This dissertation aims to expand the current one-dimensional game theory based model to a multidimensional model for multi-actor predictive analytics and generalize the concept of position to address problems where actors’ positions are distributed over a position spectrum. The one-dimensional models are used for the problems where actors are interacting in a single issue space only. This is less than an ideal assumption since, in most cases, players’ strategies may depend on the dynamics of multiple issues when dealing with other players. In this research, the one-dimensional model is expanded to N-Dimensional model by considering different positions, and separate salience values, across different axes for the players. The model predicts the outcome for a given problem by taking into account stakeholder’s positions in different dimensions and their conflicting perspectives. Furthermore, we generalize the concept of position in the model to include continuous positions for the actors throughout the position spectrum, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between positions of actors. The proposed models are able to attain the same results as the previous one-dimensional models. In addition, to illustrate the capability of the proposed models, multiple case studies are designed and examined to assess the models’ capability and explainability.

The effective implementation of infrastructure asset management systems within organizations that own, operate, and manage infrastructure assets is critical to address the main challenges facing the infrastructure industry (e.g., infrastructure ageing and deterioration, maintenance backlogs, strict regulatory operating conditions, limited financial resources, and losing valuable experience through retirements). Infrastructure asset management systems contain connectivity between major operational components and such connectivity can lead to systemic risks (i.e., dependence-induced failures). This thesis analyzes the asset management system as a network of connected components (i.e., nodes and links) to identify critical components exposed to systemic risks induced by information asymmetry and information overload. This thesis applies descriptive and prescriptive analytics strategies to address information asymmetry and information overload and predictive analytics is employed to enhance the resilience. Specifically, descriptive analytics was employed to visualize the key performance indicators of infrastructure assets ensuring that all asset management stakeholders make decisions using consistent information sources and that they are not overwhelmed by having access to the entire database. Predictive analytics is employed to classify the resilience key performance indicator pertaining to the forced outage rapidity of power infrastructure components enabling power infrastructure owners to estimate the rapidity of an outage soon after its occurrence, and thus allocating the appropriate resources to return the infrastructure to operation. Using predictive analytics allows decision-makers to use consistent and clear information to inform their decision to respond to forced outage occurrences. Finally, prescriptive analytics is applied to optimize the asset management system network by increasing the connectivity of the network and in turn decreasing the exposure of the asset management system to systemic risk from information asymmetry and information overload. By analyzing an asset management system as a network and applying descriptive-, predictive-, and prescriptive analytics strategies, this dissertation illustrates how systemic risk exposure, due to information asymmetry and information overload could be mitigated and how power infrastructure resilience could be enhanced in response to forced outage occurrences. / Thesis / Doctor of Science (PhD) / Effective infrastructure asset management systems are critical for organizations that own, manage, and operate infrastructure assets. Infrastructure asset management systems contain main components (e.g., engineering, project management, resourcing strategy) that are dependent on information and data. Inherent within this system is the potential for failures to cascade throughout the entire system instigated by such dependence. Within asset management, such cascading failures, known as systemic risks, are typically caused by stakeholders not using the same information for decision making or being overwhelmed by too much information. This thesis employs analytics strategies including: i) descriptive analytics to present only relevant and meaningful information necessary for respective stakeholders, ii) predictive analytics to forecast the resilience key performance indicator, rapidity, enabling all stakeholders to make future decisions using consistent projections, and iii) prescriptive analytics to optimize the asset management system by introducing additional information connections between main components. Such analytics strategies are shown to mitigate the systemic risks within the asset management system and enhance the resilience of infrastructure in response to an unplanned disruption.

One of the key determinants of an organization’s success is its ability to adapt to marketplace change. Given this reality, how do organizations survive or even thrive in today’s dynamic markets? The answer to this question is highly related to the adaptability of one of the organization’s key resource: its employees. Indeed, the central component of an organization’s success will depend on its ability to drive changes in the mental models of individual employees. Moreover, a critical facilitator of that will be the development of decision support tools that support change of those mental models. In response to this need there has been a tremendous growth in business analytic decision support tools, estimated to reach almost $200 billion in sales by 2019. The premise of this research is that these decision support tools are ill-suited to support true mental model change because they have focused on a feedback-enabled view and generally lack a predictive (feedforward-enabled) view of the likely outcomes of the decision. The purpose of this research is to study how changes in mental models can be facilitated through this feedforward mechanisms within the DSS tool. This research used a mixed method approach, leveraging the strengths of quantitative and qualitative research methodologies, to study this research question. The research showed that the feedforward-enabled DSS tool did create more mental model change and alignment (versus an ideal solution) compared to the control. The feedforward enabled tool also produced better alignment than the feedback-enabled decision support tool. In fact, the feedback-enabled decision support was shown to result in a poorer alignment with the ideal solution. This paper concludes by suggesting five areas for future research. / Business Administration/Management Information Systems

