Matching Items (9)
Description
Free/Libre Open Source Software (FLOSS) is the product of volunteers collaborating to build software in an open, public manner. The large number of FLOSS projects, combined with the data that is inherently archived with this online process, make studying this phenomenon attractive. Some FLOSS projects are very functional, well-known, and successful, such as Linux, the Apache Web Server, and Firefox. However, for every successful FLOSS project there are 100's of projects that are unsuccessful. These projects fail to attract sufficient interest from developers and users and become inactive or abandoned before useful functionality is achieved. The goal of this research is to better understand the open source development process and gain insight into why some FLOSS projects succeed while others fail. This dissertation presents an agent-based model of the FLOSS development process. The model is built around the concept that projects must manage to attract contributions from a limited pool of participants in order to progress. In the model developer and user agents select from a landscape of competing FLOSS projects based on perceived utility. Via the selections that are made and subsequent contributions, some projects are propelled to success while others remain stagnant and inactive. Findings from a diverse set of empirical studies of FLOSS projects are used to formulate the model, which is then calibrated on empirical data from multiple sources of public FLOSS data. The model is able to reproduce key characteristics observed in the FLOSS domain and is capable of making accurate predictions. The model is used to gain a better understanding of the FLOSS development process, including what it means for FLOSS projects to be successful and what conditions increase the probability of project success. It is shown that FLOSS is a producer-driven process, and project factors that are important for developers selecting projects are identified. In addition, it is shown that projects are sensitive to when core developers make contributions, and the exhibited bandwagon effects mean that some projects will be successful regardless of competing projects. Recommendations for improving software engineering in general based on the positive characteristics of FLOSS are also presented.
ContributorsRadtke, Nicholas Patrick (Author) / Collofello, James S. (Thesis advisor) / Janssen, Marco A (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Sundaram, Hari (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation uses a comparative approach to investigate long-term human- environment interrelationships in times of climate change. It uses Geographical Information Systems and ecological models to reconstruct the Magdalenian (~20,000- 14,000 calibrated years ago) environments of the coastal mountainous zone of Cantabria (Northwest Spain) and the interior valleys of the Dordogne (Southwest France) to contextualize the social networks that could have formed during a time of high climate and resource variability. It simulates the formation of such networks in an agent-based model, which documents the processes underlying the formation of archaeological assemblages, and evaluates the potential impacts of climate-topography interactions on cultural transmission. This research then reconstructs the Magdalenian social networks visible through a multivariate statistical analysis of stylistic similarities among portable art objects. As these networks cannot be analyzed directly to infer social behavior, their characteristics are compared to the results of the agent-based model, which provide characteristics estimates of the Magdalenian latent social networks that most likely produced the empirical archaeological assemblage studied.
This research contributes several new results, most of which point to the advantages of using an inter-disciplinary approach to the study of the archaeological record. It demonstrates the benefits of using an agent-based model to parse social data from long- term palimpsests. It shows that geographical and environmental contexts affect the structure of social networks, which in turn affects the transmission of ideas and goods that flow through it. This shows the presence of human-environment interactions that not only affected our ancestors’ reaction to resource insecurities, but also led them to innovate and improve the productivity of their own environment. However, it also suggests that such alterations may have reduced the populations’ resilience to strong climatic changes, and that the region with diverse resources provided a more stable and resilient environment than the region transformed to satisfy the immediate needs of its population.
This research contributes several new results, most of which point to the advantages of using an inter-disciplinary approach to the study of the archaeological record. It demonstrates the benefits of using an agent-based model to parse social data from long- term palimpsests. It shows that geographical and environmental contexts affect the structure of social networks, which in turn affects the transmission of ideas and goods that flow through it. This shows the presence of human-environment interactions that not only affected our ancestors’ reaction to resource insecurities, but also led them to innovate and improve the productivity of their own environment. However, it also suggests that such alterations may have reduced the populations’ resilience to strong climatic changes, and that the region with diverse resources provided a more stable and resilient environment than the region transformed to satisfy the immediate needs of its population.
