VTechWorks Repository :: Browsing by Author "Ghaffarzadegan, Navid"

Browsing by Author "Ghaffarzadegan, Navid"

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Analysis of Information Diffusion through Social Media
Khalili, Nastaran (Virginia Tech, 2021-06-16)
The changes in the course of communication changed the world from different perspectives. Public participation on social media means the generation, diffusion, and exposure to a tremendous amount of user-generated content without supervision. This four-essay dissertation analyzes information diffusion through social media and its opportunities and challenges through management systems engineering and data analytics. First, we evaluate how information can be shared to reach maximum exposure for the case on online petitions. We use system dynamics modeling and propose policies for campaign managers to schedule the reminders they send to have the highest number of petition signatures. We find that sending reminders is more effective in the case of increasing the signature rate. In the second essay, we investigate how people build trust/ mistrust in science during an emergency. We use data analytics methods on more than 700,000 tweets containing keywords of Hydroxychloroquine and chloroquine, two candidate medicines, to prevent and cure patients infected with COVID-19. We show that people's opinions are concentrated in the case of polarity and spread out in the case of subjectivity. Also, they tend to share subjective tweets than objective ones. In the third essay, building on the same dataset as essay two, we study the changes in science communication during the coronavirus pandemic. We used topic modeling and clustered the tweets into seven different groups. Our analysis suggests that a highly scientific and health-related subject can become political in the case of an emergency. We found that the groups of medical information and research and study have fewer tweets than the political one. Fourth, we investigated fake news diffusion as one of the main challenges of user-generated content. We built a system dynamics model and analyzed the effects of competition and correction in combating fake news. We show that correction of misinformation and competition in fake news needs a high percentage of participation to be effective enough to deal with fake news.
Application of Systems Engineering Analysis Methods to Examine Engineering Transfer Student Persistence
Smith, Natasha Leigh (Virginia Tech, 2020-01-20)
The demand for engineering graduates in the United States continues to grow, yet the number of students entering post-secondary education is declining, and graduation rates have seen little to no change over the last several decades. Engineering transfer students are a growing population and can help meet the nation's needs, however, there is little research on the persistence of this population after they transfer to the receiving institution. Student persistence is dependent on a complex set of interactions over time. Management systems engineering provides a framework for working with complex systems through system analysis and design, with a focus on the interactions of the system components. This research includes multiple management systems engineering analysis methods used to define and develop a systems view of engineering transfer student persistence. This work includes a comprehensive literature review to identify factors affecting engineering transfer student persistence, an empirical analysis of an institutional dataset, and development of a simulation model to demonstrate the throughput of engineering transfer student. Findings include 34 factors identified in the literature as affecting engineering student persistence. A review of the literature also highlighted two important gaps in the literature, including a focus on post-transfer success almost exclusively in the first post-transfer year and a significant interest in vertical transfer students, with little consideration given to lateral transfer students. The empirical analysis addressed the gaps found in the literature. Vertical and lateral engineering transfer students were found to experience different levels of transfer shock which also impacts their 4-year graduation rates. The analysis also found transfer shock was not unique to the first post-transfer term, it was also present in the second and third post-transfer terms, and reframed as transfer adjustment. The simulation model uncovers leaving patterns of engineering transfer students which include the students leaving engineering in the second year, as well as those graduating with an engineering degree in the third year. Overall this research identifies explicit factors that affect engineering transfer student persistence and suggests a new systems engineering approach for understanding student persistence and how institutions can affect change.
Can a Patient's In-Hospital Length of Stay and Mortality Be Explained by Early-Risk Assessments?
Azadeh-Fard, Nasibeh; Ghaffarzadegan, Navid; Camelio, Jaime A. (PLOS, 2016-09-15)
Objective To assess whether a patient’s in-hospital length of stay (LOS) and mortality can be explained by early objective and/or physicians’ subjective-risk assessments. Data Sources/Study Setting Analysis of a detailed dataset of 1,021 patients admitted to a large U.S. hospital between January and September 2014. Study Design We empirically test the explanatory power of objective and subjective early-risk assessments using various linear and logistic regression models. Principal Findings The objective measures of early warning can only weakly explain LOS and mortality. When controlled for various vital signs and demographics, objective signs lose their explanatory power. LOS and death are more associated with physicians’ early subjective risk assessments than the objective measures. Conclusions Explaining LOS and mortality require variables beyond patients’ initial medical risk measures. LOS and in-hospital mortality are more associated with the way in which the human element of healthcare service (e.g., physicians) perceives and reacts to the risks.
