An Assessment of How Mobile Telecommunications Competition Effect on Mobile Call Prices

世界范围内，各个行业都存在激烈的竞争。文献中，前人已经从各个角度分析了竞争带给经济的影响。本文通过分析来自49个国家，2010年到2014年5年的面板数据，分析通讯行业内的竞争对通讯费的影响。本文的目标是判断行业竞争是否是影响通讯价格最主要的因素，以及竞争在发展中国家和发达国家的不同影响。本文中，通讯费的数据来自国际通讯报告(以占国内生产总值的比例计算而来)，其他变量来自世界银行和维基百科。固定国家效应和时间效应被纳入模型中，本文结果显示：竞争对于通讯费有重要的正面影响；此外，HHI因素对于通讯费的影响在发展中国家更为显著。Competition is a wide spread phenomenon present in almost all the industries in the world. In the academic research field competition has been examined from different angles. This thesis assesses the effect of mobile telecommunications competition on mobile call prices on a panel of 49 countries for a period of five years (from 2010 to 2014). The objective of this paper is to find out if competiti...学位：经济学硕士院系专业：王亚南经济研究院_金融学学号：2772014115463

Runaway Feedback Loops in Predictive Policing

Predictive policing systems are increasingly used to determine how to allocate police across a city in order to best prevent crime. Discovered crime data (e.g., arrest counts) are used to help update the model, and the process is repeated. Such systems have been empirically shown to be susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. In response, we develop a mathematical model of predictive policing that proves why this feedback loop occurs, show empirically that this model exhibits such problems, and demonstrate how to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Our results are quantitative: we can establish a link (in our model) between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. Moreover, we can also demonstrate the way in which \emph{reported} incidents of crime (those reported by residents) and \emph{discovered} incidents of crime (i.e. those directly observed by police officers dispatched as a result of the predictive policing algorithm) interact: in brief, while reported incidents can attenuate the degree of runaway feedback, they cannot entirely remove it without the interventions we suggest.Comment: Extended version accepted to the 1st Conference on Fairness, Accountability and Transparency, 2018. Adds further treatment of reported as well as discovered incident

MS 226 Guide to the Ruth SoRelle Papers (1950s-2019)

The collection includes photos, plaques, framed objects, clipping books, clipping files, reference and topic files, childhood writings, science and medical articles, reporter notebooks, and clippings from SoRelle\u27s budding career as a journalist at the University of Texas. One notable area of interest are the articles related to the HIV/AIDS epidemic in Houston. See more at MS 226

Approximation algorithm for the kinetic robust K-center problem

AbstractTwo complications frequently arise in real-world applications, motion and the contamination of data by outliers. We consider a fundamental clustering problem, the k-center problem, within the context of these two issues. We are given a finite point set S of size n and an integer k. In the standard k-center problem, the objective is to compute a set of k center points to minimize the maximum distance from any point of S to its closest center, or equivalently, the smallest radius such that S can be covered by k disks of this radius. In the discrete k-center problem the disk centers are drawn from the points of S, and in the absolute k-center problem the disk centers are unrestricted.We generalize this problem in two ways. First, we assume that points are in continuous motion, and the objective is to maintain a solution over time. Second, we assume that some given robustness parameter 0<t⩽1 is given, and the objective is to compute the smallest radius such that there exist k disks of this radius that cover at least ⌈tn⌉ points of S. We present a kinetic data structure (in the KDS framework) that maintains a (3+ε)-approximation for the robust discrete k-center problem and a (4+ε)-approximation for the robust absolute k-center problem, both under the assumption that k is a constant. We also improve on a previous 8-approximation for the non-robust discrete kinetic k-center problem, for arbitrary k, and show that our data structure achieves a (4+ε)-approximation. All these results hold in any metric space of constant doubling dimension, which includes Euclidean space of constant dimension

Fair Meta-Learning: Learning How to Learn Fairly

Data sets for fairness relevant tasks can lack examples or be biased according to a specific label in a sensitive attribute. We demonstrate the usefulness of weight based meta-learning approaches in such situations. For models that can be trained through gradient descent, we demonstrate that there are some parameter configurations that allow models to be optimized from a few number of gradient steps and with minimal data which are both fair and accurate. To learn such weight sets, we adapt the popular MAML algorithm to Fair-MAML by the inclusion of a fairness regularization term. In practice, Fair-MAML allows practitioners to train fair machine learning models from only a few examples when data from related tasks is available. We empirically exhibit the value of this technique by comparing to relevant baselines.Comment: arXiv admin note: substantial text overlap with arXiv:1908.0909

Fairness in representation: quantifying stereotyping as a representational harm

While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention. In this paper, we formalize two notions of stereotyping and show how they manifest in later allocative harms within the machine learning pipeline. We also propose mitigation strategies and demonstrate their effectiveness on synthetic datasets.Comment: 9 pages, 6 figures, Siam International Conference on Data Minin