VTechWorks Repository :: Browsing by Author "Rahmandad, Hazhir"

Browsing by Author "Rahmandad, Hazhir"

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Collaborative learning in Open Source Software (OSS) communities: The dynamics and challenges in networked learning environments
Mitra, Raktim (Virginia Tech, 2011-06-14)
The proliferation of web based technologies has resulted in new forms of communities and organizations with enormous implications for design of learning and education. This thesis explores learning occurring within open source software (OSS) communities. OSS communities are a dominant form of organizing in software development with implications not only for innovative product development but also for the training of a large number of software developers. The central catalyst of learning within these communities is expert-novice interactions. These interactions between experts and novices or newcomers are critical for the growth and sustenance of a community and therefore it is imperative that experts are able to provide newcomers requisite advice and support as they traverse the community and develop software. Although prior literature has demonstrated the significance of expert-novice interactions, there are two central issues that have not been examined. First, there is no examination of the role of external events on community interaction, particularly as it relates to experts and novices. Second, the exact nature of expert help, particularly, the quantity of help and whether it helps or hinders newcomer participation has not been studied. This thesis studies these two aspects of expert-novice interaction within OSS communities. The data for this study comes from two OSS communities. The Java newcomer forum was studied as it provided a useful setting for examining external events given the recent changes in Java's ownership. Furthermore, the forum has a rating system which classifies newcomers and experienced members allowing the analysis of expert-novice interactions. The second set of data comes from the MySQL newcomer forum which has also undergone organizational changes and allows for comparison with data from the Java forum. Data were collected by parsing information from the HTML pages and stored in a relational database. To analyze the effect of external events, a natural experiment method was used whereby participation levels were studied around significant events that affected the community. To better understand the changes contextually, an extensive study of major news outlets was also undertaken. Findings from the external event study show significant changes in participation patterns, especially among newcomers in response to key external events. The study also revealed that the changes in participation of newcomers were observed even though other internal characteristics (help giving, expert participation) did not change indicating that external events have a strong bearing on community participation. The effect of expert advice was studied using a logistic regression model to determine how specific participation patterns in discussion threads led to the final response to newcomers. This was supported by social network analysis to visually interpret the participation patterns of experienced members in two different scenarios, one in which the question was answered and the other where it was not. Findings show that higher number of responses from experienced members did not correlate with a response. Therefore, although expert help is essential, non-moderated or unguided help can lead to conflict among experts and inefficient feedback to newcomers.
Design in the Modern Age: Investigating the Role of Complexity in the Performance of Collaborative Engineering Design Teams
Ambler, Nathaniel Palenaka (Virginia Tech, 2015-06-12)
The world of engineering design finds itself at a crossroads. The technical and scientifically rooted tools that propelled humankind into the modern age are now insufficient as evidenced by a growing number of failures to meet design expectations and to deliver value for users and society in general. In the empirical world, a growing consensus among many design practitioners has emerged that engineering design efforts are becoming too unmanageable and too complex for existing design management systems and tools. One of the key difficulties of engineering design is the coordination and management of the underlying collaboration processes. Development efforts that focus on the design of complex artefacts, such as a satellite or information system, commonly require the interaction of hundreds to thousands of different disciplines. What makes these efforts and the related collaboration processes complex from the perspective of many practitioners is the strong degree of interdependency between design decision-making occurring, often concurrently, across multiple designers who commonly reside in different organizational settings. Not only must a design account for and satisfice these dependencies, but it must remain also acceptable to all design participants. Design in effect represents a coevolution between the problem definition and solution, with a finalized design approach arising not from a repeatable series of mathematical optimizations but rather through the collective socio-technical design activities of a large collaboration of designers. Despite the importance of understanding design as a socio-technical decision-making entity, many of the existing design approaches ignore socio-technical issues and often view them as either too imprecise or too difficult to consider. This research provides a performance measurement framework to explore these factors by investigating design as a socio-technical complex adaptive collaborative process between the designer, artefact, and user (DAU). The research implements this framework through an agent-based model, the Complex Adaptive Performance Evaluation Method for Collaboration Design (C2D). This approach allows a design management analyst to generate insights about potential design strategies and mechanisms as they relate to design complexity by examining the simulated performance of a design collaboration as it explores theoretical design fitness landscapes with various degrees of ruggedness.
