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Essays on Belief Updating and Decision-Making
No full textMy dissertation consists of three chapters on belief updating and decision-making.
The first chapter, coauthored with Marco Castillo, aims to understand how to mitigate negative economic consequences that can arise when individuals have biased beliefs about their surroundings. Specifically, we design two task assignment rules in teams based on a theoretical framework of Heidhues et al. (2018), and investigate their causal impact on task allocative efficiency. Using a series of laboratory experiments, we find that equally biased beliefs about team members��� productivities can lead to different efficiency outcomes depending on task assignment rules. The study highlights the importance of institutional designs that ensure economically desirable outcomes even in the presence of biased beliefs.
The second chapter, coauthored with Andy Cao, Marco Castillo, and Ragan Petrie, examines how non-pecuniary costs incurred during college impact college major choices. We conduct a randomized controlled trial to provide truthful information on major-specific teaching quality and inclusive climate to college freshmen and sophomores at Texas A&M University. We find significant effects of teaching and climate information, especially for choosing among business and economics majors. We also find that information has affected the college major choices of women more than it has for men. Our study shows that non-pecuniary aspects of human capital accumulation experienced during college can have an impact on the allocation of talent across fields.
The third chapter, co-authored with Hyundam Je, presents novel evidence that the timing of informativeness affects the level of motivated reasoning. Building upon the primacy effect, our framework predicts that receiving a signal before learning about its informativeness can exacerbate the extent of motivated reasoning compared to receiving it afterward. Our online experiment finds supporting evidence for this prediction. When participants learn about informativeness after receiving a signal, there is a stronger tendency for them to interpret the signal in a way that reinforces their preferred beliefs. These findings suggest the practical importance of structuring information as a strategy to mitigate motivated reasoning in information transmission
Ira Greenbaum field notebook: GK5501-GK6000.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for GK5501-GK6000 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection
Shame as an Affective Component of Pain Experiences
No full textThe experience of pain is a warning that potential harm has come to the body. Furthermore, pain is recognized as a biopsychosocial phenomenon, with a person���s traits, emotions, social interactions, and environments all encompassing pain outcomes. As such, identifying specific psychological components of the experience of pain may help further guide our understanding of the manifestation and persistence of pain. One potential aspect of pain may be the self-conscious emotion of shame, a feeling arising from the judgment of others acting as a warning that something about one���s self is socially unacceptable. Indeed, people living with chronic pain frequently express feeling shame because of how pain impacts their lives; however it has not been assessed in relation to the exacerbation of pain itself. Therefore, this dissertation employed four studies examining the relationship between shame and pain, specifically hypothesizing that greater feelings of shame would be associated with greater pain experiences. Studies 1a and 1b were correlational online studies, finding that shame was positively associated with clinically relevant self-report of pain. Study 2 found a similar relationship with self-reported measures of central sensitization, but not laboratory-based sensory measures central sensitization as assessed by mechanical temporal summation. Study 3 explored the relationship between shame and endogenous opioid response measured using conditioned pain modulation, and also did not find support for the overarching hypothesis. Study 4 utilized a daily diary design to observe actual lived experiences of shame and pain, and found that people experienced greater pain severity and interference on days they felt more shame. Although results were mixed across studies they provide a foundation for future understanding of the relationship between shame and pain outcomes with implications for clinical treatment and care
Unfolding the Complexity of Soil Chemical Process and Remote Sensing for the Detection and Monitoring of Oil Contaminants Using AI Techniques
No full textThe rapid acceleration of global economic development has significantly increased energy demands, leading to severe environmental consequences, particularly soil contamination due to oil pollutants. This contamination not only alters soil's physical and chemical properties but also jeopardizes its ecological balance and human health. In response to these challenges, our research embarks on a comprehensive exploration of soil contamination by oil pollutants, emphasizing the need for a deep understanding of these contaminants within their ecosystems. We investigate the effectiveness of various remediation strategies, considering the intricate dynamics of soil ecosystems. Our study aims to contribute significantly to environmental science by identifying pollutants and deploying tailored remediation techniques that harmonize with soil ecosystem complexities.
First, our research utilizes an AI-assisted systematic review to understand the remediation of soils contaminated with Polycyclic Aromatic Hydrocarbons (PAHs) and heavy metals. By employing literature databases, text mining, and interactive data mining tools, we aim to offer a holistic view of soil contamination. Results indicate a prevalence of combined treatment techniques, with biological-biological approaches being most common, highlighting the challenges and potential strategies for effective remediation.
Secondly, our efforts are directed toward transforming soil remediation techniques with the introduction of Advanced Fenton-Photo Systems, complemented by the integration of deep-learning neural networks aimed at refining petrochemical degradation processes. The empirical evidence from our research indicates remarkable oxidation rates of Total Petroleum Hydrocarbons (TPHs) and Polycyclic Aromatic Hydrocarbons (PAHs), with degradation rates reaching up to 99% in mere minutes. This highlights a substantial leap forward in the efficiency of removing contaminants from soil.