Closing the Road Infrastructure Gap: Analysis of Expenditure Dynamics and Public-Private Partnership Shaping Challenges
Guevara Maldonado, Jose Alberto (Virginia Tech, 2017-06-26)
The global infrastructure gap has continually widened over the last few decades. Industry reports and academic publications suggest that, in terms of road infrastructure, both advanced and developing economies have not paid sufficient attention to modernize their infrastructure assets. A wider road infrastructure gap signifies that highway conditions have declined because governments have not had enough resources for maintenance and rehabilitation. In the same way, it also indicates that congestion levels have grown and the level of service in most road networks has dropped because public agencies have not had sufficient funds to generate new highways and expand existing corridors. This dissertation, therefore, provided insights into the difficulties associated with improving the existing highway assets and the barriers related to expanding the current roadway capacity through public-private partnerships (PPPs). The research involved three interdependent studies. In the first study, I examined the continuous deterioration of the US highway system through a system dynamics model, which focused on the dynamics of capital investments and maintenance expenditures in the US road infrastructure. The results confirmed that the American highway system is currently stuck in a capability trap. This makes it difficult for the system to improve at the rates required by the country's economic growth. In my second investigation, my attention shifted toward the governance challenges related to building new roads and expanding highway capacity through PPPs. I developed a systems map of governance variables informed by past-published evidence from actual projects. By specifically examining the shaping phase of public-private initiatives, the work uncovered the effects of feedback relationships and interdependencies on PPP feasibility. This offered insights about the relationship between governance mechanisms and successful PPP development. In the third study, I utilized variables and relationships identified in my second investigation to develop a management flight simulator in order to better explain governance difficulties in the procurement phase of PPP projects. The simulator was implemented during an educational exercise with graduate students of civil engineering. By doing so, I confirmed that the simulator has the potential to increase our understanding of PPP procurement processes. Results indicated that the simulation tool was a suitable instrument to explain how government capacity, project uncertainty, and technical complexity influence PPP tendering. Overall, my findings across the three studies illustrate different means to understand why closing the global road infrastructure gap is challenging. Together, the three inquiries indicate that examining the road infrastructure sector as a socio-technical system contributes to improve our understanding of the expenditure dynamics related to existing assets and to enhance our comprehension of the governance challenges associated with developing new roads.
Comparing Self-Report Assessments and Scenario-Based Assessments of Systems Thinking Competence
Davis, Kirsten A.; Grote, Dustin; Mahmoudi, Hesam; Perry, Logan; Ghaffarzadegan, Navid; Grohs, Jacob; Hosseinichimeh, Niyousha; Knight, David B.; Triantis, Konstantinos (Springer, 2023-03)
Self-report assessments are used frequently in higher education to assess a variety of constructs, including attitudes, opinions, knowledge, and competence. Systems thinking is an example of one competence often measured using self-report assessments where individuals answer several questions about their perceptions of their own skills, habits, or daily decisions. In this study, we define systems thinking as the ability to see the world as a complex interconnected system where different parts can influence each other, and the interrelationships determine system outcomes. An alternative, less-common, assessment approach is to measure skills directly by providing a scenario about an unstructured problem and evaluating respondents' judgment or analysis of the scenario (scenario-based assessment). This study explored the relationships between engineering students' performance on self-report assessments and scenario-based assessments of systems thinking, finding that there were no significant relationships between the two assessment techniques. These results suggest that there may be limitations to using self-report assessments as a method to assess systems thinking and other competencies in educational research and evaluation, which could be addressed by incorporating alternative formats for assessing competence. Future work should explore these findings further and support the development of alternative assessment approaches.