Development of an individual-based model for polioviruses: implications of the selection of network type and outcome metrics
Rahmandad, Hazhir; Hu, K.; Tebbens, R. J. D.; Thompson, Kimberly M. (Cambridge University Press, 2011-06-01)
We developed an individual-based (IB) model to explore the stochastic attributes of state transitions, the heterogeneity of the individual interactions, and the impact of different network structure choices on the poliovirus transmission process in the context of understanding the dynamics of outbreaks. We used a previously published differential equation-based model to develop the IB model and inputs. To explore the impact of different types of networks, we implemented a total of 26 variations of six different network structures in the IB model. We found that the choice of network structure plays a critical role in the model estimates of cases and the dynamics of outbreaks. This study provides insights about the potential use of an IB model to support policy analyses related to managing the risks of polioviruses and shows the importance of assumptions about network structure.
Dynamic Redundancy Management of Multisource Multipath Routing Integrated with Voting-based Intrusion Detection in Wireless Sensor Networks
Al-Hamadi, Hamid Helal (Virginia Tech, 2014-04-24)
Wireless sensor networks (WSNs) are frequently deployed unattended and can be easily captured or compromised. Once compromised, intrusion prevention methods such as encryption can no longer provide any protection, as a compromised node is considered a legitimate node and possesses the secret key for decryption. Compromised nodes are essentially inside attackers and can perform various attacks to break the functionality of the system. Thus, for safety-critical WSNs, intrusion detection techniques must be used to detect and remove inside attackers and fault tolerance techniques must be used to tolerate inside attackers to prevent security failure. In this dissertation research, we develop a class of dynamic redundancy management algorithms for redundancy management of multisource multipath routing for fault and intrusion tolerance, and majority voting for intrusion detection, with the goal of maximizing the WSN lifetime while satisfying application quality-of-service and security requirements, for base station based WSNs, homogeneous clustered WSNs, and heterogeneous clustered WSNs. By means of a novel model-based analysis methodology based on probability theory, we model the tradeoff between energy consumption vs. reliability, timeliness and security gain, and identify the optimal multisource multipath redundancy level and intrusion detection settings for maximizing the lifetime of the WSN while satisfying application quality-of-service requirements. A main contribution of our research dissertation is that our dynamic redundancy management protocol design addresses the issues of "how many paths to use" and "what paths to use" in multisource multipath routing for intrusion tolerance. Another contribution is that we take an integrated approach combining intrusion detection and tolerance in the protocol design to address the issue of "how much intrusion detection is enough" to prevent security failure and prolong the WSN lifetime time. We demonstrate resiliency of our dynamic redundancy management protocol design for intrusion detection and tolerance against sophisticated attacker behaviors, including selective and random capture, as well as persistent, random, opportunistic and insidious attacks, by model-based performance analysis with results supported by extensive simulation based on ns3.
Enhancing long-term forecasting: Learning from COVID-19 models
Rahmandad, Hazhir; Xu, Ran; Ghaffarzadegan, Navid (PLOS, 2022-05-01)
While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, and few other nuances, offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs).