ContributorsGravel-Miguel, Claudine (Author) / Barton, C. Michael (Thesis advisor) / Coudart, Anick (Committee member) / Clark, Geoffrey A. (Committee member) / Arizona State University (Publisher)
Created2017
Description
The mechanisms behind the emergence of collective behaviors arising from physics, biology, economics and many other related fields have drawn a lot of attention among the applied math community in the last few decades. Broadly speaking, collective behaviors in natural, life and social sciences are all modelled by interacting particle systems, in which a bulk of N particles are engaging in some simple binary pairwise interactions. In this dissertation, some prototypical interacting particle systems having applications in econophysics and statistical averaging dynamics are investigated. It is also emphasized that there is an increasing tendency among the applied math community to apply tools or concepts for studying many particle systems to the (rigorous) investigation of artificial (deep) neural networks.
ContributorsCao, Fei (Author) / Motsch, Sebastien S.M. (Thesis advisor) / Lanchier, Nicolas N.L. (Committee member) / Jones, Donald D.J. (Committee member) / Hahn, Paul P.H. (Committee member) / Fricks, John J.F. (Committee member) / Arizona State University (Publisher)
Created2022
Description
Global transitioning towards battery-based clean energy and green technologies has rapidly accelerated demands for Lithium (Li) as one of the most important minerals. However, the social and ecological implications of the anticipated growth in mineral extractions have not been acknowledged or adequately studied. Therefore, using the telecoupling framework, this dissertation aims to systematically understand the linkages between globally increasing adoption of green technologies and the social-ecological impacts in Li-extracted places, in order to help identify potential mechanisms or solutions to address such consequences. This dissertation selects the Salar de Atacama in Chile as the study area to firstly provide a socio-environmental assessment to synthesize the interdependent relationship between Li-mining companies and host communities. Then, an agent-based model was developed to demonstrate future social-ecological implications in the mining area for various mining projections. Lastly, the perceptions of end-users of green-tech products (e.g., electric cars) were collected and studied as to the awareness of embodied mining impacts and how these impacts should be addressed. Results found that Li-mining operations and local communities are closely linked at both local and regional scales through the shared resource space, economic opportunities, and resource governance. The excessive groundwater consumption from mining drives the most sustainability concerns. Material uncertainties of groundwater were found to play a vital role in causing the mismatched evolution of environmental and social dynamics, thereby highlighting some governance challenges stemming from resource uncertainties. Meanwhile, among sampled end-users, tensions and conflicts are widely found between the imperative of energy transitions and the reality of adversity mining impacts, along with a general lack of awareness. Fortunately, most respondents recognized the complexity in the supply chain of EVs and the role of consumers in influencing its governance. Overall, this dissertation provides a benchmark of the social-environmental impacts of Li-mining in Salar de Atacama, along with suggestions for decision-makers and mining managers on improved mineral governance. It also highlights the need and urgency for a telecoupling view in sustainable mineral governance and suggests a shift in green-tech supply chain to include broader sustainable development goals and a global community of consumers, affected communities, and the general public.
ContributorsLiu, Wenjuan (Author) / Agusdinata, Datu Buyung DA (Thesis advisor) / Romero, Hugo HR (Committee member) / Eakin, Hallie HE (Committee member) / Arizona State University (Publisher)
Created2021
Description
In response to the COVID-19 pandemic, countries took serious measures to control its spread and reduce its effect on health, social, and economic aspects. The United Arab Emirates (UAE) has taken unprecedented preventive measures against the spread of COVID-19, including complete lockdowns and the closing of some businesses. Therefore, 27% of companies expected to lose their businesses within a month, while 43% of companies expected to go out of business within six months. This was not only due to the countrywide lockdown, or the impacts caused by the pandemic, but also due to the bad leadership of some leaders during this crisis. There are little of studies and data that discuss the consequences of these decisions on businesses, and it will be helpful to measure the consequences over three years. This study answers the following question: How much did myopic staffing and compensation decisions in the context of COVID-19 affect companies’ performance? To answer this question, I use agent-based modeling (ABM) supported by secondary data to create a simulation to study the consequences of myopic decisions made on employees’ performance in the private sector in the United Arab Emirates starting from the 2020 year and through an anticipated period of 3 years . The study found that under the assumptions that pay deductions, layoffs, and unpaid leaves, are myopic decisions and in the context of the COVID-19 pandemic and its impact on the companies’ performance, there is a huge affect on companies’ performance over the study period which is 3 years.
Keywords: bad leadership, myopic decisions, companies, businesses, COVID-19, agent-based model.
