Harmful Freedom of Choice: Lessons from the Cellphone Market

This article focuses on the relationship between provider and customer, specifically on the complexity of available contracts in the cellphone market and the ways this complexity might be harmful to consumers. This article aims to elucidate the issues, fleshing them out both as a general phenomenon and as a specific implementation in the cellphone context. The aim is not to provide ultimate solutions, but to show the directions these solutions might take and the difficulties involved

Predicting the risk of injury of professional football players with machine learning

Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementSports analytics is quickly changing the way sports are played. With the rise of sensor data and new tracking technologies, data is collected at an unprecedented degree which allows for a plethora of innovative analytics possibilities, with the goal of uncovering hidden trends and developing new knowledge from data sources. This project creates a prediction model which predicts a player’s muscular injury in a professional football team using GPS and self-rating training data, by following a Data Mining methodology and applying machine learning algorithms. Different sampling techniques for imbalanced data are described and used. An analysis of the quality of the results of the different sampling techniques and machine learning algorithms are presented and discussed

Stock price change prediction using news text mining

Along with the advent of the Internet as a new way of propagating news in a digital format, came the need to understand and transform this data into information. This work presents a computational framework that aims to predict the changes of stock prices along the day, given the occurrence of news articles related to the companies listed in the Down Jones Index. For this task, an automated process that gathers, cleans, labels, classifies, and simulates investments was developed. This process integrates the existing data mining and text algorithms, with the proposal of new techniques of alignment between news articles and stock prices, pre-processing, and classifier ensemble. The result of experiments in terms of classification measures and the Cumulative Return obtained through investment simulation outperformed the other results found after an extensive review in the related literature. This work also argues that the classification measure of Accuracy and incorrect use of cross validation technique have too few to contribute in terms of investment recommendation for financial market. Altogether, the developed methodology and results contribute with the state of art in this emerging research field, demonstrating that the correct use of text mining techniques is an applicable alternative to predict stock price movements in the financial market.Com o advento da Internet como um meio de propagação de notícias em formato digital, veio a necessidade de entender e transformar esses dados em informação. Este trabalho tem como objetivo apresentar um processo computacional para predição de preços de ações ao longo do dia, dada a ocorrência de notícias relacionadas às companhias listadas no índice Down Jones. Para esta tarefa, um processo automatizado que coleta, limpa, rotula, classifica e simula investimentos foi desenvolvido. Este processo integra algoritmos de mineração de dados e textos já existentes, com novas técnicas de alinhamento entre notícias e preços de ações, pré-processamento, e assembleia de classificadores. Os resultados dos experimentos em termos de medidas de classificação e o retorno acumulado obtido através de simulação de investimentos foram maiores do que outros resultados encontrados após uma extensa revisão da literatura. Este trabalho também discute que a acurácia como medida de classificação, e a incorreta utilização da técnica de validação cruzada, têm muito pouco a contribuir em termos de recomendação de investimentos no mercado financeiro. Ao todo, a metodologia desenvolvida e resultados contribuem com o estado da arte nesta área de pesquisa emergente, demonstrando que o uso correto de técnicas de mineração de dados e texto é uma alternativa aplicável para a predição de movimentos no mercado financeiro

Recontextualizing dance skills: Overcoming impediments to motor learning and expressivity in ballet dancers

The process of transmitting ballet’s complex technique to young dancers can interfere with the innate processes that give rise to efficient, expressive and harmonious movement. With the intention of identifying possible solutions, this article draws on research across the fields of neurology, psychology, motor learning, and education, and considers their relevance to ballet as an art form, a technique, and a training methodology. The integration of dancers’ technique and expressivity is a core theme throughout the paper. A brief outline of the historical development of ballet’s aesthetics and training methods leads into factors that influence dancers’ performance. An exploration of the role of the neuromotor system in motor learning and the acquisition of expert skills reveals the roles of sensory awareness, imagery, and intention in cuing efficient, expressive movement. It also indicates potentially detrimental effects of conscious muscle control, explicit learning and persistent naïve beliefs. Finally, the paper presents a new theory regarding the acquisition of ballet skills. Recontextualization theory proposes that placing a problematic task within a new context may engender a new conceptual approach and/or sensory intention, and hence the genesis of new motor programs; and that these new programs may lead to performance that is more efficient, more rewarding for the dancer, more pleasing aesthetically, and more expressive. From an anecdotal point of view, this theory appears to be supported by the progress of many dancers at various stages of their dancing lives

Systems Modeling As A Means Of Building Accuate Mental Models Of Physiology Core Concepts In Undergraduate And Graduate Health Sciences Students