Essays on Mathematical Modeling and Empirical Investigations of Organizational Learning in Cancer Research
Mahmoudi, Hesam (Virginia Tech, 2023-09-01)
After numerous renewals and reignitions since the initiation of the "War on Cancer" more than five decades ago, the recent reignition of "Moonshot to Cure Cancer" points to the systemic persistence of cancer as a major cause of loss of life and livelihood. Literature points to the diminishing returns of cancer research through time, as well as heterogeneities in cancer research centers' innovation strategies. This dissertation focuses on the strategic decision by cancer research centers to invest their resources in conducting early phases of clinical trials on new candidate drugs/treatments (resembling exploration) or late phases of clinical trials that push established candidates towards acquiring FDA approvals (resembling exploitation). The extensive clinical trials data suggests that cancer research centers are not only different in their emphasis on exploratory trials, but also in how their emphasis is changing over time. This research studies the dynamics of this heterogeneity in cancer research centers' innovation strategies, how experiential learning and capability development interact to cause dynamics of divergence among learning agents, and how the heterogeneity among cancer research centers' innovation strategies is affected by the dynamics of learning from experience and capability development. The findings of this dissertation shows that endogenous heterogeneities can arise from the process of learning from experience and accumulation of capabilities. It is also shown that depending on the sensitivity of the outcome of decisions to the accumulated capabilities, such endogenous heterogeneities can be value-creating and thus, justified. Empirical analysis of cancer clinical trials data shows that cancer research centers learn from success and failure of their previous trials to adopt more/less explorative tendencies. It also demonstrates that cancer research centers with a history of preferring exploratory or FDA trials have the tendency to increase their preference and become more specialized in one specific type (endogenous specialization). These behavioral aspects of the cancer research centers' innovation strategies provide some of the tools necessary to model the behavior of the cancer research efforts from a holistic viewpoint.
The Examination and Evaluation of Dynamic Ship Quiescence Prediction and Detection Methods for Application in the Ship-Helicopter Dynamic Interface
Sherman, Brook W. (Virginia Tech, 2007-04-16)
Motion sensitive operations at sea are conducted in an unpredictable environment. While occasionally these operations can be planned around suitable weather forecast or delayed until smoother motions are apparent, naval ships conducting flight operations may have little liberty in their mission planning and execution. Tools exist to translate the ocean's harsh conditions into discretely defined low motion operational periods. Particularly of interest, the identification of discrete lull periods or quiescence for shipboard helicopter operations can be better defined using a landing period indicator than with the current method of utilizing static deck angle measurements. While few of these systems exist, assessing their operational benefits is difficult due to a lack of well-defined performance metrics. This thesis defines and examines the use of two methodical approaches to evaluating Landing Period Indicators (LPIs) and their subject ship-helicopter dynamic interface system. First a methodology utilizing the comparison of a basic transparent algorithm is detailed and a case study employing this methodology is examined. Second, a system dynamics approach is taken to pilot workload analysis, utilizing a dynamic systems model characterizing a subset of the Dynamic Interface. This approach illustrates the realistic gains in understanding and development that can be accomplished by utilizing system dynamics in the analysis of the Dynamic Interface and LPI insertion.
Extending the System Dynamics Toolbox to Address Policy Problems in Transportation and Health
Seyed Zadeh Sabounchi, Nasim (Virginia Tech, 2012-03-16)
System dynamics can be a very useful tool to expand the boundaries of one's mental models to better understand the underlying behavior of systems. But despite its utility, there remains challenges associated with system dynamics modeling that the current research addresses by expanding the system dynamics modeling toolbox. The first challenge relates to imprecision or vagueness, for example, with respect to human perception and linguistic variables. The most common approach is to use table or graph functions to capture the inherent vagueness in these linguistic (qualitative) variables. Yet, combining two or more table functions may lead to further complexity and, moreover, increased difficulty when analyzing the resulting behavior. As part of this research, we extend the system dynamics toolbox by applying fuzzy logic. Then, we select a problem of congestion pricing in mitigating traffic congestion to verify the effectiveness of our integration of fuzzy logic into system dynamics modeling. Another challenge, in system dynamics modeling, is defining proper equations to predict variables based on numerous studies. In particular, we focus on published equations in models for energy balance and weight change of individuals. For these models there is a need to define a single robust prediction equation for Basal Metabolic Rate (BMR), which is an element of the energy expenditure of the body. In our approach, we perform an extensive literature review to explore the relationship between BMR and different factors including age, body composition, gender, and ethnicity. We find that there are many equations used to estimate BMR, especially for different demographic groups. Further, we find that these equations use different independent variables and, in a few cases, generate inconsistent conclusions. It follows then that selecting a single equation for BMI can be quite difficult for purposes of modeling in a systems dynamics context. Our approach involves conducting a meta-regression to summarize the available prediction equations and identifying the most appropriate model for predicting BMR for different sub-populations. The results of this research potentially could lead to more precise predictions of body weight and enhanced policy interventions to help mitigate serious health issues such as obesity.
