. This research advocates and seeks to articulate the concept of a more human- centric approach to MS& A, one that better represents decision-making and other cognitive aspects of human behavior as well as it does physical activity. It starts with a view of individuals and groups as complex adaptive systems, which are best represented using agent-based modeling. Representation of human behavior through intelligent agents incorporates models of decision-making, knowledge engineering and knowledge representation, as well as the whole gamut of the psychological and physiological interactions of humans with each other and their environment. This representation is exemplified by consideration of situation awareness/situation understanding (SA/SU) as a core element. This leads to the development of a proof-of-concept simulation of a specific, easily understood, and quantifiable example of human behavior: intelligent agents being spatially "lost" while trying to navigate in a simulation world. This model is named MOdeling Being Intelligent and Lost (MOBIL), noting the ability to be in both of these states is central to the simulation. MOBIL uses a blend of object oriented software principles with agent based modeling to establish the utility of applying the human- centric approach to analysis. Applying that simulation in a number of virtual experiments illustrates how it supports investigation into an individual's SA/SU and associated decision-making processes.

Committee:

Frank Ciarallo, Ph.D. (Advisor); Raymond Hill, Ph.D. (Committee Member); Yan Liu, Ph.D. (Committee Member); Mateen Rizki, Ph.D. (Committee Member); Mary E. Fendley, Ph.D. (Committee Member); David Hudak, Ph.D. (Committee Member)

Subjects:

Armed Forces; Artificial Intelligence; Cognitive Psychology; Computer Science; Engineering; Industrial Engineering; Information Systems

Keywords:

Situation awareness; situation understanding; error; modeling; simulation; arc node networks; agent based modeling; intelligent agents, human-centric analysis; dismounted combatants; war games

Karimian, KimiaBioCompT - A Tutorial on Bio-Molecular Computing
MS, University of Cincinnati, 2013, Engineering and Applied Science: Computer Engineering
DNA computing is a new and interesting development that connects computer science to molecular biology. The idea of DNA computing arose from Adleman's 1994 experiment in which he showed how to solve the Hamiltonian path problem (HPP) in polynomial time using oligonucleotides of DNA. DNA computing enables massive parallelism at the molecular level and is one of the technologies being explored by researchers as a supplement to traditional silicon-based computing. But many computer scientists and computer engineers have little knowledge of biology and therefore find it difficult to get started in the field of DNA computing. Thus the aim of this work is to provide a tutorial to introduce DNA computing to a wider audience and to show some examples of how DNA computing can be simulated using agent-based techniques and can be applied to solve complex problems. Currently our system consists of four sections: DNA structure and behavior, basic DNA computation, DNA-based cryptography, and using agent based modeling and simulation to explore DNA behavior. We also provide a small assessment test to enable users to test themselves and evaluate their knowledge of the topics covered. The system is modular in design and can easily be modified or extended to include more information on each topic or to include additional examples of DNA computing.

Committee:

Carla Purdy, Ph.D. (Committee Chair); George Purdy, Ph.D. (Committee Member); Anca Ralescu, Ph.D. (Committee Member)

Subjects:

Computer Engineering

Keywords:

DNA Computing;agent-based modeling;DNA based cryptography;Bio-molecular computing;DNA structure and behavior;tutorial on DNA computing;

