Search

Purpose: This study aimed to examine personal and professional factors in determining the research activity and research culture among dental teaching faculty members in Hyderabad City, Telangana. Materials and Method: Two dental schools with ongoing dental undergraduate and postgraduate programmes were included in the study. The Research and Development (R&D) Culture Index Questionnaire comprising 16 items was employed for this study. Also, self‑reported research activity undertaken in the past twelve months was recorded. The association of demographic variables and professionally related variables with the perception of R&D culture and participation in research activities was assessed using the Chi‑square test. A P value was set at 0.05. Results: Of the one‑hundred and fifteen faculty members who participated in the study, seventy‑four (64.4%) worked in the government sector and forty‑one (35.6%) in privately funded dental college. The mean age of the study population was 34.11 + 7.13 years. The majority of them presented a paper or poster at the conference (66.1%; 76) followed by publishing paper in peer‑review journal (42.6%; 49) and grant application for research (6.1%; 7), and authoring of a textbook was undertaken by only 2.6% of the study population. Males (61.1%; P = 0.04) and those employed in private dental college (65.8%; P = 0.02) had significantly high levels of overall perception than their respective counterparts. Conclusion: Our study concludes that this study population has a positive perception (50.32 + 6.95) towards research with the majority of them having done a paper or poster publication (66.1%) in the last one year. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has posed an unprecedented challenge to humanity and science. On the one side, public and private incentives have been put in place to promptly allocate resources toward research areas strictly related to the COVID-19 emergency. However, research in many fields not directly related to the pandemic has been displaced. In this paper, we assess the impact of COVID-19 on world scientific production in the life sciences and find indications that the usage of medical subject headings (MeSH) has changed following the outbreak. We estimate through a difference-in-differences approach the impact of the start of the COVID-19 pandemic on scientific production using the PubMed database (3.6 Million research papers). We find that COVID-19-related MeSH terms have experienced a 6.5 fold increase in output on average, while publications on unrelated MeSH terms dropped by 10 to 12%. The publication weighted impact has an even more pronounced negative effect (-16% to -19%). Moreover, COVID-19 has displaced clinical trial publications (-24%) and diverted grants from research areas not closely related to COVID-19. Note that since COVID-19 publications may have been fast-tracked, the sudden surge in COVID-19 publications might be driven by editorial policy. [ABSTRACT FROM AUTHOR]

Industrial and research activities in the space sector involve a heterogeneous group of actors such as private and public firms, universities, research institutes and space agencies, collaborating to different stages of missions' realization. Such collaborations are associated to the creation of spillover effects, proxied in different ways, such as the number of patents recorded, post‐graduate students involved, spin‐off activities and co‐authored papers. The aim of this paper is to investigate the impact of the collaboration between universities /research institutes and the Italian Space Agency (ASI). To this aim, we focus on papers where the authors acknowledge grants received by ASI. We use data on publications and citations to capture the association between ASI funding of a large number of scientific articles published in the period 1989–2017 and the quality of research, proxied by the number of citations received. By performing parametric estimates with multiple levels of fixed effects (year, author and coauthors fixed effects), we find that articles mentioning ASI in the funding information are associated to a higher citation impact with respect to articles not financially supported by ASI. Such result suggests a positive impact on the scientific community of public funds granted to universities and research institutes. [ABSTRACT FROM AUTHOR]

This paper investigates the return on investment (ROI) paradigm from fundamental educational research grant funding perspectives. The researchers conducted a wide-ranging literature search regarding the educational research grant funding ROI from public policy and economics viewpoints. The preferred reporting items for systematic reviews and metaanalyses (PRISMA) were utilized to identify and analyze research articles and original reports related to ROI from educational research grant funding. The research data were screened through a literature review based on the inclusion criteria, namely study focusing on return on investment of educational research funding and study published between 2001 and 2021 (as of December 2021). This study found evidence that ROI calculation from fundamental educational research grants is not straightforward and varies. Besides, most available research focuses on research impact rather than economic and intrinsic research value. Based on the compiled literature on research-related performance attributes, this study identified three distinct deliverables of educational research funding: tangible output, intangible output, and research outcome. The present research proposes more robust and reliable methods for measuring the ROI of fundamental educational research impact, potentially generating a much-inform decision-making and resource allocation in educational research grant funding. [ABSTRACT FROM AUTHOR]

