Modeling and simulation of military operations are becoming popular with the widespread application of artificial intelligence methods. As the decision makers would like to analyze the results of the simulations in greater details, entity-level simulation of physical world and activities of actors (soldiers, tanks, etc) is unavoidable. In this thesis, a multiagent framework for simulating task driven autonomous activities of actors or group of actors is proposed. The framework is based on BDI-architecture where an agent is composed of beliefs, goals and plans. Besides, an agent team is organized hierarchically and decisions at different levels of the hierarchy are governed by virtual command agents with their own beliefs, goals and plans. The framework supports an interpreter that realizes execution of single or multiagent plans coherently. The framework is implemented and a case study demonstrating the capabilities of the framework is carried out.

Managing a sustainable built environment with a large number of buildings rests on the ability to assess and improve the performance of the building stock over time. Building stock models are cornerstones to the assessment of the combined impact of energy-related building interventions across different spatial and temporal scales. However, such models, particularly those accounting for both physical formulation and social behaviors of the underlying buildings, are still in their infancy. This research strives to more thoroughly examine how buildings perform aggregately in energy usage by focusing on how to tackled three major technical challenges: (1) quantifying building energy performance in an objective and scalable manner, (2) mapping building stock model space to real-world data space, and (3) quantifying and evaluating energy intervention behaviors of a building stock. This thesis hypothesizes that a new paradigm of aggregation of large-scale building stocks can lead to (1) an accurate and efficient intervention analysis model and (2) a functionally comprehensive decision support tool for building stock energy intervention analysis. Specifically, this thesis presents three methodologies. To address the first challenge, this thesis develops a normative building physical energy model that can rapidly estimate single building energy performance with respect to its design and operational characteristics. To address the second challenge, the thesis proposes a statistical procedure using regression and Markov chain Monte Carlo (MCMC) sampling techniques that inverse-estimate building parameters based on building stock energy consumption survey data. The outcomes of this statistical procedure validate the approach of using prototypical buildings for two types of intervention analysis: energy retrofit and demand response. These two cases are implemented in an agent-based modeling and simulation (ABMS) framework to tackle the third challenge. This thesis research contributes to the body of knowledge pertaining to building energy modeling beyond the single building scale. The proposed framework can be used by energy policy makers and utilities for the evaluation of energy retrofit incentives and demand-response program economics.

The network externality function plays a very important role in the study of economic network industries. Moreover, the consumer group dynamic interactions coupled with network externality concept is going to play a dominant role in the network goods in the 21st century. The existing literature is stemmed on a choice of externality function with certain quantitative properties. The utility function coupled with the network externality function is used to investigate static properties of rational equilibrium. The aim of this work is to systematically initiate a development of quantitative effects of the concept of network externality and its influence on the characteristics of network market equilibrium. We introduce several basic concepts, notably, network externality process and network goods. Formulating a principle of network externality, we developed a mathematical dynamic model (1) for the network externality process. A closed form solution of the mathematical model was determined and analyzed (2). The presented qualitative and quantitative analysis provides a systematic and unified way of constructing the existing network externality function. The solution process is called "Generalized Network Externality Function (GNEF)". Moreover, our study of quantitative description, parametric representation of attributes and sensitivity analysis of network externality process provides a tool for planning, policy and performance for network goods (3). In the absence of desired data set, we presented an illustration to exhibit the significance of GNEF. We used two types of data sets on the US banking asset and deposit. Employing nonlinear regression methods and data sets, we developed statistical models for the US banking asset and deposit, and constructed two normalized the US banking deposit models (4). Finally, using the concept of theory of relative growth and GNEF (4), we developed two dynamic models for the network externality for the US banking asset with respect to the US banking deposit as a financial market share (5). Incorporating the GNEF (2) in the consumer utility function, a concept of market share adjustment function is introduced and utilized to develop dynamic models for existing rational and static expectation processes (6). In fact, the role and scope of dynamic models of market share adjustment process are extended to the well-known adaptive expectation and its extension process (7). Using a fixed point theorem and the method of upper and lower solutions of discrete time processes, the existence of equilibrium states of developed dynamic models of market share adjustment processes are established in a systematic way (8). Furthermore, the qualitative properties (stability and oscillatory) of equilibrium states are investigated in terms of model and speed of adjustment parameters. Moreover, the system parameter space is decomposed according to qualitative properties (stability, instability and oscillatory) and the type of expectation processes. Very recently, the idea of local network externality is utilized to characterize the rational equilibrium (under fulfilled expectation assumptions). From the study on two-scale network dynamic model of human mobility process an eco-socio-culture interactions, we note that heterogeneity in the network goods consumer community generates a local network externality. Furthermore, dynamic models of adaptive expectation processes (6,7) for network goods provide tool to extend the characterization of rational equilibrium study to static, current and lagged adaptive types equilibriums. Here, we treat the consumer decision to be a dynamic process. We formulate a dynamic structural representation of a consumer network structure, structure of utility function and decision rule under the influence of local network externality concept (9). For the consumer network structure, we generalize the one-dimensional Hotelling location line model to multi-dimensional location (10). This formulation generates a mathematical model for a consumer decision dynamic process (11). The byproduct of the dynamic model leads to an agent-based simulation model (12). The simulation model is employed to investigate different types of consumer decision dynamic market equilibriums. Moreover, prototype illustrations are given to exhibit the association between network attributes and its market equilibriums. We extend the work of two firms (duopoly) into multi-firms (oligopoly and monopolistic competition). This work shed light on the policies for manager to meet performance goal of their firm in network goods industry. In future, we propose to extend this work to incorporate random fluctuations, to remove restrictions and the local and global economic framework in the 21st century.

