<p>In recent years, government aid agencies and international organizations have increased their financial commitments to controlling and eliminating malaria from the planet. This renewed emphasis on elimination is reminiscent of a previous worldwide campaign to eradicate malaria in the 1960s, a campaign which ultimately failed. To avoid a repeat of the past, mechanisms must be developed to sustain effective malaria control programs.</p><p>A number of sociobehavioral, economic, and biophysical challenges exist for sustainable malaria control, particularly in high-burden areas such as sub-Saharan Africa. Sociobehavioral challenges include maintaining high long-term levels of support for and participation in malaria control programs, at all levels of society. Reasons for the failure of the previous eradication campaign included a decline in donor, governmental, community, and household-level support for control programs, as malaria prevalence ebbed due in part to early successes of these programs.</p><p>Biophysical challenges for the sustainability of national malaria control programs (NMCPs) encompass evolutionary challenges in controlling the protozoan parasite and the mosquito vector, as well as volatile transmission dynamics which can lead to epidemics. Evolutionary challenges are particularly daunting due to the rapid generational turnover of both the parasites and the vectors: The reliance on a handful of insecticides and antimalarial drugs in NMCPs has placed significant selection pressures on vectors and parasites respectively, leading to a high prevalence of genetic mutations conferring resistance to these biocides.</p><p>The renewed global financing of malaria control makes research into how to effectively surmount these challenges arguably more salient now than ever. Economics has proven useful for addressing the sociobehavioral and biophysical challenges for malaria control. A necessary next step is the careful, detailed, and timely integration of economics with the natural sciences to maximize and sustain the impact of this financing.</p><p>In this dissertation, I focus on 4 of the challenges identified above: In the first chapter, I use optimal control and dynamic programming techniques to focus on the problem of insecticide resistance in malaria control, and to understand how different models of mosquito evolution can affect our policy prescriptions for dealing with the problem of insecticide resistance. I identify specific details of the biological model--the mechanisms for so-called "fitness costs" in insecticide-resistant mosquitoes--that affect the qualitative properties of the optimal control path. These qualitative differences carry over to large impacts on the economic costs of a given control plan.</p><p>In the 2nd chapter, I consider the interaction of parasite resistance to drugs and mosquito resistance to insecticides, and analyze cost-effective malaria control portfolios that balance these 2 dynamics. I construct a mathematical model of malaria transmission and evolutionary dynamics, and calibrate the model to baseline data from a rural Tanzanian district. Four interventions are jointly considered in the model: Insecticide-spraying, insecticide-treated net distribution, and the distribution of 2 antimalarial drugs--sulfadoxine pyramethamine (SP) and artemisinin-based combination therapies (ACTs). Strategies which coordinate vector controls and treatment protocols should provide significant gains, in part due to the issues of insecticide and drug resistance. In particular, conventional vector control and ACT use should be highly complementary, economically and in terms of disease reductions. The ongoing debate concerning the cost-effectiveness of ACTs should thus consider prevailing (and future) levels of conventional vector control methods, such as ITN and IRS: If the cost-effectiveness of widespread ACT distribution is called into question in a given locale, scaling up IRS and/or ITNs probably tilts the scale in favor of distributing ACTs. </p><p>In the 3rd chapter, I analyze results from a survey of northern Ugandan households I oversaw in November 2009. The aim of this survey was to assess respondents' perceptions about malaria risks, and mass indoor residual spraying (IRS) of insecticides that had been done there by government-sponsored health workers. Using stated preference methods--specifically, a discrete choice experiment (DCE)--I evaluate: (a) the elasticity of household participation levels in IRS programs with respect to malaria risk, and (b) households' perceived value of programs aimed at reducing malaria risk, such as IRS. Econometric results imply that the average respondent in the survey would be willing to forego a $10 increase in her assets for a permanent 1% reduction in malaria risk. Participation in previous IRS significantly increased the stated willingness to participate in future IRS programs. However, I also find that at least 20% of households in the region perceive significant transactions costs from IRS. One implication of this finding is that compensation for these transactions costs may be necessary to correct theorized public good aspects of malaria prevention via vector control.</p><p>In the 4th chapter, I further study these public goods aspects. To do so, I estimate a welfare-maximizing system of cash incentives. Using the econometric models estimated in the 3rd chapter, in conjunction with a modified version of the malaria transmission models developed and utilized in the first 2 chapters, I calculate village-specific incentives aimed at correcting under-provision of a public good--namely, malaria prevention. This under-provision arises from incentives for individual malaria prevention behavior--in this case the decision whether or not to participate in a given IRS round. The magnitude of this inefficiency is determined by the epidemiological model, which dictates the extent to which households' prevention decisions have spillover effects on neighbors. </p><p>I therefore compute the efficient incentives in a number of epidemiological contexts. I find that non-negligible monetary incentives for participating in IRS programs are warranted in situations where policymakers are confident that IRS can effectively reduce the incidence of malaria cases, and not just exposure rates. In these cases, I conclude that the use of economic incentives could reduce the incidence of malaria episodes by 5%--10%. Depending on the costs of implementing a system of incentives for IRS participation, such a system could provide an additional tool in the arsenal of malaria controls.</p> / Dissertation