Accurate medical and health sciences problem solving relies upon a solid foundation of basic sciences content knowledge, primarily physiology. Yet, due to its nature as a dynamic system of interconnected, networked, concepts, physiology is often difficult for students to master. The three studies in this dissertation explore the use of a cognitive tool, systems modeling, to facilitate the development of an accurate mental model of physiology content knowledge in undergraduate and graduate physiology students. In the first study, undergraduate physiology student participation within online asynchronous peer group systems modeling activities was associated with progressive improvement on multiple choice question answer accuracy in the modeling condition versus the written discussion post condition. In the second study, graduate physician assistant students ranked systems modeling to be the top strategy for learning physiology content in the basic sciences year of study and the second to top strategy for retaining that content into the clinical year. In the third study, graduate physician assistant students demonstrated increased use of integrated core concept terms, after systems modeling activity participation, when describing the pathophysiology threshold concept of inflammation in writing. Together, these three studies provide evidence that the systems modeling strategy is an effective cognitive tool that contributes to improved student learning and retention of physiology content through visualization and subsequent refinement of the learner’s mental model of the problem space

A Comparison of Causal Inference Methods and Their Application in Big Data Analytics

With the rise of Big Data analytics, the new field of causal inference (Pearl, 2009) has received more attention in business research fields such as Accounting (Lawrence, Minutti-Meza, & Zhang, 2011) and Marketing (Manganaris, Bhasin, Reid, & Hermiz Keith, 2010). Traditional statistics focuses on correlation which may lead to misleading conclusions because the estimates can be severely biased even when data sets are large. The objective of causal inference is to obtain estimates from observational data that are unbiased and can thus be interpreted as causal. This study provides a systematic comparison of the performance of four causal inference methods which are Propensity Score Matching, Standardization, Inverse Probability Weighting and Orthogonal Arrays. The risk difference, risk ratio and odds ratio are compared for these estimators. This research uses bootstrapping with different sample sizes to ensure that reliable estimates for bias and mean squared error are obtained. Topics relevant to method selection and recommendations for use of the methods are offered. Additionally, with applying the suggestions and recommendations derived from the simulation, two examples are used to demonstrate how causal inference improves estimates. The first example explores the use of causative analytics for improving retention and graduation rates using a series of causal inference methods with semester-based information about student performance. The findings reveal that the effect of living on campus and math preparation for improving student retention rates and graduation rates is considerably lower than traditional estimates showed. The second example investigates the relationship and effect size between the implementation of the UberX service and fatalities due to drunk driving among different age groups. The findings disclose that while traditional methods show that there is a statistically significant effect of UberX deployment on the number of DWI fatalities among youth ages 17-34 and older ages 35-65, the causal estimates are no longer statistically significant

BIASeD: Bringing Irrationality into Automated System Design

Human perception, memory and decision-making are impacted by tens of cognitive biases and heuristics that influence our actions and decisions. Despite the pervasiveness of such biases, they are generally not leveraged by today's Artificial Intelligence (AI) systems that model human behavior and interact with humans. In this theoretical paper, we claim that the future of human-machine collaboration will entail the development of AI systems that model, understand and possibly replicate human cognitive biases. We propose the need for a research agenda on the interplay between human cognitive biases and Artificial Intelligence. We categorize existing cognitive biases from the perspective of AI systems, identify three broad areas of interest and outline research directions for the design of AI systems that have a better understanding of our own biases.Comment: 14 pages, 1 figure; Accepted for presentation at the AAAI Fall Symposium 2022 on Thinking Fast and Slow and Other Cognitive Theories in A

The impact of 3D virtual environments with different levels of realism on route learning: a focus on age-based differences