A cyclical wildfire pattern as the outcome of a coupled human natural system
Farkhondehmaal, Farshad; Ghaffarzadegan, Navid (Nature Portfolio, 2022-03-28)
Over the past decades, wildfire has imposed a considerable cost on natural resources and human lives. In many regions, annual wildfire trends show puzzling oscillatory patterns with increasing amplitudes for burned areas over time. This paper aims to examine the potential causes of such patterns by developing and examining a dynamic simulation model that represents interconnected social and natural dynamics in a coupled system. We develop a generic dynamic model and, based on simulation results, postulate that the interconnection between human and natural subsystems is a source of the observed cyclical patterns in wildfires in which risk perception regulates activities that can result in more fire and development of vulnerable properties. Our simulation-based policy analysis points to a non-linear characteristic of the system, which rises due to the interconnections between the human side and the natural side of the system. This has a major policy implication: in contrast to studies that look for the most effective policy to contain wildfires, we show that a long-term solution is not a single action but is a combination of multiple actions that simultaneously target both human and natural sides of the system.
Dell's SupportAssist customer adoption model: enhancing the next generation of data-intensive support services
Ghaffarzadegan, Navid; Rad, Armin A.; Xu, Ran; Middlebrooks, Sam E.; Mostafavi, Sarah; Shepherd, Michael; Chambers, Landon; Boyum, Todd (2017-12)
We developed a decision support system to model, analyze, and improve market adoption of Dell's SupportAssist program. SupportAssist is a proactive and preventive support service capability that can monitor system operations data from all connected Dell devices around the world and predict impending failures in those devices. Performance of such data-intensive services is highly interconnected with market adoption: service performance depends on the richness of the customer database, which is influenced by customer adoption that in turn depends on customer satisfaction and service performancea reinforcing feedback loop. We developed the SupportAssist adoption model (SAAM). SAAM utilizes various data sources and modeling techniques, particularly system dynamics, to analyze market response under different strategies. Dell anticipates improving market adoption of SupportAssist and revenue from support services, as results of using this analytical tool. Copyright (c) 2018 The Authors System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society
Development and Evaluation of System Dynamics Education Modules for Complex Socioenvironmental Systems
Costello, Ryan Patrick (Virginia Tech, 2023-05-30)
Complex socioenvironmental problems such as food, energy and water shortages, health impacts from environmental contamination and global climate change present significant challenges to the global community. Addressing these problems will require an interdisciplinary systems-thinking approach that coordinates problem-solving between practitioners of varied disciplines including engineers, physical scientists, economists and other social scientists. Civil and environmental engineers have distinct technical skills necessary to help address these challenges as part of coordinated multidisciplinary efforts towards the achievement of comprehensive and sustainable resolutions to these problems. Ensuring civil and environmental engineers are trained to think and work in this multidisciplinary exchange requires incorporation of systems-thinking into engineering academic curricula. Attempts have been made to incorporate these skill sets into civil and environmental engineering (CEE) coursework. These efforts, as well as evaluation of their effectiveness in training CEE students to think systemically, have lacked in coordination to integrate them as part of the overarching academic curricula. This research advances the current body of knowledge regarding incorporation of systems-thinking into CEE coursework by examining the impacts of system dynamics model based educational tools on systems-thinking learning outcomes of CEE students in a one-semester CEE elective course. The findings suggest that system dynamics modeling can be an effective tool in educating future systems thinkers in the CEE disciplines.
Development of Novel Attention-Aware Deep Learning Models and Their Applications in Computer Vision and Dynamical System Calibration
Maftouni, Maede (Virginia Tech, 2023-07-12)
In recent years, deep learning has revolutionized computer vision and natural language processing tasks, but the black-box nature of these models poses significant challenges for their interpretability and reliability, especially in critical applications such as healthcare. To address this, attention-based methods have been proposed to enhance the focus and interpretability of deep learning models. In this dissertation, we investigate the effectiveness of attention mechanisms in improving prediction and modeling tasks across different domains. We propose three essays that utilize task-specific designed trainable attention modules in manufacturing, healthcare, and system identification applications. In essay 1, we introduce a novel computer vision tool that tracks the melt pool in X-ray images of laser powder bed fusion using attention modules. In essay 2, we present a mask-guided attention (MGA) classifier for COVID-19 classification on lung CT scan images. The MGA classifier incorporates lesion masks to improve both the accuracy and interpretability of the model, outperforming state-of-the-art models with limited training data. Finally, in essay 3, we propose a Transformer-based model, utilizing self-attention mechanisms, for parameter estimation in system dynamics models that outpaces the conventional system calibration methods. Overall, our results demonstrate the effectiveness of attention-based methods in improving deep learning model performance and reliability in diverse applications.