The complexities of our surrounding environments are becoming increasingly diverse, more integrated, and continuously more difficult to predict and characterize. These modeling complexities are ever more prevalent in System-of-Systems (SoS) simulations where computational times can surpass real-time and are often dictated by stochastic processes and non-continuous emergent behaviors. As the number of connections continue to increase in modeling environments and the number of external noise variables continue to multiply, these SoS simulations can no longer be explored with traditional means without significantly wasting computational resources. This research develops and tests an adaptive sequential design of experiments to reduce the computational expense of exploring these complex design spaces. Prior to developing the algorithm, the defining statistical attributes of these spaces are researched and identified. Following this identification, various techniques capable of capturing these features are compared and an algorithm is synthesized. The final algorithm will be shown to improve the exploration of stochastic simulations over existing methods by increasing the global accuracy and computational speed, while reducing the number of simulations required to learn these spaces.

Zugl.: Kassel, Univ., Diss.

Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a variety of domain-specific approaches and applications. However, while cas researchers are inherently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains. In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers to adopt a suitable framework level on the basis of available data types, their research study objectives and expected outcomes, thus allowing them to better plan and conduct their respective research case studies. Firstly, the complex network modeling level of the proposed framework entails the development of appropriate complex network models for the case where interaction data of cas components is available, with the aim of detecting emergent patterns in the cas under study. The exploratory agent-based modeling level of the proposed framework allows for the development of proof-of-concept models for the cas system, primarily for purposes of exploring feasibility of further research. Descriptive agent-based modeling level of the proposed framework allows for the use of a formal step-by-step approach for developing agent-based models coupled with a quantitative complex network and pseudocode-based specification of the model, which will, in turn, facilitate interdisciplinary cas model comparison and knowledge transfer. Finally, the validated agent-based modeling level of the proposed framework is concerned with the building of in-simulation verification and validation of agent-based models using a proposed Virtual Overlay Multiagent System approach for use in a systematic team-oriented approach to developing models. The proposed framework is evaluated and validated using seven detailed case study examples selected from various scientific domains including ecology, social sciences and a range of complex adaptive communication networks. The successful case studies demonstrate the potential of the framework in appealing to multidisciplinary researchers as a methodological approach to the modeling and simulation of cas by facilitating effective communication and knowledge transfer across scientific disciplines without the requirement of extensive learning curves.

This thesis explores a border security system based on agent-based modeling and simulation (ABMS). The ABMS software platform, map aware non-uniform automata, is used to model various scenarios and evaluate the border security system given a set of infiltrators who have evolutionary behavior governed by a genetic algorithm (GA). The GA is used to represent adaptive behavior of the enemy when the friendly force has deployed our border security at a maximum level. By using a near optimal Latin hypercube design, our simulation runs are implemented efficiently and the border security system is analyzed using four different kinds of measures of effectiveness. / text