ContributorsAlsaleh, Mohammad (Author) / Trinh, Mai P. (Thesis advisor) / Castillo, Elizabeth (Committee member) / Wallace, L. Marie (Committee member) / Arizona State University (Publisher)
Created2022
Description
Abortion is a controversial topic internationally. Most current debates about abortion concern when, if at all, it should be legal. However, researchers have shown many times that after an abortion ban, maternal and infant mortalities rise significantly, as women who seek out abortions do so regardless of abortion legality. So, is it possible to reduce abortions in a population without delegalizing abortion and, if so, how? Why do some countries have higher abortion rates than others in the presence of the same law?This dissertation answers both questions. First, I present historical evidence in the first comprehensive comparative analysis of all 15 post-Soviet countries, which have very similar abortion laws originating from the Union of Soviet Socialist Republics (USSR). Second, I use those findings to build the first agent-based model (ABM) of unintended pregnancies in a hypothetical artificial population.
USSR was the only country in the world to complete its demographic transition through abortion instead of modern contraception, and the Soviet government passed the first law in the world to allow abortion upon request in 1920. After the USSR dissolution in 1991, post-Soviet countries maintained very similar abortion laws, but had very different abortion rates for most years. Analysis of fertility data from post-Soviet countries shows that the prevalence of some specific contraceptive methods, namely the rhythm method (r = 0.82), oral pill (r = 0.56), and male condom (r = 0.51) are most strongly correlated with high abortion rates, and that sex education is a factor that reduces the rates in otherwise similar countries (p = 0.02).
The ABM shows that even basic sex education results in fewer abortions than no sex education or abstinence-based sex education (p < 0.01). In scenarios without sex education, basic quality of post-abortion contraceptive counseling (PACC) is better than no PACC or low-quality PACC at reducing abortions (p < 0.01). Still, the higher the quality of sex education or PACC, the fewer abortions in the artificial population. The ABM is adaptive and policy makers can use it as a decision-support tool to make evidence-based policy decisions regarding abortion, and, potentially, other sociobiological phenomena with some adjustments to the code.
ContributorsZiganshina Lienhard, Dina A. (Author) / Maienschein, Jane (Thesis advisor) / Gaughan, Monica (Thesis advisor) / Laubichler, Manfred (Committee member) / Ellison, Karin (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation consists of three essays, each examining distinct aspects about public organization adaptation to extreme events using evidence from public transit agencies under the influence of extreme weather in the United States (U.S.). The first essay focuses on predicting organizational adaptive behavior. Building on extant theories on adaptation and organizational learning, it develops a theoretical framework to uncover the pathways through which extreme events impact public organizations and identify the key learning mechanisms involved in adaptation. Using a structural equation model on data from a 2016 national survey, the study highlights the critical role of risk perception to translate signals from the external environment to organizational adaptive behavior.
The second essay expands on the first one to incorporate the organizational environment and model the adaptive system. Combining an agent-based model and qualitative interviews with key decision makers, the study investigates how adaptation occurs over time in multiplex contexts consisting of the natural hazards, organizations, institutions and social networks. The study ends with a series of refined propositions about the mechanisms involved in public organization adaptation. Specifically, the analysis suggests that risk perception needs to be examined relative to risk tolerance to determine organizational motivation to adapt, and underscore the criticality of coupling between the motivation and opportunities to enable adaptation. The results further show that the coupling can be enhanced through lowering organizational risk perception decay or synchronizing opportunities with extreme event occurrences to promote adaptation.
The third essay shifts the gaze from adaptation mechanisms to organizational outcomes. It uses a stochastic frontier analysis to quantify the impacts of extreme events on public organization performance and, importantly, the role of organizational adaptive capacity in moderating the impacts. The findings confirm that extreme events negatively affect organizational performance and that organizations with higher adaptive capacity are more able to mitigate those effects, thereby lending support to research efforts in the first two essays dedicated to identifying preconditions and mechanisms involved in the adaptation process. Taken together, this dissertation comprehensively advances understanding about public organization adaptation to extreme events.
The second essay expands on the first one to incorporate the organizational environment and model the adaptive system. Combining an agent-based model and qualitative interviews with key decision makers, the study investigates how adaptation occurs over time in multiplex contexts consisting of the natural hazards, organizations, institutions and social networks. The study ends with a series of refined propositions about the mechanisms involved in public organization adaptation. Specifically, the analysis suggests that risk perception needs to be examined relative to risk tolerance to determine organizational motivation to adapt, and underscore the criticality of coupling between the motivation and opportunities to enable adaptation. The results further show that the coupling can be enhanced through lowering organizational risk perception decay or synchronizing opportunities with extreme event occurrences to promote adaptation.