Jun, Min SuThree Essays on Complex Contractual Networks of Farmers
Doctor of Philosophy, The Ohio State University, 2016, Agricultural, Environmental and Developmental Economics
In Essay 1, I examine the effects of systemic weather shocks on agricultural producers, agricultural lenders, and rural communities in the northern Malawi and the potential benefits of using weather index insurance as a catastrophic risk management tool. To this end, I develop an agent-based model (ABM) of a hypothetical rural community and the model is calibrated to study the northern Malawi area. I simulate the system-wide impacts of catastrophic weather events and how weather index insurance can be used to mitigate adverse impacts of these events. The model generates results that are consistent with the theoretical and empirical findings that have been published to date on the impacts of weather index insurance when used to support agricultural credit. My simulations also generate novel findings regarding the system-wide impacts of catastrophic weather shocks and the potential benefits of index insurance. In particular, I find that if financial institutions purchase index insurance, they will be able to supply significant extra credit only when the area faces a similar or more severe drought that occurs one in fifty years according to the historical record. In Essay 2, I examine the potential benefits of index insurance by answering more detailed questions. The first question addressed is whether financial institutions have economic motivation to supply additional credit to households if they insure their portfolios using weather index insurance. I find that financial institutions can expect higher profits when utilizing indemnities to supply additional credit than when just holding them. The second question addressed is whether insurance can increase financial inclusion of marginal smallholder households. Specifically, I test whether insuring loan portfolios against weather catastrophes can promote reduction of interest rates offered by banks to smallholder farmers. I find that meso-index insurance could promote reduction of the interest rates for the borrower classes that have the lowest credit rating. Lastly, I test whether a counter-cyclical capital requirement can boost the systemic benefits of meso-index insurance. Due to the pressure on credit supply from the capital requirement and the higher interest rates needed to cover insurance premiums, synergies between insurance and counter-cyclic capital requirement policies do not appear to exist. In Essay 3, I estimate oligopolistic power of major U.S. grain companies which might originate from their oligopsonistic positions to farmers. By extending the linear-quadratic model developed by Karp and Perloff (1989, 1993a, 1993b), the market conduct parameter is estimated in the open loop and the feedback equilibria. In the grain-processing sector, firms’ oligopoly power originates from the oligopolistic power exerted by firms over farmers. Thus, unlike the existing literature, an output quantity is expressed with the input (grain) quantity variable under assumption of CRS production technology. Each firm’s share of storage capacity is taken as a proxy of the firm’s input procurement. Although my result reject the hypothesis of perfect collusion, the do not reject the hypotheses of price taking behavior and the existence of a Nash-Cournot equilibrium.

Committee:

Mario Miranda (Committee Chair); Elena Irwin (Committee Member); Ani Katchova (Committee Member)

Subjects:

Agricultural Economics; Economics

Keywords:

Risk Management, Agent Based Modeling, Weather Systemic Risk, Index Insurance, Market Power

Reinhardt, James WThe Role of Cell-Substrate Interactions in ECM Remodeling, Migration, and the Formation of Multicellular Structures
Doctor of Philosophy, The Ohio State University, 2014, Biomedical Engineering
Active, mechanical interactions between cells and their extracellular matrix (ECM) are essential for ECM remodeling and cell migration, two behaviors that support diverse biological processes including embryonic development, wound healing, fibrosis, and cancer progression. Cell-populated reconstituted type I collagen hydrogels are often used as a model system in which to study ECM remodeling and cell migration in vitro. Unfortunately, this system has limitations since it is not possible to independently control individual microstructural properties. This limitation has inspired theoretical models as an alternative way to study ECM remodeling and migration. However, so far no single approach has been able to capture both fibril-level detail and dynamic cell traction force. With the goal of creating a model that can capture both fibril-level detail and dynamic cell traction force, we developed an agent-based model of cell-mediated collagen compaction and migration. With this model we observed behaviors that were not programmed, but emerged from simple rules for cell-fibril interactions. Among these, our model qualitatively reproduced remodeling commonly seen in cell-populated collagen gels: macroscopic, pericellular, and intercellular compaction. Similar to experimental observations, matrical tracks formed between pairs of cells before directional migration of nearby cells toward on another. Cells also exhibited durotaxis in the absence of force-strengthening of cell-matrix bonds. This suggests that durotaxis may not involve a complicated mechanism, but may simply be an emergent behavior, the cumulative result of analogous, simple, cell-matrix interactions. We then further developed this model to make collagen fibrils more physically-realistic by modeling them as elastic rods and using parameter values obtained from the experimental literature. Subjecting our fibrils to loading conditions that created tension and bending demonstrated that our simulated fibrils approximated their analytically-predicted deformations and shapes. Modeling of cross-links was also improved to more closely approximate their expected behavior. Together, these changes resulted in a more intentionally constructed network that was stress-free in the absence of external perturbation. Model development culminated in model validation against two sets of data from the experimental literature. Similar to experimental data our computational model showed that collagen displacement decreased linearly with increasing distance from a single cell and that the compaction of collagen between pairs of cells was inversely related to cell-cell distance. In other work we used in vitro experiments to show that PANC-1 cells did not exhibit directed migration toward a central cluster. Using agent-based modeling, we then were able to show that clustering may occur simply due to random migration, relatively high cell-cell adhesion, and low cell-matrix adhesion. Separately, I have contributed to the refinement of an experimental system used to study cell-matrix interactions that provides an alternative to reconstituted type I collagen.

