Uncertainty Visualization using Hypothetical Outcome Plots

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceData-driven decision-making is crucial for any business. With an increasing interest in Business Intelligence, Data Visualization is playing a major role in decision-making processes. For making a well-informed and accurate decision, it is important to understand uncertainty in the data visualizations. Uncertainty visualizations improve the way users understand the data, as well as the confidence in their conclusions. An important type of uncertainty visualizations is the Hypothetical Outcome Plots (HOPs), which allow the audience to gain an intuitive idea of uncertainty through animated sequences of random draws from a distribution, leading to a more accurate understanding and decision. This document intends to detail a proof-of-concept by carrying out a comparison of static visualization vs. HOPs in terms of efficiency and accuracy of results interpretation for Wayne Enterprises (fictional name) forecasting projects, in particularly the ones related with product launches and product loss of exclusivity. Wayne Enterprises is a world-leading supplier of advanced analytics, technological services and clinical investigation solutions for the life sciences industry. For that objective, it was built two prototypes using Python to support the proof-of-concept execution. A between-group experiment was carried out with 40 members of the German consulting team of Wayne Enterprises, where half answered a survey based on static visualizations and the other half based on HOPs. From this experiment, it is possible to conclude that HOPs can achieve similar results that static visualizations, with people taking the decision in less than half of the time when visualizing a HOP. Thus, it is possible to improve Wayne Enterprises decision-making process by accelerating it with Hypothetical Outcome Plots.A tomada de decisões baseada em dados é crucial para qualquer negócio. Com um interesse crescente em Business Intelligence, a Visualização de Dados está a desempenhar um papel importante nos processos de tomada de decisão. Para se tomar uma decisão bem informada e precisa, é importante compreender a incerteza nas visualizações de dados. As visualizações de incerteza melhoram a forma como os utilizadores compreendem os dados, bem como a confiança nas suas conclusões. Um tipo importante de visualizações de incerteza é o Hypothetical Outcome Plots (HOPs), que permite ao público obter uma ideia intuitiva da incerteza através de sequências animadas de desenhos aleatórios de uma distribuição, conduzindo a uma compreensão e decisão mais precisas. Este documento pretende detalhar uma prova de conceito através da realização de uma comparação entre visualizações estáticas e HOPs em termos de eficiência e exactidão de interpretação de resultados para projectos de forecast da Wayne Enterprises (nome fictício), em particular os relacionados com lançamentos de produtos e perda de exclusividade de produtos. A Wayne Enterprises é um líder mundial de análises avançadas, serviços tecnológicos e soluções de investigação clínica para a indústria das ciências da vida. Para esse objectivo, foram construídos dois protótipos utilizando Python para apoiar a execução da prova de conceito. Foi realizada uma experiência entre grupos com 40 membros da equipa de consultoria alemã da Wayne Enterprises, onde metade respondeu a um inquérito baseado em visualizações estáticas e a outra metade com base em HOPs. A partir desta experiência, é possível concluir que os HOPs podem alcançar resultados semelhantes aos das visualizações estáticas, com as pessoas a tomarem a decisão em menos de metade do tempo quando visualizam um HOP. Por conseguinte, é possível melhorar o processo de tomada de decisão da Wayne Enterprises, acelerando-o com Hypothetical Outcome Plots

Visualizing Uncertainty for Non-Expert End Users: The Challenge of the Deterministic Construal Error

There is a growing body of evidence that numerical uncertainty expressions can be used by non-experts to improve decision quality. Moreover, there is some evidence that similar advantages extend to graphic expressions of uncertainty. However, visualizing uncertainty introduces challenges as well. Here, we discuss key misunderstandings that may arise from uncertainty visualizations, in particular the evidence that users sometimes fail to realize that the graphic depicts uncertainty. Instead they have a tendency to interpret the image as representing some deterministic quantity. We refer to this as the deterministic construal error. Although there is now growing evidence for the deterministic construal error, few studies are designed to detect it directly because they inform participants upfront that the visualization expresses uncertainty. In a natural setting such cues would be absent, perhaps making the deterministic assumption more likely. Here we discuss the psychological roots of this key but underappreciated misunderstanding as well as possible solutions. This is a critical question because it is now clear that members of the public understand that predictions involve uncertainty and have greater trust when uncertainty is included. Moreover, they can understand and use uncertainty predictions to tailor decisions to their own risk tolerance, as long as they are carefully expressed, taking into account the cognitive processes involved

