UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
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Item PART OF THE WHOLE: FACULTY CAMPUS SERVICE DECISIONS AT A CATHOLIC LIBERAL ARTS UNIVERSITY(2021) Kilmer, Sarah Jane; O'Meara, KerryAnn; Education Policy, and Leadership; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Faculty members in higher education institutions are typically evaluated based on their labor in research, teaching, and service. Although varying by institution type, research and teaching are often rewarded more heavily in tenure and promotion decisions than service work. Research indicates that women faculty perform more campus service work than do men faculty within academia, with important consequences for career advancement. One explanation for women’s greater participation in service is that colleges and universities are gendered institutions, with structures that preserve and promote differential expectations for men and women faculty. Within these gendered organizations, cultural and gender stereotypes shape the choices and behaviors of men and women faculty in important ways, including what they sign up for, what they are asked to do, and how they evaluate themselves and others. This study explored the impact of cultural and gender stereotyping as well as institutional context on the decisions that men and women faculty make regarding campus service at one Catholic liberal arts institution in the United States. Distinctive contextual factors such as mission and institutional culture can also influence decision-making around campus service in different ways, and this study examined how environmental factors, alongside the structural nature of gender norms, affected the campus service-related decisions of faculty in this particular case. Guided by the Stereotype Content Model (SCM), Social Role Theory (SRT), and March’s (1994) Decision-Making Theory, the findings from interviews with 21 faculty participants and analysis of over 50 institutional documents and key campus artifacts indicated that participants were aware of gender stereotypes and social roles and sometimes influenced by them. However, the distinctive institutional context had an important effect on participants’ campus service decisions. Participants made campus service decisions inside a women-founded institution where campus service was key to the institutional mission and campus service work was fully integrated into the faculty role, while also emphasized, expected, and rewarded by the institution. Data from the study suggests that participant reasons for engaging in campus service included a desire to understand the university better, make positive contributions to the common good, build meaningful relationships, and perform campus service work that was consistent with their values and strengths. As such, faculty members frequently employed rational choice motives but were also influenced to an important degree by the institution’s mission to engage in rule following decision-making. There are implications for faculty hiring and fit and tenure and promotion guidelines at institutions with distinctive foundations, as well as implications for campus service equity for faculty members and for addressing gendered and racialized norms within Catholic higher education.Item EVOLUTIONARY GAME THEORETIC MODELING OF DECISION MAKING AND CULTURE(2012) Roos, Patrick; Nau, Dana S; Gelfand, Michele J; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Evolutionary Game Theory (EGT) has become an attractive framework for modeling human behavior because it provides tools to explicitly model the dynamics of behaviors in populations over time and does not require the strong rationality assumptions of classical game theory. Since the application of EGT to human behavior is still relatively new, many questions about human behavior and culture of interest to social scientists have yet to be examined through an EGT perspective to determine whether explanatory and predictive rather than merely descriptive insights can be gained. In this thesis, informed by social science data and under close collaboration with social scientists, I use EGT-based approaches to model and gain a qualitative understanding of various aspects of the evolution of human decision-making and culture. The specific phenomena I explore are i) risk preferences and their implications on the evolution of cooperation and ii) the relationship between societal threat and the propensity with which agents of societies punish norm-violating behavior. First, inspired by much empirical research that shows human risk-preferences to be state-dependent rather than expected-value-maximizing, I propose a simple sequential lottery game framework to study the evolution of human risk preferences. Using this game model in conjunction with known population dynamics provides the novel insight that for a large range of population dynamics, the interplay between risk-taking and sequentiality of choices allows state-dependent risk behavior to have an evolutionary advantage over expected-value maximization.I then demonstrate how this principle can facilitate the evolution of cooperation in classic game-theoretic games where cooperation entails risk. Next, inspired by striking differences across cultural groups in their willingness to punish norm violators, I develop evolutionary game models based on the Public Goods Game to study punishment behavior. Operationalizing various forms of societal threat and determining the relationship between these threats and evolved punishment propensities, these models show how cross-cultural differences in punishment behavior are at least partially determined by cultures' exposure to societal threats, providing support for social science theories hypothesizing that higher threat is a causal factor for higher punishment propensities. This work advances the state of the art of EGT and its applications to the social sciences by i) creating novel EGT models to study different phenomena of interest in human decision-making and culture, and ii) using these models to provide insights about the relationships between variables in these models and their impact on evolutionary outcomes. By developing and analyzing these models under close consideration of relevant social science data, this work not only advances our understanding of how to use evolutionary game and multi-agent system models to study social phenomena, but also lays the foundation for more complex explanatory and predictive tools applicable to behaviors in human populations.Item A RISK-INFORMED DECISION-MAKING METHODOLOGY TO IMPROVE LIQUID ROCKET ENGINE PROGRAM TRADEOFFS(2013) Strunz, Richard; Herrmann, Jeffrey W.; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This work provides a risk-informed decision-making methodology to improve liquid rocket engine program tradeoffs with the conflicting areas of concern affordability, reliability, and initial operational capability (IOC) by taking into account psychological and economic theories in combination with reliability engineering. Technical program risks are associated with the number of predicted failures of the test-analyze-and-fix (TAAF) cycle that is based on the maturity of the engine components. Financial and schedule program risks are associated with the epistemic