In our third objective, we harness Artificial Intelligence (AI) to enhance the capabilities of remote sensing in accurately predicting oil contamination within the Al-Burgan oil field. Our findings, derived from the application of advanced neural network models and Sentinel-2 satellite data, have significantly improved oil contamination detection, achieving flawless accuracy in certain scenarios. This approach not only refines the detection and quantification of oil contamination but also showcases the transformative potential of AI in elevating environmental surveillance and monitoring practices.
Lastly, the research is aimed at advancing the monitoring and prediction of vegetation coverage in arid ecosystems, with a specific focus on Kuwait's Burgan field, through the application of AI-integrated remote sensing. Our analysis has unveiled notable vegetation recovery following remediation efforts, highlighted by interannual and seasonal changes in vegetation cover. The utilization of Soil-Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI), processed through neural networks, has provided deep insights into vegetative dynamics. This underscores the effectiveness of remote sensing in ecological assessments, driven by the development of innovative vegetation indices and the employment of advanced remote sensing techniques. These efforts are pivotal in offering profound insights into the health and dynamics of vegetation, underlining the critical role of ecological monitoring and management.
This research will result in improved strategies for environmental management and remediation, offering novel insights and methodologies that facilitate the sustainable management of contaminated soils. Through a multifaceted approach that integrates advanced technological solutions and ecological understanding, our study contributes to the advancement of environmental restoration efforts, ensuring healthier ecosystems for future generations
Essays on the Causal Effects of Retailer Strategic Actions on Sales, Omnichannel Shopping, and Mobile App Engagement
No full textRetailers engage in strategic actions such as store closure and the introduction of new features in mobile apps. Each action has the potential to change shoppers��� omnichannel shopping behavior and engagement. Empirical analysis of the causal effects of these strategic actions is challenging because field experiments are often expensive and infeasible. In the two essays of my dissertation, I focus on the strategic actions of a large U.S. retailer of video games and consumer electronics and analyze the causal effects of these actions. I use the difference-in-differences (DID) framework, controlling for potential endogeneity, to causally estimate the effects of the strategic actions. I apply machine learning algorithms to explore treatment effect heterogeneity and analyze unstructured app clickstream data.
In Essay 1, I study the impact of store closure on the retail chain���s aggregate sales, customers��� omnichannel shopping, and mobile app usage. The results show that store closures led to a significant loss of $209,317 in net monthly sales per county, surpassing the average sales of the closed stores. Both offline and online sales dropped after the closure of a store. These results highlight that retailers should re-examine their closure plans and account for the negative spillover effect.
Essay 2 examines the causal effect of in-app payment introduction on omnichannel shopping and mobile app usage behavior, uncovers individual-level treatment effect heterogeneity, and investigates the underlying mechanisms. I find that adopting in-app payment significantly boosts overall purchases. As a result, overall spending net of returns is 25.3% (33.6%) higher for Apple Pay (PayPal) adopters than nonadopters. Mechanisms driving these results include increased spending both offline and online, reduced friction, lower mobile cart abandonment rate, increased Buy-Online-and-Pickup-In-Store (BOPIS) orders, heightened app usage near stores, greater store visits due to increased product returns and trade-ins, and more impulse purchases
Design, Development, and Characterization of a 3D Solar Heat Exchanger
No full textThis thesis presents a multifaceted approach to the design, characterization, and development of a 3D Solar heat exchanger, employing state-of-the-art Computational Fluid Dynamics (CFD) and experimental techniques. The Solar Model represents a compact and innovative multi-level heat exchanger, incorporating intricate design elements such as pin-fins, vanes, multiple fluid passages, or channels. The primary objective of this design is to bolster heat transfer efficiency by prolonging the contact duration between the fluid and the heat transfer surfaces. To substantiate the theoretical models and designs, a comprehensive CFD and experimental study of the 3D model was conducted. CFD has proven to be an effective tool in the design and optimization of heat exchangers by considering thermal properties and it has been employed to study different modifications, compare results, and present the best possible combination of variables to ensure optimum performance. The proposed experimental procedure was implemented, with a step-by-step guide for system setup, including the installation of a solar collector, heat exchanger, and fluid circulation system. Measurement devices, comprising of thermocouples and pressure transducers, were placed throughout the system to monitor temperatures, pressure drop, and heat transfer rates under varying experimental conditions.