Multi-Level Audio-Visual Interactions in Speech and Language Perception

That we perceive our environment as a unified scene rather than individual streams of auditory, visual, and other sensory information has recently provided motivation to move past the long-held tradition of studying these systems separately. Although they are each unique in their transduction organs, neural pathways, and cortical primary areas, the senses are ultimately merged in a meaningful way which allows us to navigate the multisensory world. Investigating how the senses are merged has become an increasingly wide field of research in recent decades, with the introduction and increased availability of neuroimaging techniques. Areas of study range from multisensory object perception to cross-modal attention, multisensory interactions, and integration. This thesis focuses on audio-visual speech perception, with special focus on facilitatory effects of visual information on auditory processing. When visual information is concordant with auditory information, it provides an advantage that is measurable in behavioral response times and evoked auditory fields (Chapter 3) and in increased entrainment to multisensory periodic stimuli reflected by steady-state responses (Chapter 4). When the audio-visual information is incongruent, the combination can often, but not always, combine to form a third, non-physically present percept (known as the McGurk effect). This effect is investigated (Chapter 5) using real word stimuli. McGurk percepts were not robustly elicited for a majority of stimulus types, but patterns of responses suggest that the physical and lexical properties of the auditory and visual stimulus may affect the likelihood of obtaining the illusion. Together, these experiments add to the growing body of knowledge that suggests that audio-visual interactions occur at multiple stages of processing

Riders’ Lived Experiences About Ride-Sharing on the Concept of Customer Trust in the Choice of Rides in Dallas, Texas

A business’s long-term success is dependent on the ability to build and maintain customer trust. Failed projects within the sharing economy, which ride-sharing is part of, suggest distrust of the services and products of sharing economy companies. The purpose of this qualitative phenomenological study was to explore the lived experiences of riders using ride-sharing regarding the concept of customer trust in Dallas, Texas. The conceptual framework for this study was the theory of planned behavior. Interview data were gathered from 15 participants who met the inclusion requirements of being a rider with Uber or Lyft with ride experiences before, during, and after the COVID-19 epidemic. Data from the transcripts were inductively analyzed. Main themes were motivation to use ride-sharing, satisfaction with ride-sharing, and trust in ride-sharing. Key results included that the participants all had trust in ride-sharing and would continue to use ride-sharing. One of the main conclusions was that ride-sharing management may find reliability concerns with the service that can be addressed in a timely manner to increase revenue. Positive social change may be achieved because the participants in this study ranged from 21 to 60-plus years of age, and every age group commended the ride-share app’s user-friendliness. User-friendly technology, especially for users age 50 and above, may lead to individual, community, regional, and universal positive social change in the ride-sharing sector of the transportation industry

Doctor of Philosophy

dissertationManual annotation of clinical texts is often used as a method of generating reference standards that provide data for training and evaluation of Natural Language Processing (NLP) systems. Manually annotating clinical texts is time consuming, expensive, and requires considerable cognitive effort on the part of human reviewers. Furthermore, reference standards must be generated in ways that produce consistent and reliable data but must also be valid in order to adequately evaluate the performance of those systems. The amount of labeled data necessary varies depending on the level of analysis, the complexity of the clinical use case, and the methods that will be used to develop automated machine systems for information extraction and classification. Evaluating methods that potentially reduce cost, manual human workload, introduce task efficiencies, and reduce the amount of labeled data necessary to train NLP tools for specific clinical use cases are active areas of research inquiry in the clinical NLP domain. This dissertation integrates a mixed methods approach using methodologies from cognitive science and artificial intelligence with manual annotation of clinical texts. Aim 1 of this dissertation identifies factors that affect manual annotation of clinical texts. These factors are further explored by evaluating approaches that may introduce efficiencies into manual review tasks applied to two different NLP development areas - semantic annotation of clinical concepts and identification of information representing Protected Health Information (PHI) as defined by HIPAA. Both experiments integrate iv different priming mechanisms using noninteractive and machine-assisted methods. The main hypothesis for this research is that integrating pre-annotation or other machineassisted methods within manual annotation workflows will improve efficiency of manual annotation tasks without diminishing the quality of generated reference standards

Sensory and cognitive factors in multi-digit touch, and its integration with vision