With technological advancements, it has become notably easier to create virtual environments (VEs) depicting the real world with high fidelity and realism. These VEs offer some attractive use cases for navigation studies looking into spatial cognition. However, such photorealistic VEs, while attractive, may complicate the route learning process as they may overwhelm users with the amount of information they contain. Understanding how much and what kind of photorealistic information is relevant to people at which point on their route and while they are learning a route can help define how to design virtual environments that better support spatial learning. Among the users who may be overwhelmed by too much information, older adults represent a special interest group for two key reasons: 1) The number of people over 65 years old is expected to increase to 1.5 billion by 2050 (World Health Organization, 2011); 2) cognitive abilities decline as people age (Park et al., 2002). The ability to independently navigate in the real world is an important aspect of human well-being. This fact has many socio-economic implications, yet age-related cognitive decline creates difficulties for older people in learning their routes in unfamiliar environments, limiting their independence. This thesis takes a user-centered approach to the design of visualizations for assisting all people, and specifically older adults, in learning routes while navigating in a VE. Specifically, the objectives of this thesis are threefold, addressing the basic dimensions of: ❖ Visualization type as expressed by different levels of realism: Evaluate how much and what kind of photorealistic information should be depicted and where it should be represented within a VE in a navigational context. It proposes visualization design guidelines for the design of VEs that assist users in effectively encoding visuospatial information. ❖ Use context as expressed by route recall in short- and long-term: Identify the implications that different information types (visual, spatial, and visuospatial) have over short- and long-term route recall with the use of 3D VE designs varying in levels of realism. ❖ User characteristics as expressed by group differences related to aging, spatial abilities, and memory capacity: Better understand how visuospatial information is encoded and decoded by people in different age groups, and of different spatial and memory abilities, particularly while learning a route in 3D VE designs varying in levels of realism. In this project, the methodology used for investigating the topics outlined above was a set of controlled lab experiments nested within one. Within this experiment, participants’ recall accuracy for various visual, spatial, and visuospatial elements on the route was evaluated using three visualization types that varied in their amount of photorealism. These included an Abstract, a Realistic, and a Mixed VE (see Figure 2), for a number of route recall tasks relevant to navigation. The Mixed VE is termed “mixed” because it includes elements from both the Abstract and the Realistic VEs, balancing the amount of realism in a deliberate manner (elaborated in Section 3.5.2). This feature is developed within this thesis. The tested recall tasks were differentiated based on the type of information being assessed: visual, spatial, and visuospatial (elaborated in Section 3.6.1). These tasks were performed by the participants both immediately after experiencing a drive-through of a route in the three VEs and a week after that; thus, addressing short- and long-term memory, respectively. Participants were counterbalanced for their age, gender, and expertise while their spatial abilities and visuospatial memory capacity were controlled with standardized psychological tests. The results of the experiments highlight the importance of all three investigated dimensions for successful route learning with VEs. More specifically, statistically significant differences in participants’ recall accuracy were observed for: 1) the visualization type, highlighting the value of balancing the amount of photorealistic information presented in VEs while also demonstrating the positive and negative effects of abstraction and realism in VEs on route learning; 2) the recall type, highlighting nuances and peculiarities across the recall of visual, spatial, and visuospatial information in the short- and long-term; and, 3) the user characteristics, as expressed by age differences, but also by spatial abilities and visuospatial memory capacity, highlighting the importance of considering the user type, i.e., for whom the visualization is customized. The original and unique results identified from this work advance the knowledge in GIScience, particularly in geovisualization, from the perspective of the “cognitive design” of visualizations in two distinct ways: (i) understanding the effects that visual realism has—as presented in VEs—on route learning, specifically for people of different age groups and with different spatial abilities and memory capacity, and (ii) proposing empirically validated visualization design guidelines for the use of photorealism in VEs for efficient recall of visuospatial information during route learning, not only for shortterm but also for long-term recall in younger and older adults

Information Retrieval of Opioid Dependence Medications Reviews from Health-Related Social Media

Social media provides a convenient platform for patients to share their drug usage experience with others; consequently, health researchers can leverage this potential data to gain valuable information about users’ drug satisfaction. Since the 1990s, opioid drug abuse has become a national crisis. In order to reduce the dependency of opioids, several drugs have been presented to the market, but little is known about patient satisfaction with these treatments. Sentiment analysis is a method to measure and interpret patients’ satisfaction. In the first phase of this study, we aimed to utilize social media posts to predict patients’ sentiment towards opioid dependency treatment. We focused on Suboxone, a well-known opioid dependence medication, as our targeted treatment and Drugs.com, an online healthcare forum as our data source. For the purpose of our analysis, we first collected 1,532 posts to create a training dataset, split the posts to sentences, and annotated 1100 sentences for sentiment analysis. To predict patients’ sentiment, we extracted features from patients’ posts, including bigrams, trigrams, and features extracted from topic modeling. To develop the prediction model, we used two machine learning methods, Naïve Bayes and SVM, for predicting sentiment. We achieved the best performance using SVM, getting an accuracy of 61% for SVM. In the second phase of this study, we also aimed to understand the behavior of the patients toward the targeted medication. To accomplish this goal, we used the Health Belief Model (HBM), a social psychological model that describes and predicts patients’ health-related attitudes in action, benefit, barrier, and threat categories, for predicting such behavior from patients’ reviews. We also utilized the same combinations of features and machine learning methods that we used in the first phase of the study, and the best accuracy performance was 47% for the SVM classifier as compared to 43% as our baseline