A Dynamic Model of Post-Traumatic Stress Disorder for Military Personnel and Veterans
Ghaffarzadegan, Navid; Ebrahimvandi, Alireza; Jalali, Mohammad S. (PLOS, 2016-10-07)
Post-traumatic stress disorder (PTSD) stands out as a major mental illness; however, little is known about effective policies for mitigating the problem. The importance and complexity of PTSD raise critical questions: What are the trends in the population of PTSD patients among military personnel and veterans in the postwar era? What policies can help mitigate PTSD? To address these questions, we developed a system dynamics simulation model of the population of military personnel and veterans affected by PTSD. The model includes both military personnel and veterans in a "system of systems." This is a novel aspect of our model, since many policies implemented at the military level will potentially influence (and may have side effects on) veterans and the Department of Veterans Affairs. The model is first validated by replicating the historical data on PTSD prevalence among military personnel and veterans from 2000 to 2014 (datasets from the Department of Defense, the Institute of Medicine, the Department of Veterans Affairs, and other sources). The model is then used for health policy analysis. Our results show that, in an optimistic scenario based on the status quo of deployment to intense/combat zones, estimated PTSD prevalence among veterans will be at least 10% during the next decade. The model postulates that during wars, resiliency-related policies are the most effective for decreasing PTSD. In a postwar period, current health policy interventions (e.g., screening and treatment) have marginal effects on mitigating the problem of PTSD, that is, the current screening and treatment policies must be revolutionized to have any noticeable effect. Furthermore, the simulation results show that it takes a long time, on the order of 40 years, to mitigate the psychiatric consequences of a war. Policy and financial implications of the findings are discussed.
Effect of mandating vaccination on COVID-19 cases in colleges and universities
Ghaffarzadegan, Navid (Elsevier, 2022-10-01)
Background: With the introduction of COVID-19 vaccines, many colleges and universities decided to mandate vaccination for all students and employees. The objective of this paper is to empirically investigate the effect of the mandate policy on Fall 2021 COVID-19 cases in institutions of higher education. Method: We construct a unique dataset of a sample of 94 colleges and universities in the east and southeast regions of the United States, 41 of which required vaccination prior to Fall 2021. A difference-in-differences analysis is conducted, considering vaccine requirement as a policy implemented only in a sub-group of these institutions. We control for several factors, including state-level case per capita and student population. Results: Our analysis shows that mandatory vaccination substantially decreased cases in institutions of higher education by 1,473 cases per 100,000 student population (95 CI: 132, 2813). Conclusions: The results suggest that a COVID-19 vaccine requirement is an effective policy in decreasing cases in such institutions, leading to a safer educational experience.
Effects of Government Spending on Research Workforce Development: Evidence from Biomedical Postdoctoral Researchers
Hur, Hyungjo; Ghaffarzadegan, Navid; Hawley, Joshua D. (PLOS, 2015-05-01)
We examine effects of government spending on postdoctoral researchers’ (postdocs) productivity in biomedical sciences, the largest population of postdocs in the US. We analyze changes in the productivity of postdocs before and after the US government’s 1997 decision to increase NIH funding. In the first round of analysis, we find that more government spending has resulted in longer postdoc careers. We see no significant changes in researchers’ productivity in terms of publication and conference presentations. However, when the population is segmented by citizenship, we find that the effects are heterogeneous; US citizens stay longer in postdoc positions with no change in publications and, in contrast, international permanent residents (green card holders) produce more conference papers and publications without significant changes in postdoc duration. Possible explanations and policy implications of the analysis are discussed.
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).