This comprehensive research endeavor not only explores the theoretical aspects of solar design and optimization using CFD but also validates these models through practical experimentation. The findings emphasize the paramount role of design configurations and parameters, particularly the aspect ratios and how they influence the overall thermal performance of solar systems. This combined approach paves the way for the advancement of efficient solar thermal systems, contributing to sustainable and eco-friendly energy solutions.
Steady state CFD simulations were conducted to study the fluid flow patterns, velocity profiles and temperature distribution of the solar at different aspect ratios. The modified geometry with an aspect ratio of 0.5 led to a more homogenous temperature distribution within the computational domain characterized by well-distributed fluid flow patterns. The modified geometry was then selected for fabrication so a prototype could be characterized experimentally. Experimental results revealed that the overall heat transfer coefficient, U, increased with flow rate. Furthermore, U reached an optimum value between 2.8 and 3.0 l/min, suggesting that the flow behavior inside the solar heat exchanger reached an optimum condition despite depicting higher pressure drop at higher flow rates. In summary, designing, numerically simulating, and experimentally characterizing a solar heat exchanger with features such as long fins and guide vanes proved to be a successful heat exchanger development approach. Such an approach should help the development of heat exchangers for renewable energy applications
Systematic Uncertainty Quantification of MCNP Predicted Nuclide Concentrations in Fuel Burnup Simulations
No full textMonte Carlo N-Particle transport code (MCNP) is often used to simulate nuclear fuel burnup and depletion because it is efficient in solving the radiation transport equation for complex geometries. MCNP simulates fuel burnup and estimates the concentrations of actinides and fission products generated in the fuel, which are useful in nuclear forensics as well as safeguards monitoring. During fuel burnup simulations, the uncertainties in the predicted nuclide concentrations due to the uncertainty in the nuclear data used by MCNP are not propagated and predicted. The nuclide concentration is calculated through CINDER 90 isotope generation and depletion module in MCNP. The CINDER90 module uses the neutron reaction rates and flux values computed by MCNP for each burnup time step. The reaction rates can be broken down into three terms: neutron flux, number density of the target isotope that is transmuting, and microscopic neutron interaction cross section. The number density and neutron flux are provided by MCNP; however, the microscopic cross sections are not directly provided by MCNP in the output and will contain systematic uncertainty in varying degrees depending on the microscopic cross section of the target isotope of interest. Systematic uncertainty is not propagated through each MCNP burnup time step. Propagating the effects of systematic uncertainty using a Backward Euler numerical scheme allows for the reporting of the systematic relative error in the predicted nuclide concentrations, which the study undertaken in this thesis. This Backward Euler methodology was executed through python scripting and a program was developed to output the systematic relative error for user desired isotopes of interest utilizing on the results of MCNP fuel burn up simulation. It was concluded that the Backward Euler methodology and the Bateman equations successfully replicated the MCNP estimated concentrations given the appropriate one group cross sections. Additionally, it was determined that for select isotopes of interest the systematic uncertainty for the associated concentration can be estimated
A Multifaceted Investigation of Cloud Microphysics: From Improving Convective Cloud Microphysics Parameterizations to Revealing Aerosol-Cirrus Cloud Interactions
No full textCloud microphysical processes and their parameterizations are at the core of multi-scale atmospheric modeling but still remain one of the major sources of uncertainty in weather and climate simulations. The aims of this dissertation are twofold: advancing our understanding of the physical processes involved in aerosol-cloud interactions and developing new parameterizations for better representing cloud microphysics in global climate models (GCMs). This dissertation is divided into two main parts. Part I focuses on evaluating and improving cloud microphysics parameterizations for convective clouds in GCMs. Part II focuses on investigating the impacts of volcanic aerosol on cirrus cloud using satellite observations, detailed microphysical model simulations and two GCMs.
GCMs have started treating convective clouds with detailed cloud microphysics parameterizations. However, the representations of convective cloud microphysical processes are often based on those for large-scale stratiform clouds, warranting further model evaluation for the fidelity of microphysics treatments transferred among various cloud types. Here, we evaluate and improve several aspects of the convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3), including processes of hydrometeor sedimentation, graupel production, convective snow detrainment, and rain generation against ground-based and satellite observations. Our model development efforts lead to substantial improvements in the simulations of cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation over the default model settings. These improvements set a better stage for future studies of convective cloud processes and their interactions with large-scale environments and aerosols.