Immunological, Epidemiological, and Economic modeling of HIV, Influenza, and Fungal Meningitis
Dorratoltaj, Nargesalsadat (Virginia Tech, 2016-07-28)
This dissertation focuses on immunological, epidemiological, and economic modeling of HIV, influenza, and fungal meningitis, and includes three research studies. In the first study on HIV, the study objective is to analyze the dynamics of HIV-1, CD4+ T cells and macrophages during the acute, clinically latent and late phases of HIV infection in order to predict their dynamics from acute infection to clinical latency and finally to AIDS in treatment naive HIV-infected individuals. The findings of the study show that the peak in viral load during acute HIV infection is due to virus production by infected CD4+ T cells, while during the clinically latent and late phases of infection infected macrophages dominate the overall viral production. This leads to the conclusion that macrophage-induced virus production is the significant driver of HIV progression from asymptomatic phase to AIDS in HIV-infected individuals. In the second study on influenza, the study objective is to estimate the direct and indirect epidemiological and economic impact of vaccine interventions during an influenza pandemic in Chicago, and assist in vaccine intervention priorities. Population is distributed among high-risk and non-high risk within 0-19, 20-64 and 65+ years subpopulations. The findings show that based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost-saving for all age and risk groups. In the third study on fungal meningitis, the study objective is to evaluate the effectiveness and cost of the fungal meningitis outbreak response in New River Valley of Virginia during 2012-2013, from the local public health department and clinical perspectives. We estimate the epidemiological effectiveness of this outbreak response to be 153 DALYs averted among the patients, and the costs incurred by the local health department and clinical facilities to be $30,413 and $39,580 respectively. Moving forward, multi-scale analysis of infectious diseases connecting the different scales of evolutionary, immunological, epidemiological, and economic dynamics has good potential to derive meaningful inferences for decision making in clinical and public health practice, and improve health outcomes.
Information Network Design for Lean Logistics
Chaudhari, Gaurav Singh (Virginia Tech, 2008-11-06)
Manufacturing supply chains are invariably dynamic and complicated in nature. Hence, steady state models are not sufficient for analyzing and designing supply chains. Models of supply chains must accurately capture their dynamic behavior, which is determined by the structure of the organization, and the policies adopted by management. System dynamics modeling provides a powerful framework for this purpose. The use of system dynamics models in supply chain management has thus far been limited to explaining phenomenon like the bullwhip effect, and for policy development. We provide a structured approach for policy design, which doesn't rely on any simulation experiments. Further, we study the impact that information network design has on the response of supply chains. We use a combinatorial approach to develop guidelines for information network design. Further, we examine the possibility of utilizing a PID information feedback structure to enhance the responsiveness of the supply chain. Lastly, we propose a combined feedback feed-forward information structure to enable a supply chain to rapidly respond to disturbances whose effects are known. The goal of this dissertation is to provide a structured approach for the design of information network structure, and operating policy.
