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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Is there too much benchmarking in asset management?
We propose a tractable model of asset management in which benchmarking arises endogenously, and analyze its welfare consequences. Fund managersâ 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
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
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 schemesâparticularly, Multi-Instance Learning and classical Machine Learning formulationsâto 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 âPrograma Redes-I3CE de investigacion en docencia universitaria del Instituto de Ciencias de la Educacion (REDES-I3CE-2020-5069)â of the University of Alicante. The third author is supported by grant APOSTD/2020/256 from âPrograma I+D+I de la Generalitat Valencianaâ
A systematic literature review on the utilization of extended operating room hours to reduce surgical backlogs
This article is part of the Research Topic âHealth 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
Deep learning models can be fooled by small -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 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
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
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
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
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 firmâa 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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