Enhancing Safety in Critical Monitoring Systems: Investigating the Roles of Human Error, Fatigue, and Organizational Learning in Socio-Technical Environments
Liu, Ning-Yuan (Virginia Tech, 2024-04-09)
Modern complex safety-critical socio-technical systems (STSs) operate in an environment that requires high levels of human-machine interaction. Given the potential for catastrophic events , understanding human errors is a critical research area spanning disciplines such as management science, cognitive engineering, resilience engineering, and systems theory. However, a research gap remains when researching how errors impact system performance from a systemic perspective. This dissertation employs a systematic methodology and develops models that explore the relationship between errors and system performance, considering both macro-organizational and micro-worker perspectives. In Essay 1, the focus is on how firms respond to serious errors (catastrophic events), by exploring the oscillation behavior associated with the organizational learning and forgetting theory. The proposed simulation model contributes to the organizational science literature with a comprehensive approach that assesses the firm's response time to "serious" errors when the firm has a focus on safety with established safety thresholds. All of these considerations have subsequent impact on future performance. Essay 2 explores the relationship between safety-critical system's workers' workload, human error, and automation reliance for the Belgian railway traffic control center. Key findings include a positive relationship between traffic controller performance and workload, and an inverted U-shaped relationship with automation usage. This research offers new insights into the effects of cognitive workload and automation reliance in safety-critical STSs. Essay 3 introduces a calibrated System Dynamics model, informed by empirical data and existing theories on workload suboptimality. This essay contributes to the managerial understanding of workload management, particularly the feedback mechanism between operators' workload and human errors, which is driven by overload and underload thresholds. The model serves as a practical tool for managerial practitioners to estimate the likelihood of human errors based on workload distributions. Overall, this dissertation presents an interdisciplinary and pragmatic approach, blending theoretical and empirical methodologies. Its broad impacts extend across management science, cognitive engineering, and resilience engineering, contributing significantly to the understanding and management of safety-critical socio-technical systems.
Essays on Innovation and Dynamic Capabilities: Evidence from Public Sector Operations and Cybersecurity
Miller, Marcus Soren (Virginia Tech, 2024-08-16)
The public sector needs the capacity for continual improvement and innovation. Cybersecurity threats against U.S. federal civilian agencies and national critical infrastructure stand out as a major problem area requiring agile and timely responses. Moreover, curbing ransomware attacks directed towards uniquely vulnerable domains, such as healthcare, education, and local government poses a particularly vexing policy challenge for government leaders. In three discrete essays, this dissertation examines management theories applied to the public sector and cybersecurity. The first two essays investigate a public management approach for improvement and innovation based on dynamic capabilities - that is, the organizational capacity to observe, understand, learn, and react in a transformational manner. The first essay of this dissertation presents a systematic literature review of empirical research on dynamic capabilities in the public sector which indicates clear benefits from the employment of dynamic capabilities through impacts on organizational capabilities, innovation, organizational change, operational performance, and public value. Building upon that literature review, the second essay of this dissertation applies archival data research and first-person interviews to examine the pivotal role played by dynamic capabilities in facilitating the generation and deployment of innovative cybersecurity approaches among the federal civilian agencies. This novel research identified and categorized dynamic capabilities in action and assessed their operational influence, specifically inter- and intra-agency collaboration, strategic planning, governance, and signature processes. The third essay of this dissertation was the first-ever documented system dynamics model of the ransomware ecosystem to understand incident trend patterns and provide insight into policy decisions. Simulation showed improvement by mandating incident reporting, reducing reporting delays, and strengthening passive defenses, but unexpectedly not by capping ransom payments.
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.
Essays on Risk Indicators and Assessment: Theoretical, Empirical, and Engineering Approaches
Azadeh Fard, Nasibeh (Virginia Tech, 2016-01-15)
Risk indicators are metrics that are widely used in risk management to indicate how risky an activity is. Among different types of risk indicators, early warning systems are designed to help decision makers predict and be prepared for catastrophic events. Especially, in complex systems where outcomes are often difficult to predict, early warnings can help decision makers manage possible risks and take a proactive approach. Early prediction of catastrophic events and outcomes are at the heart of risk management, and help decision makers take appropriate actions in order to mitigate possible effects of such events. For example, physicians would like to prevent any adverse events for their patients and like to use all pieces of information that help accurate early diagnosis and interventions. In this research, first we study risk assessment for occupational injuries using accident severity grade as an early warning indicator. We develop a new severity scoring system which considers multiple injury severity factors, and can be used as a part of a novel three-dimensional risk assessment matrix which includes an incident's severity, frequency, and preventability. Then we study the predictability of health outcome based on early risk indicators. A systems model of patient health outcomes and hospital length of stay is presented based on initial health risk and physician assessment of risk. The model elaborates on the interdependent effects of hospital service and a physician's subjective risk assessment on length of stay and mortality. Finally, we extend our research to study the predictive power of early warning systems and prognostic risk indicators in predicting different outcomes in health such as mortality, disease diagnosis, adverse outcomes, care intensity, and survival. This study provides a theoretical framework on why risk indicators can or cannot predict healthcare outcomes, and how better predictors can be designed. Overall, these three essays shed light on complexities of risk assessments, especially in health domain, and in the contexts where individuals continuously observe and react to the risk indicators. Furthermore, our multi-method research approach provides new insights into improving the design and use of the risk measures.