Infrastructure Performance and Risk Assessment under Extreme Weather and Climate Change Conditions
Bhatkoti, Roma (Virginia Tech, 2016-07-19)
This dissertation explores the impact of climate change and extreme weather events on critical infrastructures as defined by US Department of Homeland Security. The focus is on two important critical infrastructure systems – Water and Transportation. Critical infrastructures are always under the risk of threats such as terrorist attacks, natural disasters, faulty management practices, regulatory policies, and defective technologies and system designs. Measuring the performance and risks of critical infrastructures is complex due to its network, geographic and dynamic characteristics and multiplicity of stakeholders associated with them. Critical infrastructure systems in crowded urban and suburban areas like the Washington Metropolitan Area (WMA) are subject to increased risk from geographic proximity. Moreover, climate is challenging the assumption of stationary (the idea that natural systems fluctuate within an unchanging envelope of variability) that is the foundation of water resource engineering and planning. Within this context, this research uses concepts of systems engineering such as 'systems thinking' and 'system dynamics' to understand, analyze, model, simulate, and critically assess a critical infrastructure system's vulnerability to extreme natural events and climate change. In most cases, transportation infrastructure is designed to withstand either the most extreme or close to the most extreme event that will add abnormal stresses on a physical structure. The system may fail to perform as intended if the physical structure faces an event larger than what it is designed for. The results of the transportation study demonstrate that all categories of roadways are vulnerable to climate change and that the magnitude of bridge vulnerability to future climate change is variable depending on which climate model projection is used. Results also show that urbanization and land use patterns affects the susceptibility of the bridge to failures. Similarly, results of the water study indicate that the WMA water supply system may suffer from water shortages accruing due to future droughts but climate change is expected to improve water supply reliability due to an upward trend in precipitation and streamflow.
Integrated Mobility and Service Management for Network Cost Minimization in Wireless Mesh Networks
Li, Yinan (Virginia Tech, 2012-04-30)
In this dissertation research, we design and analyze integrated mobility and service management for network cost minimization in Wireless Mesh Networks (WMNs). We first investigate the problem of mobility management in WMNs for which we propose two efficient per-user mobility management schemes based on pointer forwarding, and then a third one that integrates routing-based location update and pointer forwarding for further performance improvement. We further study integrated mobility and service management for which we propose protocols that support efficient mobile data access services with cache consistency management, and mobile multicast services. We also investigate reliable and secure integrated mobility and service man agement in WMNs, and apply the idea to the design of a protocol for secure and reliable mobile multicast. The most salient feature of our protocols is that they are optimal on a per-user basis (or on a per-group basis for mobile multicast), that is, the overall network communication cost incurred is minimized for each individual user (or group). Per-user based optimization is critical because mobile users normally have vastly different mobility and service characteristics. Thus, the overall cost saving due to per-user based optimization is cumulatively significant with an increasing mobile user population. To evaluate the performance of our proposed protocols, we develop mathematical models and computational procedures used to compute the network communication cost incurred and build simulation systems for validating the results obtained from analytical modeling. We identify optimal design settings under which the network cost is minimized for our mobility and service management protocols in WMNs. Intensive comparative performance studies are carried out to compare our protocols with existing work in the literature. The results show that our protocols significantly outperform existing protocols under identical environmental and operational settings. We extend the design notion of integrated mobility and service management for cost minimization to MANETs and propose a scalable dual-region mobility management scheme for location-based routing. The basic design concept is to use local regions to complement home regions and have mobile nodes in the home region of a mobile node serve as location servers for that node. We develop a mathematical model to derive the optimal home region size and local region size under which overall network cost incurred is minimized. Through a comparative performance study, we show that dual-region mobility management outperforms existing mobility management schemes based on static home regions.
