Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs

Given a graph where vertices represent alternatives and arcs represent pairwise comparison data, the statistical ranking problem is to find a potential function, defined on the vertices, such that the gradient of the potential function agrees with the pairwise comparisons. Our goal in this paper is to develop a method for collecting data for which the least squares estimator for the ranking problem has maximal Fisher information. Our approach, based on experimental design, is to view data collection as a bi-level optimization problem where the inner problem is the ranking problem and the outer problem is to identify data which maximizes the informativeness of the ranking. Under certain assumptions, the data collection problem decouples, reducing to a problem of finding multigraphs with large algebraic connectivity. This reduction of the data collection problem to graph-theoretic questions is one of the primary contributions of this work. As an application, we study the Yahoo! Movie user rating dataset and demonstrate that the addition of a small number of well-chosen pairwise comparisons can significantly increase the Fisher informativeness of the ranking. As another application, we study the 2011-12 NCAA football schedule and propose schedules with the same number of games which are significantly more informative. Using spectral clustering methods to identify highly-connected communities within the division, we argue that the NCAA could improve its notoriously poor rankings by simply scheduling more out-of-conference games.Comment: 31 pages, 10 figures, 3 table

Calorie menu labeling on quick-service restaurant menus: an updated systematic review of the literature

Nutrition labels are one strategy being used to combat the increasing prevalence of overweight and obesity in the United States. The Patient Protection and Affordable Care Act of 2010 mandates that calorie labels be added to menu boards of chain restaurants with 20 or more locations. This systematic review includes seven studies published since the last review on the topic in 2008. Authors searched for peer-reviewed studies using PUBMED and Google Scholar. Included studies used an experimental or quasi-experimental design comparing a calorie-labeled menu with a no-calorie menu and were conducted in laboratories, college cafeterias, and fast food restaurants. Two of the included studies were judged to be of good quality, and five of were judged to be of fair quality. Observational studies conducted in cities after implementation of calorie labeling were imprecise in their measure of the isolated effects of calorie labels. Experimental studies conducted in laboratory settings were difficult to generalize to real world behavior. Only two of the seven studies reported a statistically significant reduction in calories purchased among consumers using calorie-labeled menus. The current evidence suggests that calorie labeling does not have the intended effect of decreasing calorie purchasing or consumption

Post-play communication of emotions facilitates prosociality and cooperation

Social decisions with monetary consequences are often accompanied with emotional consequences. Previous studies document a robust role of pre-play message communication in facilitating pro-sociality and cooperation. Yet, the effects of communicating emotional experiences in social interactions (particularly post-play) remain understudied. Here, we examine the value of a social environment where emotional expressions are shared post-play in contrast to a private environment where emotion exposure is absent. We develop an experimental design that facilitates emotion exposure and can be readily administered in or outside the laboratory. In this pre-registered online study, participants (N = 196) completed incentivized extensions of the Dictator Game (DG) and the Prisoners' Dilemma Game (PD). Participants learned to classify their emotional experiences on the arousal, valence, and dominance dimensions using non-verbal pictorial representations. Our experiment comprised both a within-subject and a between-subject manipulation: each participant completed a control condition (C, no emotional exposure) as well as an emotion exposure condition (Emotions), but the type of exposure varied between subjects (certain exposure, or Emotions-E, or probabilistic exposure, or Emotions-P). In all conditions, participants complete a one-shot DG and PD. We find that emotion exposure increases other-regarding behavior under both Emotions-E and Emotion-P conditions in the DG and under Emotions-E only in PD. Further, we find that demand for emotion exposure is hardly driven by the outcome of the social interaction (or the actions selected). We also document how empathic concern influence other-regarding behavior and how reports of emotional experiences vary across treatments and with the different outcomes of social interactions. Our results highlight the integral role of emotion exposure in social decision making. Environments that facilitate the communication of emotional experiences increase pro-sociality and encourage cooperation

Two dimensional bulge disk decomposition

We propose a two dimensional galaxy fitting algorithm to extract parameters of the bulge, disk, and a central point source from broad band images of galaxies. We use a set of realistic galaxy parameters to construct a large number of model galaxy images which we then use as input to our galaxy fitting program to test it. We find that our approach recovers all structural parameters to a fair degree of accuracy. We elucidate our procedures by extracting parameters for 3 real galaxies -- NGC 661, NGC 1381, and NGC 1427.Comment: 23 pages, LaTeX, AASTEX macros used, 7 Postscript figures, submitted to Ap

Should We Keep Everything Forever? Determining Long-Term Value of Research Data

The University of Illinois at Urbana-Champaign's library-based Research Data Service (RDS) launched an institutional data repository called the Illinois Data Bank (IDB) in May 2016. The RDS makes a commitment to preserving and facilitating access to published research datasets for a minimum of five years after the date of publication in the Illinois Data Bank. The RDS has developed guidelines and processes for reviewing published datasets after their five-year commitment ends to determine whether to retain, deaccession, or dedicate more stewardship resources to datasets. In this poster, we will describe how the University of Illinois at Urbana-Champaign preservation review planning team drew upon appraisal and reappraisal theory and practices from the archives community to develop preservation review processes and guidelines for datasets published in the Illinois Data Bank.Ope