Explosive volcanic eruptions inject a large amount of sulfur dioxide and ash particles into the upper troposphere and lower stratosphere, where volcanic-origin aerosols (sulfate and ashes) may modify cirrus cloud microphysics through ice nucleation but to an unknown extent due to limited research on this topic. Here, we aim to narrow this knowledge gap with the advent of advanced satellite retrievals of aerosol and cloud capturing the episodes of enhanced stratospheric aerosol loadings produced by modern moderate-magnitude eruptions. An analysis of 10-yr satellite datasets shows a phenomenal decrease in number and increase in size of cirrus ice crystals in the midlatitude lower stratosphere in response to ash-rich volcanic eruptions (2008 Kasatochi, 2009 Sarychev), indicative of heterogeneous freezing on volcanic ash suppressing homogeneous freezing. Conversely, cirrus clouds for the ash-deficit scenario (2015 Calbuco) are found to have up to 2.2 times more ice crystals, implying a moderately enhanced homogeneous freezing on volcanic sulfate aerosols. These impacts of aerosol on cirrus cloud, disentangling influences from meteorological co-variability, are most likely. Cloud parcel model with detailed physical ice nucleation processes is employed to elucidate the mechanisms of aerosol effects and the modeling results corroborate the observational findings. Sensitivity modeling experiments are also performed using two GCMs, CESM2.2 and E3SM-PA. Impacts of sub-grid scale vertical velocity generated by gravity waves on cirrus ice formation and volcanic ash emissions are found absent, identifying models��� inability to accurately capture the response of cirrus clouds to volcanic emissions and areas for future model development.
The studies in this dissertation advance our understanding of volcanic aerosol-cirrus cloud interactions on the process-level, stress the necessity and importance of accurate representations of cloud microphysical processes in GCMs, and advocate iterative effort in improving parameterizations as GCMs are the only means by which we project future climate
Reducing Risky Driving Behaviors by Considering Personality Factors
No full textTraffic accidents at intersections have been a concerning issue that negatively affects driving safety, with nearly 1000 people are killed in traffic accidents involving running red lights every year. However, driving safety issues at traffic light intersection remains understudied compared with other driving safety topics such as aggressive driving or distracted driving.
Current driving studies show that driving safety and driving behaviors are affected by many individual characteristics, such as age, sex, and personality. While other individual characteristics have been deeply studied, personality, as a well-developed and widely recognized individual characteristic, has not been given equal amount of attention in the study field of driving safety and behaviors.
In this dissertation, three studies were designed and conducted to further investigate the relationship between personality and risky driving behaviors. Data analysis results showed a strong correlation (R = 0.74) between the category of extraversion/introversion personalities and risky driving behaviors, meaning that the more extraverted the person is, the more risky driving behaviors they tend to exhibit. Theory on personality suggests that this is because extraverts are not as capable in perceiving risk-related information from the driving context compared to introverts. Targeting these characteristics, this research showed that increasing information salience and redundantly displaying information can help reduce risky driving behaviors, especially among extraverts.
The findings from this dissertation provide contributions in both scientific and practical aspects. For the scientific aspect, this dissertation provides a foundation of studying the relationships between personalities and risky behaviors in fast-paced real-life decision-making scenarios, such as driving. Moreover, this dissertation helps draw research attention to some understudied aspects such as driving decisions and safety at traffic light intersections.
This research also contributes in a practical sense to the design of safe transportation systems. The results can be used to design and develop vehicle assistance technologies that target specific personalities. Furthermore, findings from this dissertation can be used in future research of machine learning and adaptive autonomous driving systems. Additionally, findings from this dissertation can be used to design driving safety courses and training programs that target people���s personalities and specific needs to help improve driving safety
Planning Under Uncertainty with Unreliable Robotic Actuators
No full textWe focus on a critical aspect of autonomous robotics: the challenge of decision-making under the uncertainty of inevitable actuator degradation. Through the lens of physically embodied decision-makers, this research explores the complexity in modeling and planning for robotic actuator deterioration and failure. By an analogy to biological aging, we explore the necessity for agents to anticipate and plan for their own senescence, thus embracing their finite lifespan to maximize their utility. This shift towards acknowledging and planning for actuator frailty is particularly crucial for robotic explorers on interplanetary and interstellar missions, where autonomous, resilient decision-making is paramount.
Central to our approach is the introduction of Fallible Actuator Markov Decision Processes (FA-MDPs), an extension of the traditional MDP framework that incorporates actuator reliability into planning. This allows for the anticipation of failures, enabling strategic actuator usage and rapid adaptation post-failure. Our methodology leverages the inherent structure of FA-MDPs to decompose the problem into manageable sub-problems which increases solver efficiency. Furthermore, we explore the concept of actuator dominance and introduce virtual actuators to model k-shot and degrading actuators, thereby extending our failure model and improving planning performance.
The contributions of this thesis include: (1) A novel framework for incorporating actuator reliability into planning, enabling proactive planning for actuator failures. (2) An improved solution methodology for FA-MDPs through problem decomposition and a value function lattice, demonstrating superior performance over naive solvers. (3) An analysis of actuator relationships to further enhance planning performance and address actuator degradation. This work represents a step towards the development of long-lived autonomous robots capable of navigating the uncertainties of dynamic environments and their own inevitable deterioration