Lake Taal is one of the most significant lake ecosystems in tropical East Asia. In 2011, a review paper consolidated all known biodiversity and limno-ecological studies conducted in Lake Taal from the beginning of the 20th century all the way to the late 2000s. It highlighted that insufficient funding, inadequate long-term monitoring data, and the publication culture of that time impeded having a comprehensive understanding of the lake. Since then, much research work has been done in Lake Taal, especially during the past decade. This review paper provides an analysis and summary of the research work conducted in Lake Taal over the past 10 years. The recent decade has seen a steady increase in scientific interest in Lake Taal made evident by more scientists and institutions working on topics involving the lake. Research grants, collaborations, and publications have also significantly increased during the past 10 years. This has led to an improved understanding of the lake's biodiversity, ecology, and impacts of intensive aquaculture and the introduction of non-native species. Taken together, this paper presents a better understanding of Lake Taal, which can be utilized for the design and improvement of conservation, preservation, and management programs for this multi-use freshwater system. [ABSTRACT FROM AUTHOR]

In a wide variety of applications, including healthcare, bidding in first price auctions, digital recommendations, and online education, it can be beneficial to learn a policy that assigns treatments to individuals based on their characteristics. The growing policy-learning literature focuses on settings in which policies are learned from historical data in which the treatment assignment rule is fixed throughout the data-collection period. However, adaptive data collection is becoming more common in practice from two primary sources: (1) data collected from adaptive experiments that are designed to improve inferential efficiency and (2) data collected from production systems that progressively evolve an operational policy to improve performance over time (e.g., contextual bandits). Yet adaptivity complicates the problem of learning an optimal policy ex post for two reasons: first, samples are dependent and, second, an adaptive assignment rule may not assign each treatment to each type of individual sufficiently often. In this paper, we address these challenges. We propose an algorithm based on generalized augmented inverse propensity weighted (AIPW) estimators, which nonuniformly reweight the elements of a standard AIPW estimator to control worst case estimation variance. We establish a finite-sample regret upper bound for our algorithm and complement it with a regret lower bound that quantifies the fundamental difficulty of policy learning with adaptive data. When equipped with the best weighting scheme, our algorithm achieves minimax rate-optimal regret guarantees even with diminishing exploration. Finally, we demonstrate our algorithm's effectiveness using both synthetic data and public benchmark data sets. This paper was accepted by Hamid Nazerzadeh, data science. Funding: This work is supported by the National Science Foundation [Grant CCF-2106508]. R. Zhan was supported by Golub Capital and the Michael Yao and Sara Keying Dai AI and Digital Technology Fund. Z. Ren was supported by the Office of Naval Research [Grant N00014-20-1-2337]. S. Athey was supported by the Office of Naval Research [Grant N00014-19-1-2468]. Z. Zhou is generously supported by the New York University's 2022–2023 Center for Global Economy and Business faculty research grant and the Digital Twin research grant from Bain & Company. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4921. [ABSTRACT FROM AUTHOR]

This paper shows that consumers prefer organic listings in the top-ranked positions to sponsored listings of the same product/position in an online marketplace. Sponsored product listings on online marketplaces are third-party sellers' ads blended in organic product listings. This paper investigates a seller's managerial questions: whether a sponsored listing outperforms an organic listing and how the performance varies by positions. Our large-scale field study on a mobile app with experimental and natural variation finds that consumers prefer organic listings in the top-ranked positions to sponsored listings of the same product/position. Consumers become indifferent between sponsored and organic listings in the lower-ranked positions. A mechanism check suggests that top-ranked organic listings are perceived as more credible than seller-subsidized sponsored listings. Despite consumers' preference for organic listings, a simulation analysis shows that an advertising seller may benefit financially if a lower-ranked organic listing can be replaced with a top-ranked sponsored listing. History: Olivier Toubia served as the senior editor. Funding: J. Shi acknowledges financial support from INFORMS Society for Marketing Science Doctoral Dissertation Early Stage Research Grants. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2021.0293. [ABSTRACT FROM AUTHOR]