This thesis examines the time-use patterns of adults in dual-earner households with and without children as a function of several individual and household socio-demographics and employment characteristics. A disaggregate activity purpose classification including both in-home and out-of-home activity pursuits is used because of the travel demand relevance of out-of-home pursuits, as well as to examine both mobility-related and general time-use related social exclusion and time poverty issues. The study uses the Nested Multiple Discrete Continuous Extreme Value (MDCNEV) model, which recognizes that time-decisions entail the choice of participating in one or more activity purposes along with the amount of time to invest in each chosen activity purpose, and allows generic correlation structures to account for common unobserved factors that might impact the choice of multiple alternatives. The 2010 American Time Use Survey (ATUS) data is used for the empirical analysis. A major finding of the study is that the presence of a child in dual-earner households not only leads to a reduction in in-home activity participation but also a substantially larger decrease in out-of-home activity participation, suggesting a higher level of mobility-related social exclusion relative to overall time-use social exclusion. To summarize, the results in the thesis underscore the importance of re-designing work policies in the United States to facilitate a reduction in work-family conflict in dual-earner families. / text

Doctor of Philosophy / Department of Industrial & Manufacturing Systems Engineering / David H. Ben-Arieh / Chih-Hang Wu / Patients who suffer from sepsis or septic shock are of great concern in the healthcare system. Recent data indicate that more than 900,000 severe sepsis or septic shock cases developed in the United States with mortality rates between 20% and 80%. In the United States alone, almost $17 billion is spent each year for the treatment of patients with sepsis. Clinical trials of treatments for sepsis have been extensively studied in the last 30 years, but there is no general agreement of the effectiveness of the proposed treatments for sepsis. Therefore, it is necessary to find accurate and effective tools that can help physicians predict the progression of disease in a patient-specific way, and then provide physicians recommendation on the treatment of sepsis to lower risk for patients dying from sepsis. The goal of this research is to develop a risk assessment tool and a risk management tool for sepsis. In order to achieve this goal, two system dynamic mathematical models (SDMMs) are initially developed to predict dynamic patterns of sepsis progression in innate immunity and adaptive immunity. The two SDMMs are able to identify key indicators and key processes of inflammatory responses to an infection, and a sepsis progression. Second, an integrated-mathematical-multi-agent-based model (IMMABM) is developed to capture the stochastic nature embedded in the development of inflammatory responses to a sepsis. Unlike existing agent-based models, this agent-based model is enhanced by incorporating developed SDMMs and extensive experimental data. With the risk assessment tools, a Markov decision process (MDP) is proposed, as a risk management tool, to apply to clinical decision-makings on sepsis. With extensive computational studies, the major contributions of this research are to firstly develop risk assessment tools to identify the risk of sepsis development during the immune system responding to an infection, and secondly propose a decision-making framework to manage the risk of infected individuals dying from sepsis. The methodology and modeling framework used in this dissertation can be expanded to other disease situations and treatment applications, and have a broad impact to the research area related to computational modeling, biology, medical decision-making, and industrial engineering.

This dissertation describes the development of CAMBIUM, an agent-based forest sector model for large-scale strategic analysis. This model is designed as a decision support tool for assessing the effect that changes in forest product demand and resource inventories can have on the structure and economic viability of the forest sector. CAMBIUM complements existing forest sector models by modeling aggregate product supply as an emergent property of individual companies’ production decisions and stand-level ecological processes. Modeling the forest products sector as a group of interacting autonomous agents makes it possible to introduce production capacity dynamics and the potential for mill insolvencies as factors in modeling the effects of market and forest inventory based disturbances. This thesis contains four main manuscripts. In the first manuscript I develop and test a dispersal algorithm that projects aggregated forest inventory information onto a lattice grid. This method can be used to generate ecologically and statistically consistent datasets where high-quality spatial inventory data is otherwise unavailable. The second manuscript utilizes this dataset in developing a provincial-level resource dynamics model for assessing the timber supply effects of introducing weevil-resistant spruce. This model employs a stand-level approach to simulating weevil infestation and associated merchantable volume losses. Provincial-level impacts are determined by simulating harvest activities over a 350 year time horizon. In the third manuscript I shift the focus to interactions between forest companies. I analyze the effects of strategic decisions on sector structure by developing CAMBIUM as an agent-based model of competition and industry structure evolution. The forest sector is modeled as a group of autonomous, interacting agents that evolve and compete within the limitations posed by resource inventories and product demand. In the final manuscript I calibrate CAMBIUM to current conditions in the British Columbia forest sector. Industry agents compete for roundwood inputs, as well as for profits in finished product markets for pulp, panel products, and lumber. To test the relevance and utility of this model, CAMBIUM is used to quantify the cumulative impacts of a market downturn for forest products and mountain pine beetle induced timber supply fluctuations on the structure of the forest sector.