Measuring the Efficiency of Highway Maintenance Operations: Environmental and Dynamic Considerations
Fallah-Fini, Saeideh (Virginia Tech, 2010-12-10)
Highly deteriorated U.S. road infrastructure, major budgetary restrictions and the significant growth in traffic have led to an emerging need for improving efficiency and effectiveness of highway maintenance practices that preserve the road infrastructure so as to better support society's needs. Effectiveness and efficiency are relative terms in which the performance of a production unit or decision making unit (DMU) is compared with a benchmark (best practice). Constructing the benchmark requires making a choice between an "estimation approach" based on observed best practices (i.e., using data from input and output variables corresponding to observed production units (DMUs) to estimate the benchmark with no elaboration on the details of the production process inside the black box) or an "engineering approach" to find the superior blueprint (i.e., focusing on the transformation process inside the black box for a better understanding of the sources of inefficiencies). This research discusses: (i) the application of the estimation approach (non-parametric approach) for evaluating and comparing the performance of different highway maintenance contracting strategies (performance-based contracting versus traditional contracting) and proposes a five-stage meta-frontier and bootstrapping analytical approach to account for the heterogeneity in the DMUs, the resulting bias in the estimated efficiency scores, and the effect of uncontrollable variables; (ii) the application of the engineering approach by developing a dynamic micro-level simulation model for the highway deterioration and renewal processes and its coupling with calibration and optimization to find optimum maintenance policies that can be used as a benchmark for evaluating performance of road authorities. This research also recognizes and discusses the fact that utilization of the maintenance budget and treatments that are performed in a road section in a specific year directly affect the road condition and required maintenance operations in consecutive years. Given this dynamic nature of highway maintenance operations, any "static" efficiency measurement framework that ignores the inter-temporal effects of inputs and managerial decisions in future streams of outputs (i.e., future road conditions) is likely to be inaccurate. This research discusses the importance of developing a dynamic performance measurement framework that takes into account the time interdependence between the input utilization and output realization of a road authority in consecutive periods. Finally, this research provides an overview of the most relevant studies in the literature with respect to evaluating dynamic performance and proposes a classification taxonomy for dynamic performance measurement frameworks according to five issues. These issues account for major sources of the inter-temporal dependence between input and output levels over different time periods and include the following: (i) material and information delays; (ii) inventories; (iii) capital or generally quasi-fixed factors and the related topic of embodied technological change; (iv) adjustment costs; and (v) incremental improvement and learning models (disembodied technological change). In the long-term, this line of research could contribute to a more efficient use of societal resources, greater level of maintenance services, and a highway and roadway system that is not only safe and reliable, but also efficient.
A missing behavioural feedback in COVID-19 models is the key to several puzzles
Rahmandad, Hazhir; Xu, Ran; Ghaffarzadegan, Navid (BMJ, 2022-10-25)
Summary: ⇒ Human actions have played a key role in shaping the COVID-19 pandemic patterns. While theoretically recognised, existing models of epidemics often do not endogenously capture many of the feedback loops connecting people’s choices and epidemic dynamics, for example, adoption of non-pharmaceutical interventions (NPIs) by individuals and governments shapes disease transmission, which in turn alters perceived risks and future NPI adoption. ⇒ Such ‘risk-driven response’ feedback is central to explaining important empirical puzzles of the COVID-19 pandemic, including the convergence of reproduction number to 1 across nations, multiple waves of pandemic, mortality variance and limited trade-off between economic and health outcomes in adoption of NPIs. Capturing that feedback also enhances pandemic forecasting and offers distinct and more effective vaccination strategies. ⇒ Much remains to be explored in modelling diverse behavioural feedbacks, from endogenous testing and vaccination choices to the building of infrastructure for various responses. Integrating those with epidemiological models offers promising new discoveries and enhanced policy design.
Modeling and Assessing Crossing Elimination as a Strategy to Reduce Evacuee Travel Time
Jahangiri, Arash (Virginia Tech, 2012-12-13)
During evacuations, emergency managers and departments of transportation seek to facilitate the movement of citizens out of impacted or threatened areas. One strategy they may consider is crossing elimination, which prohibits certain movements at intersections, that may be permissible under normal operating conditions. A few previous studies examined this strategy in conjunction with contra-flow operations, but fewer have considered crossing elimination by itself. This study helps fill the existing gap in knowledge of the individual effects of crossing elimination. A bi-level model that iterates between optimization and simulation is developed to determine the optimal configuration of intersection movements from a set of pre-specified possible configurations for intersections in a given area. At the upper level, evacuees' travel time is minimized and at the lower level, traffic is assigned to the network with the traffic assignment-simulation software DynusT. The overall model is solved with a simulated annealing heuristic and applied to a real case study to assess the impact of crossing elimination. Three scenarios are developed and examined using the solution method proposed in this research. These scenarios are developed using combinations of two elements: (1) Evacuee destination distributions, and (2) Evacuee departure time distributions. Results showed about 3-5 percent improvement in total evacuee travel time can be achieved in these scenarios. Availability of through movements at intersections and existing merging points in movement configurations are the two factors influencing the selection of movement configurations.
