Spectres of Law & Economics

There are spectres haunting law and economics - the spectres of G.W.F. Hegel and Jacques Lacan. This is one of the central theses of Professor Jeanne L. Schroeder\u27s challenging new book: The Triumph of Venus, the Erotics of the Market ( Triumph of Venus ). Schroeder uses insights inspired by the teachings of Hegel and the French psychoanalyst, Lacan, to critique some basic assumptiosn made by scholars who use economic ideas to investigate the law and legal institutions - the law and economics ( L&E ) practitioners. The book devotes much space to criticism of Judge Posner\u27s vision of law, using it as a proxy for L&E scholarship generally. Professor Schroeder succinctly states her basic problem with L&E: In recent years, the study of markets in American jurisprudence has been expropriated by the self-styled law-and-economics movement, the dominant discourse of private law in America\u27s most elite law schools. One of its appeals is that it gives an aura of scientific certainty and objectivity to legal analysis and normative policymaking. Despite its claim to scientific status, however, this scholarship is almost entirely devoid of methodological discussion and internal criticism, as though these matters were uncontroversial. (pp. 1-2

Measuring praise and criticism: Inference of semantic orientation from association

The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This paper introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words

Assessing the assignation of public subsidies: Do the experts choose the most efficient R&D projects?

The implementation of public programs to support business R&D projects requires the establishment of a selection process. This selection process faces various difficulties, which include the measurement of the impact of the R&D projects as well as selection process optimization among projects with multiple, and sometimes incomparable, performance indicators. To this end, public agencies generally use the peer review method,which, while presenting some advantages, also demonstrates significant drawbacks. Private firms, on the other hand, tend toward more quantitative methods, such as Data Envelopment Analysis (DEA), in their pursuit of R&D investment optimization. In this paper, the performance of a public agency peer review method of project selection is compared with an alternative DEA method.peer review, dea, subsidies, r&d

Evaluating competing theories via a common language of qualitative verdicts

Kuhn (The essential tension—Selected studies in scientific tradition and change, 1977) claimed that several algorithms can be defended to select the best theory based on epistemic values such as simplicity, accuracy, and fruitfulness. In a recent paper, Okasha (Mind 129(477):83–115, 2011) argued that no theory choice algorithm exists which satisfies a set of intuitively compelling conditions that Arrow (Social choice and individual values, 1963) had proposed for a consistent aggregation of individual preference orderings. In this paper, we put forward a solution to avoid this impossibility result. Based on previous work by Gaertner and Xu (Mathematical Social Sciences 63:193–196, 2012), we suggest to view the theory choice problem in a cardinal context and to use a general scoring function defined over a set of qualitative verdicts for every epistemic value. This aggregation method yields a complete and transitive ranking and the rule satisfies all Arrovian conditions appropriately reformulated within a cardinal setting. We also propose methods that capture the aggregation across different scientists

PROMETHEE-SAPEVO-M1 a Hybrid Approach Based on Ordinal and Cardinal Inputs: Multi-Criteria Evaluation of Helicopters to Support Brazilian Navy Operations

This paper presents a new approach based on Multi-Criteria Decision Analysis (MCDA), named PROMETHEE-SAPEVO-M1, through its implementation and feasibility related to the decision-making process regarding the evaluation of helicopters of attack of the Brazilian Navy. The proposed methodology aims to present an integration of ordinal evaluation into the cardinal procedure from the PROMETHEE method, enabling to perform qualitative and quantitative data and generate the criteria weights by pairwise evaluation, transparently. The modeling provides three models of preference analysis, as partial, complete, and outranking by intervals, along with an intra-criterion analysis by veto threshold, enabling the analysis of the performance of an alternative in a specific criterion. As a demonstration of the application, is carried out a case study by the PROMETHEE-SAPEVO-M1 web platform, addressing a strategic analysis of attack helicopters to be acquired by the Brazilian Navy, from the need to be evaluating multiple specifications with different levels of importance within the context problem. The modeling implementation in the case study is made in detail, first performing the alternatives in each criterion and then presenting the results by three different models of preference analysis, along with the intra-criterion analysis and a rank reversal procedure. Moreover, is realized a comparison analysis to the PROMETHEE method, exploring the main features of the PROMETHEE-SAPEVO-M1. Moreover, a section of discussion is presented, exposing some features and main points of the proposal. Therefore, this paper provides a valuable contribution to academia and society since it represents the application of an MCDA method in the state of the art, contributing to the decision-making resolution of the most diverse real problems.This research was funded by Centre for Research & Development in Mechanical Engineering (CIDEM), School of Engineering of Porto (ISEP), Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto, Portugal.info:eu-repo/semantics/publishedVersio

Some Issues in the Calculation of Batting Averages: Ranking (and Re-Ranking) the Top 50 Batsmen in Test Cricket, 1877-2006

Batsmen in cricket are invariably ranked according to their batting average. Such a ranking suffers from two defects. First, it does not take into account the consistency of scores across innings: a batsman might have a high career average but with low scores interspersed with high scores; another might have a lower average but with much less variation in his scores. Second, it pays no attention to the “value” of the player’s runs to the team: arguably, a century, when the total score is 600, has less value compared to a half-century in an innings total of, say, 200. The purpose of this paper is to suggest new ways of computing batting averages which, by addressing these deficiencies, complement the existing method and present a more complete picture of batsmen’s performance. Based on these “new” averages, the paper offers a “new” ranking of the top 50 batsmen in the history of Test Cricket.

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201