Landscape connectivity models aim to map the links or corridors that wildlife would or do use between patches of habitat. Migratory species such as elk traverse between such patches which serve as seasonal ranges. The goal of this study was to compare and contrast the suitability of several landscape connectivity models for describing and predicting migration in a long-distance migrant. We measured the suitability of connectivity models for covering and predicting the migratory movements of elk in the Greater Yellowstone Ecosystem. GPS point data was converted to sequential networks for multiple populations of elk. GPS data was also used to delineate the summer and winter ranges of each population. The kernel density of routes in the networks was measured for comparison to connectivity models. The ranges served as the patches to be connected by such models. A resistance surface was produced using reclassified landcover data for mapping habitat suitability and linear road data for human presence or obstruction to movement. Landscape connectivity was measured for eleven migratory elk populations using three distinct models. The first measured connectivity using circuit theory; the second, agent based modeling; the third, least cost corridors. The model results were compared to the migratory network density by measuring correlation. This was followed by a new method of measuring the influence of autocorrelation between the models and networks. Some of the models were then altered to test for suspected influences. This study shows that least cost corridors and circuit theory can are limited in their ability to predict the migratory movements between summer and winter ranges but only so much. They lack the ability to predict exploratory movements that do not link conspicuous ranges to each other. They also lack the ability to account for all avoidance behaviors in the landscape. Our results suggest that connectivity models need improvement by accounting for exploration outside of prime habitat. It also suggests connectivity models are not adequate predictors of migratory movements and not suited to conservation planning of migratory networks. This supports Sawyer's (et al. 2009) ungulate conservation planning of considering connectivity but basing priority on migratory landscape usage. It is assumed that fragmentation or loss in connectivity impedes seasonal migration, cutting off wildlife from resources (Rudnick et al. 2012). This study shows that migratory elk are actually using less than prime and supposedly fragmented habitat in migration and that there is more than connectivity at play.

Traffic congestion has been placing an extremely high burden on the development of modern cities. Congestion can be alleviated by either increasing road capacity, or by reducing traffic demand. For decades, increasing capacity by building more roads and lanes has been the major solution applied to accommodate the ever-growing traffic demand. However, it turns out to be of limited effect due to some well-known phenomenon such as latent demand. Controlling and managing traffic demand has in turn been viewed as a cost-effective alternative to increasing road capacity, as has been demonstrated many successful applications all around the world. Within the concept framework of Traffic Demand Management (TDM), Active Transportation and Demand Management (ATDM) is the dynamic management, control, and influence of traffic demand and traffic flow of transportation facilities. ATDM strategies attempt to influence traveler behavior and further manage traffic flow in a time-dependent manner within the existing infrastructure Successful ATDM applications include congestion pricing, adaptive ramp metering, dynamic speed limits, dynamic lane use control, etc. Singapore stands out to be an excellent success story of ATDM, as the implementations of "Cap and Trade" license plates and electronic road pricing make motoring a high cost privilege for citizens of Singapore, making the public relies on transit. Monetary leverage is an effective instrument to facilitate ATDM. Examples of ATDM applications adopting monetary instrument includes dynamic congestion pricing, "Cap and Trade" of car licenses, etc. Taking congestion pricing as an example, policy makers are inducing travelers' behavior and alternating their preferences towards different behavior decisions by levying price tags to different choices. As an important underpinning of rationing choice theory, an individual assigns an ordinal number over the available actions and this ordinal number is calculated by their utility function or payoff function. The individual's preference is expressed as the relationship between those ordinal assignments. In the implementation of congestion pricing, policy makers are imposing an additional high disutility to congested roads and therefore pushing some of the travelers to take alternative routes or shift to alternative departure times or even cancel the trips. However, congestion pricing suffers from public aversion as it creates burden on the motoring of low-income people and therefore doesn't help to alleviate social inequality. The concept of Tradable Mobility Credit (TMC) has been proposed by a group of researchers as another innovative application to facilitate dynamic traffic demand management and solve social inequality issues using pricing instruments. The concept of TMC is borrowed from carbon trading in environmental control. A limited quota of personal auto usage is issued to eligible travelers and credits can be traded in a free market fashion. This guarantees that the roadway usage does not exceed capacity while avoiding the negative effects of shortages normally associated with quotation systems. TMC is literally not a market-ready policy as the integration of the supporting infrastructures, including the trading market, the credit assignment component, and the credit charging component, has not been fully explored yet. Existing TMC research focuses on explaining and exploring the equilibrium condition through analytical methods such as mathematical modeling. Analytical models produce perfect convergence curves and deterministic equilibrium traffic flow patterns. Analytical models provide influential guidance for further works but the solution procedure may encounter problems when dealing with larger real world networks and scenarios. Meantime, current analytical models don't consider the microstructure of the credit trading market sufficiently while it's actually the most unique component of TMC system. Motivated by those concerns, an integrated TMC evaluation platform consisting of a policy making module and traveler behavior modules are proposed in this research. The concept of Agent-Based Modeling and Simulation (ABMS) is extensively adopted in this integrated platform as each individual traveler carries his/her personal memory across iterations. The goal of establishing this framework is to better predict a traveler's route choice and trading behavior if TMC is imposed and further provide intelligence to potential policy makers' decision making process. The proposed integrated platform is able to generate results at different aggregation levels, including both individual level microscopic behavior data as well as aggregated traffic flow and market performance data. In order to calibrate the proposed integrated platform, an online interactive experiment is designed based on an experimental economic package and a human research element with 22 participants has been conducted on this experiment platform to gather field data regarding a real person's route choice behavior and credit trading behavior in an artificial TMC system. Participants are recruited from forum, listserve, social media, etc. The calibrated platform is proved to have the ability to predict travelers' behavior accurately. A prototype market microstructure is proposed in this research as well and it is proved to be a cost-effective setting and resulted to a vast amount of economic saving given the fact that travelers would behave similar to the prediction generated by traveler behavior module. It's also demonstrated that the principle of Pareto-improving is not achieved in the proposed ABMS models.