uncertainty of the models that determine the measures of effectiveness in the three areas of concern. The affordability and IOC models' inputs reflect non-technical and technical factors such as team experience, design scope, technology readiness level, and manufacturing readiness level. The reliability model introduces the Reliability- As-an-Independent-Variable (RAIV) strategy that aggregates fictitious or actual hotfire tests of testing profiles that differ from the actual mission profile to estimate the system reliability. The main RAIV strategy inputs are the physical or functional architecture of the system, the principal test plan strategy, a stated reliability-bycredibility requirement, and the failure mechanisms that define the reliable life of the system components. The results of the RAIV strategy, which are the number of hardware sets and number of hot-fire tests, are used as inputs to the affordability and the IOC models. Satisficing within each tradeoff is attained by maximizing the weighted sum of the normalized areas of concern subject to constraints that are based on the decision-maker's targets and uncertainty about the affordability, reliability, and IOC using genetic algorithms. In the planning stage of an engine program, the decision variables of the genetic algorithm correspond to fictitious hot-fire tests that include TAAF cycle failures. In the program execution stage, the RAIV strategy is used as reliability growth planning, tracking, and projection model. The main contributions of this work are the development of a comprehensible and consistent risk-informed tradeoff framework, the RAIV strategy that links affordability and reliability, a strategy to define an industry or government standard or guideline for liquid rocket engine hot-fire test plans, and an alternative to the U.S. Crow/AMSAA reliability growth model applying the RAIV strategy.Item A Predictive Model of Nuclear Power Plant Crew Decision-Making and Performance in a Dynamic Simulation Environment(2009) Coyne, Kevin; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The safe operation of complex systems such as nuclear power plants requires close coordination between the human operators and plant systems. In order to maintain an adequate level of safety following an accident or other off-normal event, the operators often are called upon to perform complex tasks during dynamic situations with incomplete information. The safety of such complex systems can be greatly improved if the conditions that could lead operators to make poor decisions and commit erroneous actions during these situations can be predicted and mitigated. The primary goal of this research project was the development and validation of a cognitive model capable of simulating nuclear plant operator decision-making during accident conditions. Dynamic probabilistic risk assessment methods can improve the prediction of human error events by providing rich contextual information and an explicit consideration of feedback arising from man-machine interactions. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) shows promise for predicting situational contexts that might lead to human error events, particularly knowledge driven errors of commission. ADS-IDAC generates a discrete dynamic event tree (DDET) by applying simple branching rules that reflect variations in crew responses to plant events and system status changes. Branches can be generated to simulate slow or fast procedure execution speed, skipping of procedure steps, reliance on memorized information, activation of mental beliefs, variations in control inputs, and equipment failures. Complex operator mental models of plant behavior that guide crew actions can be represented within the ADS-IDAC mental belief framework and used to identify situational contexts that may lead to human error events. This research increased the capabilities of ADS-IDAC in several key areas. The ADS-IDAC computer code was improved to support additional branching events and provide a better representation of the IDAC cognitive model. An operator decision-making engine capable of responding to dynamic changes in situational context was implemented. The IDAC human performance model was fully integrated with a detailed nuclear plant model in order to realistically simulate plant accident scenarios. Finally, the improved ADS-IDAC model was calibrated, validated, and updated using actual nuclear plant crew performance data. This research led to the following general conclusions: (1) A relatively small number of branching rules are capable of efficiently capturing a wide spectrum of crew-to-crew variabilities. (2) Compared to traditional static risk assessment methods, ADS-IDAC can provide a more realistic and integrated assessment of human error events by directly determining the effect of operator behaviors on plant thermal hydraulic parameters. (3) The ADS-IDAC approach provides an efficient framework for capturing actual operator performance data such as timing of operator actions, mental models, and decision-making activities.Item The Sensititivity of Five- to Ten-Year-Old Children to Value, Probability, and Loss(2005-07-21) Boyer, Ty W.; Scholnick, Ellin K.; Byrnes, James P.; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Classic and contemporary researchers have studied the child's abilities to discriminate quantitative values, understand probability, and appreciate risk and uncertainty. The current studies were designed to extend and methodologically integrate recent insights that have been made across these sub-areas. A computerized decision-making task, which allows manipulation of probability of success and quantitative outcome value, was developed. In the first study, this task was used to analyze the development of preference between options with systematically contrasted numerical outcome values. Contrary to recent research, this study revealed that participants, and particularly younger children (i.e., five- and six-year-olds), tend to neglect quantitative outcome value information, and seem to base choices primarily on probability information. In the second study, the task was used to assess the development of preference between options with systematically contrasted probabilities of success. Consistent with recent research, this study revealed that even young participants attend to differences in probability of success between decision alternatives; however, younger participants seemed less able to explicitly integrate decision outcomes, as assessed by more explicit measures of probability understanding. In the third study, probability of success was again manipulated, but wins were combined with losses. This study revealed, like Study 2, that children adjusted preference as a function of probability of success; however, consistent with Study 1, this study revealed that children tend to neglect outcome values. Cross-study analyses were conducted which further demonstrated that decision-making probabilities loom larger than outcome values. Collectively, these studies suggest that processing of probabilities developmentally precedes processing of quantitative outcome values, and that implicit processing developmentally precedes explicit decision integration. In the conclusion these findings and possible future directions are discussed.