It is the norm for institutions to report on their retention and graduation rates only for first-year student cohorts. Colleges and universities that report their first-year retention rates in the 90% range often do not account for their newly admitted transfer students. Much of the nuance in reporting retention comes from unaccounted transfer student registrations and enrollments. Reporting transfer retention is also much harder, since many transfer students do not have predictable patterns of enrollment. This study examined factors that contribute to graduation, dropout and persistence and how they differ by race, socioeconomic class, and gender. Based on a new student questionnaire conducted in 2015, 2016, 2017 by a large research institution in the Mid Atlantic, an exploratory statistical technique CHAID (Chi-Squared Automatic Interaction Detection) designed for a categorical dependent variable, was employed to establish the characteristics of transfer students who had a high probability to drop out after transferring to their new institution. Examining the dendrogram, one can easily classify the various “at-risk” student groups by tracing each of the terminal groups to the root of the tree. The results of this study provide context and information for developing transfer-friendly programming and interventions at both community colleges and four-year institutions. The results will be valuable to senior-level staff, front line student support staff, faculty, and community organizations focused on helping students who seek re-enrollment after an extended academic leave period. Additionally, this study will demonstrate how modeling techniques can be used to develop predictive models for different populations, across different colleges. / Urban Education

Yes / This research highlights a contextual application for big data within a HR case study setting. This is achieved through the development of a normative conceptual model that seeks to envelop employee behaviors and attitudes in the context of organizational change readiness. This empirical application considers a data sample from a large public sector organization and through applying Structural Equation Modelling (SEM) identifies salary, job promotion, organizational loyalty and organizational identity influences on employee job satisfaction (suggesting and mediating employee readiness for organizational change). However in considering this specific context, the authors highlight how, where and why such a normative approach to employee factors may be limited and thus, proposes through a framework which brings together big data principles, implementation approaches and management commitment requirements can be applied and harnessed more effectively in order to assess employee attitudes and behaviors as part of wider HR predictive analytics (HRPA) approaches. The researchers conclude with a discussion on these research elements and a set of practical, conceptual and management implications of the findings along with recommendations for future research in the area.

Yes / Over the past few decades, the banking sectors in Latin America have undergone rapid structural changes to improve the efficiency and resilience of their financial systems. The up-to-date literature shows that all the research studies conducted to analyze the above-mentioned efficiency are based on a deterministic data envelopment analysis (DEA) model or econometric frontier approach. Nevertheless, the deterministic DEA model suffers from a possible lack of statistical power, especially in a small sample. As such, the current research paper develops the technique of satisficing DEA to examine the still less explored case of Peru. We propose a Satisficing DEA model applied to 14 banks operating in Peru to evaluate the bank-level efficiency under a stochastic environment, which is free from any theoretical distributional assumption. The proposed model does not only report the bank efficiency, but also proposes a new framework for peer mining based on the Bayesian analysis and potential improvements with the bias-corrected and accelerated confidence interval. Our study is the first of its kind in the literature to perform a peer analysis based on a probabilistic approach.

Most epidemiological systems described in the literature are built for evaluation and analysis of specific diseases, such as Influenza-like-illness. The modeling environments that support these systems are implemented for specific diseases and epidemiological models. Hence they are not reusable or extendable. This thesis focuses on the design and development of an integrated analytical environment with flexible data integration methodologies and multi-level web services for evaluation and forecasting of various epidemics in different regions of the world. The environment supports analysis of epidemics based on any combination of disease, surveillance sources, epidemiological models, geographic regions and demographic factors. The environment also supports evaluation and forecasting of epidemics when various policy-level and behavioral interventions are applied, that may inhibit the spread of an epidemic. First, we describe data integration methodologies and schema design, for flexible experiment design, storage and query retrieval mechanisms related to large scale epidemic data. We describe novel techniques for data transformation, optimization, pre-computation and automation that enable flexibility, extendibility and efficiency required in different categories of query processing. Second, we describe the design and engineering of adaptable middleware platforms based on service-oriented paradigms for interactive workflow, communication, and decoupled integration. This supports large-scale multi-user applications with provision for online analysis of interventions as well as analytical processing of forecast computations. Using a service-oriented architecture, we have provided a platform-as-a-service representation for evaluation and forecasting of epidemics. We demonstrate the applicability of our integrated environment through development of the applications, DISIMS and EpiCaster. DISIMS is an interactive web-based system for evaluating the effects of dynamic intervention strategies on epidemic propagation. EpiCaster is a situation assessment and forecasting tool for projecting the state of evolving epidemics such as flu and Ebola in different regions of the world. We discuss how our platform uses existing technologies to solve a novel problem in epidemiology, and provides a unique solution on which different applications can be built for analyzing epidemic containment strategies. / Ph. D.