This paper evaluates some arguments made against the conceptions of right action within virtue ethics. I argue that the different accounts of right action can meet the objections raised against them. Michael Slote‘s agent-based and Rosalind Hursthouses agent-focused account of right action give different judgments of right action but there seems to be a lack of real disagreement between the two accounts. I also argue that the concept of right action often has two important parts, relating to action guidance and moral appraisal, respectively, and that virtue ethics can deal with both without a concept of right action.

We construct and examine a model of adoption of a product or policy using, firstly, a system of differential equations and then secondly, through simulation, an agent- based model. Awareness must come before adoption, and we model this as a simple epidemic type model, where information is spread through advertising and contact with other agents in the population. Adoption is then conditional on awareness and occurs only if the agent finds the perceived cost acceptable. After simulating the system using an agent-based model, we introduce heterogeneity through the model parameters, which are then considered individual attributes and include influence rates, effectiveness of advertising, price sensitivity, and speed of adoption. We also examine the effects of various network topologies by organizing individuals into lattice and preferential attachment networks. From there, we add two extra components to the adoption mechanism by introducing a social influence factor by which an agent can be influenced by the adoption patterns of their neighbourhood, as well as a green factor, which assumes an environmental product or policy being adopted and is the likelihood that an individual will adopt based on environmental reasons alone.

Our epidemiology research found that the incident and prevalent rates for Diabetes mellitus (DM) and Diabetic End Stage Renal Disease (DM-ESRD) were at rise in Saskatchewan between year 1980 and 2005. Combining concerns regarding the rising trends reported by research studies with the concerns of the significant health and financial burden imposed by DM-ESRD on individuals and societies, we sought to project the number of DM-ESRD patients in Saskatchewan up to year 2025 with the cost required for caring for those patients. An agent-based model (ABM) is developed to simulate DM to ESRD progression, treatments for DM-ESRD patients, and the assessments and waiting list processes preparing patients for kidney transplants. The model parameters were estimated from a wide variety of data sources. The agent based modeling approach is chosen for projections regarding the DM-ESRD situation in Saskatchewan because of its advantage in capturing heterogeneities of individual patients, ability to retain biographical information on patients, capacity to capture time-varying competing risks, better presentations features and easy integration with existing models built in either agent based or System Dynamic methods. The approach was also attractive due to its flexibility for future expansion to represent social networks. The model projects the incident and prevalent case count, cost, and person years lived for the DM-ESRD population in Saskatchewan between year 1980 and 2025. The projections captured the great challenges brought by the fast growing number of DM-ESRD patients and substantial cost associated with managing the disease. In addition to producing projection results, the research presented here demonstrates how the model can be used by policy makers to experiment and evaluate different policy/interventions in a safe context. By capturing both the individual level records and population level statistics, the model provide a wealth of data for detailed analysis, which can help health policy makers gain insights in the current and future diabetic-ESRD situation in the province, aiding in resources planning for managing the fast-growing ESRD population and the growing need for dialysis services.