Modeling and estimating the feedback mechanisms among depression, rumination, and stressors in adolescents
Hosseinichimeh, Niyousha; Wittenborn, Andrea K.; Rick, Jennifer; Jalali, Mohammad S.; Rahmandad, Hazhir (PLOS, 2018-09-27)
The systemic interactions among depressive symptoms, rumination, and stress are important to understanding depression but have not yet been quantified. In this article, we present a system dynamics simulation model of depression that captures the reciprocal relationships among stressors, rumination, and depression. Building on the response styles theory, this model formalizes three interdependent mechanisms: 1) Rumination contributes to `keeping stressors alive'; 2) Rumination has a direct impact on depressive symptoms; and 3) Both `stressors kept alive' and current depressive symptoms contribute to rumination. The strength of these mechanisms is estimated using data from 661 adolescents (353 girls and 308 boys) from two middle schools (grades 6–8). These estimates indicate that rumination contributes to depression by keeping stressors `alive' — and the individual activated — even after the stressor has ended. This mechanism is stronger among girls than boys, increasing their vulnerability to a rumination reinforcing loop. Different profiles of depression emerge over time depending on initial levels of depressive symptoms, rumination, and stressors as well as the occurrence rate for stressors; levels of rumination and occurrence of stressors are stronger contributors to long-term depression. Our systems model is a steppingstone towards a more comprehensive understanding of depression in which reinforcing feedback mechanisms play a significant role. Future research is needed to expand this simulation model to incorporate other drivers of depression and provide a more holistic tool for studying depression.
Modeling and Measuring Affordability as Fitness
Keller, George Burleigh (Virginia Tech, 2012-01-27)
Affordability of products and services is an economic benefit that should accrue to consumers, whether they are corporations, government agencies or individuals. This concept of affordability goes beyond conventional wisdom that considers affordability as the ability to pay the price of a product or service. This dissertation defines and explores a broader concept of affordability – one of fitness to perform at the level of quality required by the consumer, to perform at that level whenever the product or service is used, and to do so with minimum consumption of resources. This concept of affordability is applied to technological systems by using the complexity sciences concept of fitness as the metaphor for technological systems' fitness. During a system design evolution, the specific design outcome is determined by that set of design search paths followed – it is path dependent. Dynamic mechanisms create, dictate and maintain path dependence. Initial conditions define the start and direction of a path. During subsequent design steps, positive feedback influences the designer to continue on that path. This dissertation describes underlying mechanisms that create, dictate and maintain path dependence; discusses the effects of path dependence on system design and system affordability; models these effects using system dynamics modeling; and suggests actions to address its effects. This dissertation also addresses several types of fitness landscapes, and suggests that the Data Envelopment Analysis (DEA) solution space is a form of fitness landscape suitable for evaluating the efficiency, and thus the fitness, of research and development (R&D) projects. It describes the use of DEA to evaluate and select Department of Defense (D0D) R&D projects as a new application of DEA.