Military analysis is becoming more reliant on constructive simulations for campaign modeling. Requirements for force-level capabilities, distributed command and control architectures, network centric operations, and increased levels of systems and operational integration are straining the analysis tools of choice. The models constructed are becoming more complex, both in terms of their composition and their behavior. They are complex in their composition because they are constituted from a large number of entities that interact nonlinearly through non-trivial networks and in their behavior because they display emergent characteristics. The modeling and simulation paradigm of choice for analyzing these systems of systems has been agent-based modeling and simulation. This construct is the most capable in terms of the characteristics of complex systems that it can capture, but it is the most demanding to construct, execute, verify and validate. This thesis is focused around two objectives. The first is to study the possibility of being able to compare two or more large-scale system architectures' capabilities without resorting to full-scale agent-based modeling and simulation. The second objective is to support the quantitative identification of the critical systems that compose the large-scale system architecture. The second objective will be crucial in the cases where a constructive simulation is the only option to capture the required behaviors of the complex system being studied. The enablers for this thesis are network modeling, graph theory, and in particular, spectral graph theory. The first hypothesis, stemmed from the first objective, states that if the capability of an architecture can be described as a series of functional cycles through the systems that compose them, then a simple network modeling construct can be employed to compare the different architectures' capabilities. The objective led to the second hypothesis, which states that a ranking based on the spectral characteristics of the network of functional interactions indicates the most critical systems. If modeling effort is focused on these systems, then the modeler can obtain the maximum fidelity model for the minimum effort.

Thesis (M.C.S.)--Miami University, Dept. of Computer Science and Systems Analysis, 2009. / Title from first page of PDF document. Includes bibliographical references (p. 72-75).

Chronic pain has no cure. It is a lifelong condition presenting a growing concern due to its high occurrence and effects on every facet of life. It cost about $635 billion each year in medical treatment and lost productivity (IOM, 2011). The management of chronic pain using prescription painkiller opioids has increased drastically in the last two decades, leading to a consequential increase in deaths from chronic opioid use. This Plan-Do-Study-Act quality improvement project investigates the problem of the prevalence of opioid prescription using agent-based computational modeling method. The simulation models the interaction of 50 patient-agents with pain self-management messages in an episode of 50 patient iterations (visits) for 10 simulated years. This interaction generates health benefit and risk outcomes represented by wellness data obtained when messages are processed. As the simulation runs, data are dynamically captured and visualized using wellness charts, time series plots, and benefit and risk regression plots. The result of the project provides evidence for research and practice on the process of achieving more impact of programs based on administering pain self-management education to patients with chronic non-cancer pain who are currently on opioid therapy and on the process of customizing interventions that might take advantage of the conditions of behavior change driven by pain self-management messages. The tools and the evidences in this project are highly recommended to nurse practitioners primary care providers involve with providing care to the vulnerable groups of patient with chronic non-cancer pain. These evidences might inform the formation of self-management interventions that might lead to a decline in opioid use and prescription and accelerate the acceptance of self-management practices.

Title: Computer Modeling of Tissue Development Author: Vojtěch Bednář Department: Department of applied mathematics Supervisor: Doc. RNDr. Zdeněk Hedrlín, CSc. Abstract: This thesis describes hybrid individual cell-based approach to modeling of systems of biological cells. In the first part reaction-diffusion model of environment is introduced together with vax equilibrium and model of a cell based on zygotic graph and cummulative states. Further, simulations modeling three biologically motivated situations are introduced: Lumen formation, tumor growth, and cellular migration in chronic inflammation. The first model shows a scenario of hollow structure formation based on directional division and cellular migration. The second model is concerned with the growth of a progeny of a slightly damaged cell. The resulting tumor exhibits three stages of malign transformation. Further, emergence of an aggressive tumor without detectable precursor is observed on one hand and a continual transformation of a benign neoplasm into a malign one is seen on the other hand. Each of these cases is a consequence of different parametrization of the model situation. The last model analyses the role of membrane enzymatic activity in migrating cells of the immune system in chronic inflammation. In this model it is observed that...