The third essay shifts the gaze from adaptation mechanisms to organizational outcomes. It uses a stochastic frontier analysis to quantify the impacts of extreme events on public organization performance and, importantly, the role of organizational adaptive capacity in moderating the impacts. The findings confirm that extreme events negatively affect organizational performance and that organizations with higher adaptive capacity are more able to mitigate those effects, thereby lending support to research efforts in the first two essays dedicated to identifying preconditions and mechanisms involved in the adaptation process. Taken together, this dissertation comprehensively advances understanding about public organization adaptation to extreme events.
ContributorsZhang, Fengxiu (Author) / Welch, Eric (Thesis advisor) / Barton, Michael (Committee member) / Bretschneider, Stuart (Committee member) / Feeney, Mary K. (Committee member) / Maroulis, Spiro (Committee member) / Arizona State University (Publisher)
Created2020
Description
Social insect groups, such as bees, termites, and ants, epitomize the emergence of group-level behaviors from the aggregated actions and interactions of individuals. Ants have the unique advantage that whole colonies can be observed in artificial, laboratory nests, and each individual's behavior can be continuously tracked using imaging software. In this dissertation, I study two group behaviors: (1) the spread of alarm signals from three agitated ants to a group of 61 quiescent nestmates, and (2) the reduction in per-capita energy use as colonies scale in size from tens of ants to thousands. For my first experiment, I track the motion of Pogonomyrmex californicus ants using an overhead camera, and I analyze how propagation of an initial alarm stimulus affects their walking speeds. I then build an agent-based model that simulates two-dimensional ant motion and the spread of the alarmed state. I find that implementing a simple set of rules for motion and alarm signal transmission reproduces the empirically observed speed dynamics. For the second experiment, I simulate social insect colony workers that collectively complete a set of tasks. By assuming that task switching is energetically costly, my model recovers a metabolic rate scaling pattern, known as hypometric metabolic scaling. This relationship, which predicts an organism's metabolic rate from its mass, is observed across a diverse set of social insect groups and animal species. The results suggest an explicit link between the degree of workers' task specialization and whole-colony energy use.
ContributorsLin, Michael Robert (Author) / Milner, Fabio A (Thesis advisor, Committee member) / Fewell, Jennifer H (Thesis advisor, Committee member) / Lampert, Adam (Committee member) / Arizona State University (Publisher)
Created2021
Description
This research focuses on the intricate dynamical systems of eusocial insects, particularly ants, and honey bees, known for their highly organized colonies and cooperative behaviors. Research on eusocial insects contributes to understanding of animal and social behavior and promises to help agriculture and have huge economic impacts. Collaborating closely with ecologists, I construct diverse mathematical models tailored to different environmental contexts. These models encompass individual stochastic (Agent-based model), Ordinary Differential Equation (ODE), non-autonomous, and Delay Differential Equation (DDE) models, rigorously validated with experimental data and statistical methods. Employing dynamical theory, bifurcation analysis, and numerical simulations, I gain deeper insights into the adaptive behaviors exhibited by these insects at both colony and individual levels. Our investigation addresses pivotal questions: 1) What mechanisms underlie spatial heterogeneity within social insect colonies, influencing the spread of information and pathogens through their intricate social networks?2) How can I develop accurate mathematical models incorporating age structures, particularly for species like honeybees, utilizing delayed differential equations?
3) What is the influence of seasonality on honeybee population dynamics in the presence of parasites, as explored through non-autonomous equations?
4) How do pesticides impact honeybee population dynamics, considering delayed equations and seasonality? Key findings highlight:1) The spatial distribution within colonies significantly shapes contact dynamics, thereby influencing the dissemination of information and the allocation of tasks.
2) Accurate modeling of honeybee populations necessitates the incorporation of age structure, as well as careful consideration of seasonal variations.
3) Seasonal fluctuations in egg-laying rates exert varying effects on the survival of honeybee colonies.
4) Pesticides wield a substantial influence on adult bee mortality rates and the consumption ratios of pollen. This research not only unveils the intricate interplay between intrinsic and environmental factors affecting social insects but also provides broader insights into social behavior and the potential ramifications of climate change.
ContributorsChen, Jun (Author) / Kang, Yun (Thesis advisor) / DeGrandi-Hoffman, Gloria (Committee member) / Fewell, Jeniffer (Committee member) / Harrison, Jon (Committee member) / Towers, Sherry (Committee member) / Arizona State University (Publisher)
Created2023