The dissertation comprises four manuscripts that aim to enhance the knowledge on strategic man-agement of professional football clubs within Germany, especially with regard to the prediction of success. Apart from manuscript A which is a review of extant literature of football manage-ment variables leading to success, and manuscript B, which is a qualitative investigation on sus-tainable success factors affecting professional football clubs, the remaining manuscripts C and D comprise two empirical studies; they can be further partitioned in manuscript C, investigating the prediction of failure with the worst-case scenario of bankruptcy and manuscript D, covering the prediction of failure and success in a proof of concept based on machine learning.

El presente proyecto se centra en el desarrollo de un modelo predictivo que permite pronosticar el diagnóstico de la diabetes mellitus tipo 2, siendo soportado por la herramienta SAP Predictive Analytics. Tiene como propósito el definir un modelo predictivo cuya implementación permita la optimización del proceso de diagnóstico de la Diabetes Mellitus tipo 2, además permitiendo que el resultado pueda brindar indicios sobre las acciones que una institución prestadora de servicios de cobertura de salud (tanto pública como privada) puede tomar por cada paciente en beneficio del mismo. Para lograr el propósito del proyecto, se ha realizado una investigación donde hemos alineado las 10 metas mundiales planteadas por la Organización Mundial de la Salud (OMS) a las 4 agrupaciones de enfermedades crónicas de mayor impacto económico, con lo que se ha identificado a la diabetes como la enfermedad crónica de mayor impacto para el Perú debido al creciente factor de incidencia en el país, causado principalmente por serias deficiencias en las costumbres diarias de alimentación y ejercicio en la población peruana, además de ser una enfermedad cuya propagación es alta en países en vías de desarrollo como el Perú debido a que no es mitigada adecuadamente por falta de prevención, desconocimiento o por motivos tan diversos como los económicos. Seguidamente, se realiza un benchmarking de herramientas de Predictive Analytics y las capacidades disponibles de las mismas para identificar cuál de ellas brinda el mejor soporte al modelo predictivo planteado, según el contexto identificado. / The project is focused on the development of a predictive model that enables prediction of the development of type 2 diabetes mellitus supported by SAP Predictive Analytics. Its main purpose is the definition of a predictive model that allows institutions that offer health coverage (both public and private) to optimize their diagnostic process and also enables the use of the prediction result in order to determine which actions could be taken, based on medical recommendations, on behalf of the patients benefit. To achieve the purpose of the project, an investigation has been done where there was an alignment between the 4 main chronic diseases based on their economic impact and the 10 global goals set by the World Health Organization, identifying diabetes as the chronic disease of the biggest impact for Peru due to the growing incidence factor in the country, caused mainly because of serious deficiencies in daily nutritional habits and a lack of workout culture, along with being a disease that has the most incidence in developing countries such as Peru since it is not mitigated accordingly because of lack of prevention, knowledge or economic motives. There has also been a benchmarking of Predictive Analytics tools in order to see which one complies the best with the requirements of both the chronic disease and the Peruvian context. / Tesis

abstract: Statistical process control (SPC) and predictive analytics have been used in industrial manufacturing and design, but up until now have not been applied to threshold data of vital sign monitoring in remote care settings. In this study of 20 elders with COPD and/or CHF, extended months of peak flow monitoring (FEV1) using telemedicine are examined to determine when an earlier or later clinical intervention may have been advised. This study demonstrated that SPC may bring less than a 2.0% increase in clinician workload while providing more robust statistically-derived thresholds than clinician-derived thresholds. Using a random K-fold model, FEV1 output was predictably validated to .80 Generalized R-square, demonstrating the adequate learning of a threshold classifier. Disease severity also impacted the model. Forecasting future FEV1 data points is possible with a complex ARIMA (45, 0, 49), but variation and sources of error require tight control. Validation was above average and encouraging for clinician acceptance. These statistical algorithms provide for the patient's own data to drive reduction in variability and, potentially increase clinician efficiency, improve patient outcome, and cost burden to the health care ecosystem. / Dissertation/Thesis / Ph.D. Engineering 2013