Committee:

Keith Gooch (Advisor); Samir Ghadiali (Committee Member); Richard Hart (Committee Member); Peter Anderson (Committee Member)

Subjects:

Biomedical Engineering

Keywords:

agent-based modeling; collagen; remodeling; migration; PANC-1; cell tracking; differential adhesion; directed migration; long-range signaling

Massey, David“EXPERT” AND “NON-EXPERT” DECISION MAKING IN A PARTICIPATORY GAME SIMULATION: A FARMING SCENARIO IN ATHIENOU, CYPRUS
Master of Arts, The Ohio State University, 2012, Geography
The Greek-Cypriot village of Athienou, located in the UN Buffer Zone in Cyprus, lies at the front lines of a politically complex issue that divides the island of Cyprus. Developing an understanding of how Greek-Cypriot farmers’ agricultural decisions affects land use/cover change (LUCC) allows researchers to formulate models and assessment plans for future scenarios. Drawing from the Companion Modeling (ComMod) approach, this research uses ethnographic fieldwork to develop knowledge about Greek-Cypriot farming practices and the drivers of agricultural LUCC in Athienou through grounded theory. A conceptual model of the Athienou agricultural system is then built as a Role Playing Game (RPG). The RPG simulates the farming strategies and agricultural LUCC in Athienou in a scenario where the Turkish Occupied land to the north of the village becomes available for farming again. Two sets of participants, Greek-Cypriot farmers (“experts”) and undergraduate students (“non-experts”), then play the RPG. An examination of the outcomes from decision-making strategies of the “experts” and “non-experts” during the RPG scenario suggests a potential way to crowd-source information.

Committee:

Ola Ahlqvist, PhD (Advisor); Dan Sui, PhD (Committee Chair); Mark Moritz, PhD (Committee Chair)

Subjects:

Geography

Keywords:

Agent Based Modeling; Agriculture; Companion Modeling; Complex Systems; Crowdsourcing; Cyprus; Ecosystems; LUCC; Role Playing Games

Heath, Brian L.The History, Philosophy, and Practice of Agent-Based Modeling and the Development of the Conceptual Model for Simulation Diagram
Doctor of Philosophy (PhD), Wright State University, 2010, Engineering PhD
This research advances ABM as a generic analysis tool such that ABM can reach its full potential as a revolution in modeling and simulation. To achieve this goal, the field of ABM is examined from many perspectives. The first perspectives examined are complex systems, the historical emergence of ABM, and philosophical issues related to ABM. These topics establish some clear foundations for the field across multiple disciplines. Next the current practice of ABM is investigated. Through a comprehensive 279 article survey some current deficiencies and opportunities in ABM are identified. Based on these opportunities, a new diagramming technique called the Conceptual Model for Simulation (CM4S) Diagram is developed. Fundamentally, the CM4S Diagram represents the first diagramming technique designed specifically for the effective representation, construction, and sanctioning of ABM computer simulations based on identified needs in the ABM modeling field and simulation modeling philosophy. Finally, the effectiveness of the CM4S Diagram is evaluated through the development of social science, military, and supply chain ABM simulations.

Committee:

Frank W. Ciarallo, PhD (Advisor); Raymond R. Hill, PhD (Committee Co-Chair); Misty Blue, PhD (Committee Member); Thomas C. Hartrum, PhD (Committee Member); Yan Liu, PhD (Committee Member); Ed Pohl, PhD (Committee Member)

Subjects:

Industrial Engineering; Operations Research

Keywords:

Agent-Based Modeling; Simulation; Validation; Diagrams; CM4S Diagram

Gebre, Meseret RedaeMUSE: A parallel Agent-based Simulation Environment
Master of Science, Miami University, 2009, Computer Science and Systems Analysis
Realizing the advantages of simulation-based methodologies requires the use of a software environment that is conducive for modeling, simulation, and analysis. Furthermore, parallel simulation methods must be employed to reduce the time for simulation, particularly for large problems, to enable analysis in reasonable timeframes. Accordingly, this thesis covers the development of a general purpose agent-based, parallel simulation environment called MUSE (Miami University Simulation Environment). MUSE, provides an Application Program Interface (API) for agent-based modeling and a framework for parallel simulation. The API was developed in C++ using its object oriented features. The core parallel simulation capabilities of MUSE were realized using the Time Warp synchronization methodology and the Message Passing Interface (MPI). Experiments show MUSE to be a scalable and efficient simulation environment.