Every tactile sensation – an itch, a kiss, a hug, a pen gripped between fingers, a soft fabric brushing against the skin – is experienced in relation to the body. Normally, they occur somewhere on the body’s surface – they have spatiality. This sense of spatiality is what allows us to perceive a partner’s caress in terms of its changing location on the skin, its movement direction, speed, and extent. How this spatiality arises and how it is experienced is a thriving research topic, compelled by growing interest in the nature of tactile experiences from product design to brain-machine interfaces. The present thesis adds to this flourishing area of research by examining the unified spatial quality of touch. How does distinct spatial information converge from separate areas of the body surface to give rise to our normal unified experience of touch? After explaining the importance of this question in Chapter 1, a novel paradigm to tackle this problem will be presented, whereby participants are asked to estimate the average direction of two stimuli that are simultaneously moved across two different fingerpads. This paradigm is a laboratory analogue of the more ecological task of representing the overall movement of an object held between multiple fingers. An EEG study in Chapter 2 will reveal a brain mechanism that could facilitate such aggregated perception. Next, by characterising participants’ performance not just in terms of error rates, but by considering perceptual sensitivity, bias, precision, and signal weighting, a series of psychophysical experiments will show that this aggregation ability differs for within- and between-hand perception (Chapter 3), is independent from somatotopically-defined circuitry (Chapter 4) and arises after proprioceptive input about hand posture is accounted for (Chapter 5). Finally, inspired by the demand for integrated tactile and visual experience in virtual reality and the potential of tactile interface to aid navigation, Chapter 6 will examine the contribution of tactile spatiality on visual spatial experience. Ultimately, the present thesis will reveal sensory factors that limit precise representation of concurrently occurring dynamic tactile events. It will point to cognitive strategies the brain may employ to overcome those limitations to tactually perceive coherent objects. As such, this thesis advances somatosensory research beyond merely examining the selectivity to and discrimination between experienced tactile inputs, to considering the unified experience of touch despite distinct stimulus elements. The findings also have practical implications for the design of functional tactile interfaces

ETHICAL LEADERSHIP IN HIGHER EDUCATION: EVOLUTION OF INSTITUTIONAL ETHICS LOGIC

In higher education, we face a decade in which institutional integrity and legitimacy is under fire. In the words of Charles Dickens, this is certainly \u27the worst of times\u27 both economically and ethically for our nation, as well as for our colleges and universities. While members of higher education call for student academic ethics reform, ethical infractions by institutional leaders and faculty permeate professional literature and news--student loan scandals, charges of plagiarism, and falsified research, are but a few. This study begins with the premise that perhaps our efforts toward reform should focus on a better holistic understanding of system dynamics. The research question driving this study is, \u27How does the interaction of agent work-related ethical beliefs and knowledge, perceived pressures, and institutional agents or entities influence the evolution of institutional ethics logic over time?\u27 Grounded theory methods provided the framework for this study; this research used a complexity leadership and network lens in which to examine a university\u27s ethics logic, as defined by participants. Complexity leadership proposes operating within a framework of mechanism-based theorizing (Uhl-Bien & Marion, in press). The Organizational Risk Analyzer (ORA) assisted coding and analysis of data, and DyNet, a modeling platform, assisted in manipulating data for an understanding of interrelated complexity mechanisms embedded in university ethics logic. Findings incorporate a faculty ethics logic model, as well as a model of dynamical processes of university ethics logic evolution. The evolution model recognizes that: * The leadership process shifts by leader function, context, or structure. * The process underlying network robustness reflects holistic shifts in relationships with the addition or removal of nodes and links, and represents different or new patterns of behavior * The process of agentic correlation shifts as nodal presence or relationships change * The process of information diffusion shifts as network context, structure, or content changes Theoretical, methodological, higher education implications conclude the study

Agent-based Modeling of Urban Exposome Interventions: Prospects, Model Architectures and Methodological Challenges

With ever more people living in cities worldwide, it becomes increasingly important to understand and improve the impact of the urban habitat on livability, health behaviors and health outcomes. However, implementing interventions that tackle the exposome in complex urban systems can be costly and have long-term, sometimes unforeseen, impacts. Hence, it is crucial to assess the health impact, cost-effectiveness, and social distributional impacts of possible urban exposome interventions before implementing them. Spatial agent-based modeling can capture complex behavior-environment interactions, exposure dynamics, and social outcomes in a spatial context. This paper discusses model architectures and methodological challenges for successfully modeling urban exposome interventions using spatial agent-based modeling. We review the potential and limitations of the method; model components required to capture active and passive exposure and intervention effects; human-environment interactions and their integration into the macro-level health impact assessment and social costs benefit analysis; strategies for model calibration. Major challenges for a successful application of agent-based modeling to urban exposome intervention assessment are (1) the design of realistic behavioral models that can capture different types of exposure and that respond to urban interventions, (2) the mismatch between the possible granularity of exposure estimates and the evidence for corresponding exposure-response functions, (3) the scalability issues that emerge when aiming to estimate long-term effects such as health and social impacts based on high-resolution models of human-environment interactions, (4) as well as the data- and computational complexity of calibrating the resulting agent-based model. Although challenges exist, strategies are proposed to improve the implementation of ABM in exposome research