24,556 research outputs found

    Is there too much benchmarking in asset management?

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    We propose a tractable model of asset management in which benchmarking arises endogenously, and analyze its welfare consequences. Fund managers’ portfolios are not contractible and they incur private costs in running them. Incentive contracts for fund managers create a pecuniary externality through their effect on asset prices. Benchmarking inflates asset prices and creates crowded trades. The crowding reduces the effectiveness of benchmarking in incentive contracts for others, which fund investors fail to account for. A social planner, recognizing the crowding, opts for contracts with less benchmarking and less incentive provision. The planner also delivers lower asset management costs

    Corporate Social Responsibility: the institutionalization of ESG

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    Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective

    Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence

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    Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes—particularly, Multi-Instance Learning and classical Machine Learning formulations—to model student behaviour. Besides, Explainable Artificial Intelligence is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2,500 submissions from roughly 90 different students from a programming-related course in a Computer Science degree. The results obtained validate the proposal: the model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioural pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.This work has been partially funded by the “Programa Redes-I3CE de investigacion en docencia universitaria del Instituto de Ciencias de la Educacion (REDES-I3CE-2020-5069)” of the University of Alicante. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+I de la Generalitat Valenciana”

    A systematic literature review on the utilization of extended operating room hours to reduce surgical backlogs

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    This article is part of the Research Topic ‘Health Systems Recovery in the Context of COVID-19 and Protracted Conflict'IntroductionHospital managers address elective patient surgical backlogs with different strategies: increasing installed capacity, managing demand and improving efficiency. Recently, and particularly since the COVID-19 elective surgery suspension, extended operating room hours has been used to reduce waiting lists by taking advantage of empty operating rooms and existing surgical teams.MethodsTwo research questions are raised: (1) which are the scientific literature's insights related to the use of extended operating room hours to help reduce surgery backlogs? and (2) provided that a hospital decides to extend its operating room opening time, what are the main challenges and the key aspects to consider in the design and implementation of policies to manage extended operating room hours? A systematic review on Web of Science database was conducted to gather existing literature, published from January 2012 to December 2021, regarding strategies to reduce waiting lists using empty operating rooms outside regular working hours.ResultsA total of 12 papers were selected as relevant to address the two research questions. Results were organized according to their main features, namely setting, type of strategy, methodology, and how human resources are handled.DiscussionThe review suggests that extended operating room hours might be problematic if current staff is used and that a careful choice of patients should be made. However, its potential to reduce waiting times and its implications are discussed only superficially. Therefore, we analyze the implications of extending operating room hours from four different perspectives (management, staff, patients, and strategy deployment) and define some recommendations for policy makers and healthcare managers when implementing it in practice

    Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack

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    Deep learning models can be fooled by small lpl_p-norm adversarial perturbations and natural perturbations in terms of attributes. Although the robustness against each perturbation has been explored, it remains a challenge to address the robustness against joint perturbations effectively. In this paper, we study the robustness of deep learning models against joint perturbations by proposing a novel attack mechanism named Semantic-Preserving Adversarial (SPA) attack, which can then be used to enhance adversarial training. Specifically, we introduce an attribute manipulator to generate natural and human-comprehensible perturbations and a noise generator to generate diverse adversarial noises. Based on such combined noises, we optimize both the attribute value and the diversity variable to generate jointly-perturbed samples. For robust training, we adversarially train the deep learning model against the generated joint perturbations. Empirical results on four benchmarks show that the SPA attack causes a larger performance decline with small l∞l_{\infty} norm-ball constraints compared to existing approaches. Furthermore, our SPA-enhanced training outperforms existing defense methods against such joint perturbations.Comment: Paper accepted by the 2023 International Joint Conference on Neural Networks (IJCNN 2023

    Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model

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    We consider dynamic pricing strategies in a streamed longitudinal data set-up where the objective is to maximize, over time, the cumulative profit across a large number of customer segments. We consider a dynamic probit model with the consumers' preferences as well as price sensitivity varying over time. Building on the well-known finding that consumers sharing similar characteristics act in similar ways, we consider a global shrinkage structure, which assumes that the consumers' preferences across the different segments can be well approximated by a spatial autoregressive (SAR) model. In such a streamed longitudinal set-up, we measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. We propose a pricing policy based on penalized stochastic gradient descent (PSGD) and explicitly characterize its regret as functions of time, the temporal variability in the model parameters as well as the strength of the auto-correlation network structure spanning the varied customer segments. Our regret analysis results not only demonstrate asymptotic optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information as policies based on unshrunken models are highly sub-optimal in the aforementioned set-up.Comment: 34 pages, 5 figure

    Dual dynamic programming for stochastic programs over an infinite horizon

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    We consider a dual dynamic programming algorithm for solving stochastic programs over an infinite horizon. We show non-asymptotic convergence results when using an explorative strategy, and we then enhance this result by reducing the dependence of the effective planning horizon from quadratic to linear. This improvement is achieved by combining the forward and backward phases from dual dynamic programming into a single iteration. We then apply our algorithms to a class of problems called hierarchical stationary stochastic programs, where the cost function is a stochastic multi-stage program. The hierarchical program can model problems with a hierarchy of decision-making, e.g., how long-term decisions influence day-to-day operations. We show that when the subproblems are solved inexactly via a dynamic stochastic approximation-type method, the resulting hierarchical dual dynamic programming can find approximately optimal solutions in finite time. Preliminary numerical results show the practical benefits of using the explorative strategy for solving the Brazilian hydro-thermal planning problem and economic dispatch, as well as the potential to exploit parallel computing.Comment: 45 pages. New experiments for hierarchical problem and writing update

    KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction

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    The political stance prediction for news articles has been widely studied to mitigate the echo chamber effect -- people fall into their thoughts and reinforce their pre-existing beliefs. The previous works for the political stance problem focus on (1) identifying political factors that could reflect the political stance of a news article and (2) capturing those factors effectively. Despite their empirical successes, they are not sufficiently justified in terms of how effective their identified factors are in the political stance prediction. Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance. Based on this observation, we propose a novel knowledge-aware approach to political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) to learn the relationships among words and sentences in three different levels and (2) knowledge encoding (KE) to incorporate external knowledge for real-world entities into the process of political stance prediction. Also, to take into account the subtle and important difference between opposite political stances, we build two independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by ourselves and learn to fuse the different political knowledge. Through extensive evaluations on three real-world datasets, we demonstrate the superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3) effectiveness.Comment: 12 pages, 5 figures, 10 tables, the Web Conference 2023 (WWW

    Setting contextual conditions to resolve grand challenges through responsible innovation:A comparative patent analysis in the circular economy

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    Copyright © 2023 The Authors. This article draws on responsible innovation (RI) undertaken by hybrid organizations, institutional rigidity, and national innovation systems (NISs) to assess and contextualize the innovation performance of for-profit firms seeking to resolve grand challenges (GCs). The extant research on RI lacks the theoretical underpinnings to profile the unique characteristics of RI firms and the contextual conditions behind the resolution of GCs through RI. This study aims to fill this important gap by focusing on a specific type of RI firm—a firm seeking to reduce climate change through implementation of a circular economy model. By studying a multi-country sample of 1153 manufacturing firms, we implemented propensity score matching (PSM) and the Heckman selection model to compare the patent productivity of RI and non-RI firms. Our evidence demonstrates that RI firms display lower likelihood of patenting and lower patent productivity than non-RI firms when they do engage in patenting. Furthermore, we found that a stronger national R&D environment can be conducive to aligning public interests and private incentives by enabling RI firms to enhance their patent productivity. Additionally, RI firms in industries with lower levels of technological complexity capture more value from improvements in R&D environments than RI firms in industries with higher levels of technological complexity. Our argument as a whole contributes to the GC and RI literature streams by considering both the innovation barriers faced by RI-oriented firms and the macro/industry boundary conditions that enable such organizations to overcome them.Governments of Spain and Andalusia (Research Project A-SEJ-196-UGR20); Schoeller Foundation; Taishan Scholar Program of Shandong Province
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