Essays on Utilizing Data Analytics and Dynamic Modeling to Inform Complex Science and Innovation Policies
Baghaei Lakeh, Arash (Virginia Tech, 2018-04-27)
In many ways, science represents a complex system which involves technical, social, and economic aspects. An analysis of such a system requires employing and combining different methodological perspectives and incorporation of different sources of data. In this dissertation, we use a variety of methods to analyze large sets of data in order to examine the effects of various domestic and institutional factors on scientific activities. First, we evaluate how the contributions of behavioral and social sciences to studies of health have evolved over time. We use data analytics to conduct a textual analysis of more than 200,000 publications on the topic of HIV/AIDS. We find that the focus of the scientific community within the context of the same problem varies as the societal context of the problem changes. Specifically, we uncover that the focus on the behavioral and social aspects of HIV/AIDS has increased over time and varies in different countries. Further, we show that this variation is related to the mortality level that the disease causes in each country. Second, we investigate how different sources of funding affect the science enterprise differently. We use data analytics to analyze more than 60,000 papers published on the subject of specific diseases globally and highlight the role of philanthropic money in these domains. We find that philanthropies tend to have a more practical approach in health studies as compared with public funders. We further show that they are also concerned with the economic, policy related, social, and behavioral aspects of the diseases. We uncover that philanthropies tend to mix and combine approaches and contents supported both by public and private sources of funding for science. We further show that in doing so, philanthropies tend to be closer to the position held by the public sector in the context of health studies. Finally, we find that studies funded by philanthropies tend to receive higher citations, and hence have higher impact, in comparison to those funded by the public sector. Third, we study the effect of different schemes of funding distribution on the career of scientists. In this study, we develop a system dynamics model for analyzing a scientist's career under different funding and competition contexts. We investigate the characteristics of optimal strategies and also the equilibrium points for the cases of scientists competing for financial resources. We show that a policy to fund the best can lead scientists to spend more time on writing proposals, in order to secure funding, rather than writing papers. We find that when everyone receives funding (or have the same chance of receiving funding) the overall optimal payoff of the scientists reaches its highest level and at this optimum, scientists spend all their time on writing papers rather than writing proposals. Our analysis suggests that more egalitarian distributions of funding results in higher overall research output by scientists. We also find that luck plays an important role in the success of scientists. We show that following the optimal strategies do not guarantee success. Due to the stochastic nature of funding decisions, some will eventually fail. The failure is not due to scientists' faulty decisions, but rather simply due to their lack of luck.
Generative Agent-Based Modeling: Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence
Ghaffarzadegan, Navid; Majumdar, Aritra; Williams, Ross; Hosseinichimeh, Niyousha (2024-01)
We discuss the emerging new opportunity for building feedback-rich computational models of social systems using generative artificial intelligence. Referred to as Generative Agent-Based Models (GABMs), such individual-level models utilize large language models to represent human decision-making in social settings. We provide a GABM case in which human behavior can be incorporated in simulation models by coupling a mechanistic model of human interactions with a pre-trained large language model. This is achieved by introducing a simple GABM of social norm diffusion in an organization. For educational purposes, the model is intentionally kept simple. We examine a wide range of scenarios and the sensitivity of the results to several changes in the prompt. We hope the article and the model serve as a guide for building useful dynamic models of various social systems that include realistic human reasoning and decision-making.
Global Trends and Regional Variations in Studies of HIV/AIDS
Lakeh, Arash Baghaei; Ghaffarzadegan, Navid (Springer Nature, 2017-06-23)
We conduct textual analysis of a sample of more than 200,000 papers written on HIV/AIDS during the past three decades. Using the Latent Dirichlet Allocation method, we disentangle studies that address behavioral and social aspects from other studies and measure the trends of different topics as related to HIV/AIDS. We show that there is a regional variation in scientists' approach to the problem of HIV/AIDS. Our results show that controlling for the economy, proximity to the HIV/AIDS problem correlates with the extent to which scientists look at the behavioral and social aspects of the disease rather than biomedical.

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