Committee:

Dhanajai Rao, PhD (Advisor); Mufit Ozden, PhD (Committee Member); Lukasz Opyrchal, PhD (Committee Member)

Subjects:

Computer Science

Keywords:

Parallel Simulations; Time Warp; MPI; Agent-based Modeling; MUSE; Parallel Simulation Environment; C++ API

Pavlic, Theodore PaulDesign and Analysis of Optimal Task-Processing Agents
Doctor of Philosophy, The Ohio State University, 2010, Electrical and Computer Engineering

This dissertation is given in two parts followed by concluding remarks. The first three chapters describe the generalization of optimal foraging theory for the design of solitary task-processing agents. The following two chapters address the coordinated action of distributed independent agents to achieve a desirable global result. The short concluding part summarizes contributions and future research directions.

Optimal foraging theory (OFT) uses ecological models of energy intake to predict behaviors favored by natural selection. Using models of the long-term rate of energetic gain of a solitary forager encountering a variety of food opportunities at a regular rate, it predicts characteristics of optimal solutions that should be expressed in nature. Several engineered agents can be modeled similarly. For example, an autonomous air vehicle (AAV) that flies over a region encounters targets randomly just as an animal will encounter food as it travels. OFT describes the preferences that the animal is likely to have due to natural selection. Thus, OFT applied to mobile vehicles describes the preferences of successful vehicle designs.

Although OFT has had success in existing engineering applications, rate maximization is not a good fit for many applications that are otherwise analogous to foraging. Thus, in the first part of this dissertation, the classical OFT methods are rediscovered for generic optimization objectives. It is shown that algorithms that are computationally equivalent to those inspired by classical OFT can perform better in realistic scenarios because they are based on more feasible optimization objectives. It is then shown how the design of foraging-like algorithms provides new insight into behaviors observed and expected in animals. The generalization of the classical methods extracts fundamental properties that may have been overlooked in the biological case. Consequently, observed behaviors that have been previously been called irrational are shown to follow from the extension of the classical methods.

The second part of the dissertation describes individual agent behaviors that collectively result in the achievement of a global optimum when the distributed agents operate in parallel. In the first chapter, collections of agents that are each similar to the agents from the early chapters are considered. These agents have overlapping capabilities, and so one agent can share the task processing burden of another. For example, an AAV patrolling one area can request the help of other vehicles patrolling other areas that have a sparser distribution of targets. We present a method of volunteering to answer the request of neighboring agents such that sensitivity to the relative loading across the network emerges. In particular, agents that are relatively more loaded answer fewer task-processing requests and receive more answers to their own requests. The second chapter describes a distributed numerical optimization method for optimization under inseparable constraints. Inseparable constraints typically require some direct coordination between distributed solver agents. However, we show how certain implementations allow for stigmergy, and so far less coordination is needed among the agents. For example, intelligent lighting, which maintains illumination constraints while minimizing power usage, is one application where the distributed algorithm can be applied directly.

Committee:

Kevin M. Passino (Advisor); Andrea Serrani (Committee Member); Atilla Eryilmaz (Committee Member)

Subjects:

Animals; Biology; Computer Science; Ecology; Economics; Electrical Engineering; Engineering; Mathematics; Robots; Systems Design; Technology

Keywords:

optimization; foraging theory; agent-based modeling; autonomous vehicles; game theory