Modeling Driver Behavior at Signalized Intersections: Decision Dynamics, Human Learning, and Safety Measures of Real-time Control Systems
Ghanipoor Machiani, Sahar (Virginia Tech, 2015-01-24)
Traffic conflicts associated to signalized intersections are one of the major contributing factors to crash occurrences. Driver behavior plays an important role in the safety concerns related to signalized intersections. In this research effort, dynamics of driver behavior in relation to the traffic conflicts occurring at the onset of yellow is investigated. The area ahead of intersections in which drivers encounter a dilemma to pass through or stop when the yellow light commences is called Dilemma Zone (DZ). Several DZ-protection algorithms and advance signal settings have been developed to accommodate the DZ-related safety concerns. The focus of this study is on drivers' decision dynamics, human learning, and choice behavior in DZ, and DZ-related safety measures. First, influential factors to drivers' decision in DZ were determined using a driver behavior survey. This information was applied to design an adaptive experiment in a driving simulator study. Scenarios in the experimental design are aimed at capturing drivers learning process while experiencing safe and unsafe signal settings. The result of the experiment revealed that drivers do learn from some of their experience. However, this learning process led into a higher level of risk aversion behavior. Therefore, DZ-protection algorithms, independent of their approach, should not have any concerns regarding drivers learning effect on their protection procedure. Next, the possibility of predicting drivers' decision in different time frames using different datasets was examined. The results showed a promising prediction model if the data collection period is assumed 3 seconds after yellow. The prediction model serves advance signal protection algorithms to make more intelligent decisions. In the next step, a novel Surrogate Safety Number (SSN) was introduced based on the concept of time to collision. This measure is applicable to evaluate different DZ-protection algorithms regardless of their embedded methodology, and it has the potential to be used in developing new DZ-protection algorithms. Last, an agent-based human learning model was developed integrating machine learning and human learning techniques. An abstracted model of human memory and cognitive structure was used to model agent's behavior and learning. The model was applied to DZ decision making process, and agents were trained using the driver simulator data. The human learning model resulted in lower and faster-merging errors in mimicking drivers' behavior comparing to a pure machine learning technique.
Modeling Multi-level Incentives in Health Care: A Multiscale Decision Theory Approach
Zhang, Hui (Virginia Tech, 2016-04-08)
Financial incentives offered by payers to health care providers and patients have been identified as a key mechanism to lower costs while improving quality of care. How to effectively design incentive programs that can align the varying objectives of health care stakeholders, as well as predict programs' performance and stakeholders' decision response is an unresolved research challenge. The objective of this study is to establish a novel approach based on multiscale decision theory (MSDT) that can effectively model and efficiently analyze such incentive programs, and the complex health care system in general. The MSDT model captures the interdependencies of stakeholders, their decision processes, uncertainties, and how incentives impact decisions and outcomes at the payer, hospital, physician, and patient level. In the first part of this thesis, we study the decision processes of agents pertaining to the investment and utilization of imaging technologies. We analyze the payer-hospital-physician relationships and later extend the model to include radiologist and patient as major stakeholders in the second part of this thesis. We focus on a specific incentive program, the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs). The multi-level interactions between agents are mathematically formulated as a sequential non-cooperative game. We derive the equilibrium solutions using the subgame perfect Nash equilibrium (SPNE) concept and the backward induction principle, and determine the conditions under which the MSSP incentive leads to the desired outcomes of cost reduction and quality of care improvements. In the third part of this thesis, we study the multi-level decision making in chronic disease management. We model and analyze patients' and physicians' decision processes as a general-sum stochastic game with perfect information and switching control structure. We incorporate the Health Belief Model (HBM) as the theoretical foundation to capture the behavioral aspect of agents. We analyze how incentives and interdependencies affect patients' engagement in health-promoting activities and physicians' delivery of primary care services. We show that a re-alignment of incentives can improve the effectiveness of chronic disease management.
Modeling the dynamics of software competition to find appropriate openness and pricing strategy
Ratnarajah, Thanujan (Virginia Tech, 2008-01-25)
Software firms can use open source development model combined with proprietary development model to increase their profitability. Open source development models can help software firms create products with better technical features at a lower price. Since open source development is a community based development method the popularity of the software among customers will also increase. Using open source development method with proprietary method will also require firms to sell the product at a lower price. This creates a challenge for the firms to find the optimal price and level of openness to maximize their profit. Using the systems dynamics methodology, development, employment and customer choice for a typical software firm was captured in a simulation model to understand the dynamics of the software firm in a competitive market and to find the optimal level of openness and price. The model was built based on previous research literature, various software models and from the author's understanding of the software industry. Our analysis suggests that in a fast evolving market where customers spend less time researching and shopping for a software product (Antivirus market VS Operating Systems market), companies should maintain lower level of openness and higher proprietary type development to increase the Net Present Value of the organization. The software firm could benefit from a higher level of openness in a market where the customers base their purchasing decision on the popularity and compatibility of the software and strong network effects are present (e.g. Business intelligence software).

Browsing by Author "Rahmandad, Hazhir"

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