Wang, XuguangSpatial Adaptive Crime Event Simulation With RA/CA/ABM Computational Laboratory
PhD, University of Cincinnati, 2005, Arts and Sciences : Geography
An agent-based crime event and crime pattern simulation model is developed in this research. The purpose of the simulation model is to provide a computational laboratory for environmental criminologists to study the interactions among offenders, targets, controllers, and crime places. The simulation model also aims to provide a useful tool for teaching crime event theories. Routine activity theory and crime pattern theory are the theoretical foundations of the simulation model. Agent-based modeling coupled with cellular automata addresses the complex crime event process of street robbery. A type of spatial autonomous agent is developed with a wayfinding capability on the urban street network. The wayfinding algorithm is based on a reinforcement learning algorithm. Offender agents, target agents and police agents are developed based on the spatial autonomous agent, which can be released on a street network to execute their routine activity schedules. The interactions among offender agents, target agents, police agents, and crime places create crime events and crime patterns for analysis. Offender agents and target agents can learn from their past offending/victimization experience and change their spatial behaviors. The crime event and crime pattern simulation model is tested to be able to generate credible spatial, temporal, victimization, and offending patterns. The simulation model is then applied to examine the effect of agent adaptations on spatial crime patterns, offending patterns and victimization patterns. The power-function distributions of crime events among crime places and offender population are examined as emphases. Targeted for MS Windows desktop, the RA/CA/ABM computational laboratory is implemented using Visual C++. The computational laboratory has a graphic user interface that allows users to customize the simulation model, control the simulation process, visualize agent movement and crime patterns during the simulation, and query agent properties and crime patterns during the simulation. The computational laboratory is loosely coupled with Arcview GIS, so that it can take spatial data from Arcview as input, and make use of spatial analytical capabilities of Arcview to analyze the output crime patterns.

Committee:

Dr. Lin Liu (Advisor)

Subjects:

Geography

Keywords:

agent-based modeling; cellular automata; crime pattern simulation; Geographic Information Systems; routine activity theory

Wang, NinghuaAnalyzing spatial effects of hotspot policing with a simulation approach
MA, University of Cincinnati, 2009, Arts and Sciences : Geography
Crimes tend to cluster in space. This clustering natural of crime induces criminologists and law enforcement practitioners to concentrate police resource on certain areas, i.e. crime hotspots. In general, this hotspot policing tends to reduce crime in the treatment area. This reduction may lead to either crime being displaced to the surrounding areas or the benefits of crime reduction being diffused to the surrounding areas. However, this controversy between crime displacement and diffusion of benefits is difficult to investigate with conventional empirical studies. In this article, we tackle this problem with experimentations in a virtual laboratory - SPACES. Our results reveal that crime cannot be easily displaced because crime opportunities are limited in low crime areas and offenders are often attached to the area where they perform their routine activities. Also, even if offenders are made to be highly mobile in an experiment, their crime places do not seem to change significantly and they are still vulnerable to hotspot policing. Our experiments also suggest that little evidence can be found to support that a diffusion of crime control benefits occurs in nearby areas.

Committee:

Lin Liu (Committee Chair); John Eck (Committee Member); Changjoo Kim (Committee Member)

Subjects:

Criminology; Geography

Keywords:

Crime simulation; Agent-based Modeling; SPACES; Hotspot Policing; Displacement; Diffusion of Benefits

NAMBOODIRI, EASWARIUSING AGENT BASED MODELING AND GENETIC ALGORITHMS TO UNDERSTAND AND PREDICT THE BEHAVIOR OF COMPLEX ENVIRONMENTAL SYSTEMS
MS, University of Cincinnati, 2006, Engineering : Computer Science
Agent based modeling techniques can be used effectively to study complex systems, which have many parameters. The behavior of the system typically depends heavily on the values of these parameters. In the example of a complex system studied here, an ecosystem, there are some sets of parameters for which the system will be sustainable, i.e., in which the system’s participating entities will not die off. When the number of parameters becomes large, the parameter space becomes very broad. Hence finding the optimum parameters for sustainability typically becomes an NP- hard problem. In these circumstances, an effective solution can be found by a combined application of agent-based modeling (to understand the behavior) and a genetic algorithm (for a quantitative prediction). An Agent Based Modeling framework is ideally suited for modeling these systems bottom-up, and genetic algorithms are search techniques well-suited for searching sets of optimal points in the parameter space through natural selection. Genetic algorithms running in parallel on a cluster of PCs theoretically give linear speed, leading to increased efficiency. The work presented here is divided into three phases-(i) development of an agent-based model for a complex system, an ecological food web (ii) search of the parameter space of the system using a genetic algorithm (iii) parallelization of the application. The food web was modeled using the simulation software, Swarm. This system was then integrated into a parallel genetic algorithm package PGAPack, to search for an optimal set of parameters. The resulting application was then measured for efficiency and speedup by running it on a cluster of workstations. The results obtained were very promising, in terms of successfully developing a sustainable system and obtaining increased performance through parallelization using the cluster.

Committee:

Dr. Carla Purdy (Advisor)

Subjects:

Computer Science

Keywords:

Agent Based Modeling; Genetic Algorithms; Cluster Programming