One major source of exhaustion for researchers is the redundant paperwork of three different documents—research papers, ethics review applications, and research grant applications—for the same research plan. This is a wasteful and redundant process for researchers, and it has a more direct impact on the career development of early-career researchers. Here, we propose a trinity review system based on Registered Reports that integrates scientific, ethics, and research funding reviews. In our proposed trinity review system, scientific and ethics reviews are undertaken concurrently for a research protocol before running the study. After the protocol is approved in principle through these review processes, a funding review will take place, and the researchers will begin their research. Following the experiments or surveys, the scientific review will be conducted on a completed version of the paper again, including the results and discussions (i.e., the full paper), and the full paper will be published once it has passed the second review. This paper provides the brief process of the trinity review system and discusses the need for and benefits of the proposed system. Although the trinity review system only applies to a few appropriate disciplines, it helps improve reproducibility and integrity. [ABSTRACT FROM AUTHOR]

Modern scientific work, including writing papers and submitting research grant proposals, increasingly involves researchers from different institutions. In grant collaborations, it is known that institutions involved in many collaborations tend to densely collaborate with each other, forming rich clubs. Here we investigate higher-order rich-club phenomena in networks of collaborative research grants among institutions and their associations with research impact. Using publicly available data from the National Science Foundation in the US, we construct a bipartite network of institutions and collaborative grants, which distinguishes among the collaboration with different numbers of institutions. By extending the concept and algorithms of the rich club for dyadic networks to the case of bipartite networks, we find rich clubs both in the entire bipartite network and the bipartite subnetwork induced by the collaborative grants involving a given number of institutions up to five. We also find that the collaborative grants within rich clubs tend to be more impactful in a per-dollar sense than the control. Our results highlight advantages of collaborative grants among the institutions in the rich clubs. [ABSTRACT FROM AUTHOR]

The use of social media for the collaboration of academics has been increasing in recent years. However, there are no reported studies on using Messenger as a collaborative platform to write and publish journal articles and apply for research and development grants. We use an auto-ethnography to reflect on our experiences over the last 3 years, using Messenger as our medium for our ongoing collaborative research activities. Our results highlight the benefits and challenges of using social media for this engagement. The capabilities of Messenger, as opposed to traditional correspondence through email, have paved our preference to use this platform. We can engage in dynamic collaboration and focussed discussion with less formal communication conventions through Messenger. In addition, the extra features, including easy phone calls, sending links, resources and screenshots, and using emojis and stickers for more socially cohesive interactions, are valued features of Messenger. We used the activity theory to highlight the interrelationships of factors (i.e., personal, social-emotional, structural, technological, and organisational) contributing to the success of collaborative academic activities, including the successful publication of journal articles and securing research and development grants. The findings of our study significantly contribute to understanding how social media can be effectively used for academic engagement. [ABSTRACT FROM AUTHOR]

This paper studies the impact of international business coaching via virtual collaboration technology on the strategies and sales of emerging market entrepreneurs. This paper studies the impact of international business coaching via virtual collaboration technology on the strategies and sales of emerging market entrepreneurs. It sheds light on three novel research questions. (1) What is the effect of virtual business coaching on firm sales? (2) What is the mechanism through which this effect occurs? Specifically, does virtual coaching stimulate shifts in marketing strategy? (3) Do entrepreneurs benefit more from virtual coaching when they are less strategic in their decision making? We conducted a randomized controlled field experiment with 930 entrepreneurs in Uganda to examine the impact of a virtual coaching intervention that connects management professionals in primarily advanced markets and entrepreneurs in emerging markets with the aim of improving business performance. The analysis finds a positive and significant main effect on firm sales; treatment entrepreneurs increase monthly sales by 27.6% on average. In addition, entrepreneurs who receive virtual coaching are 52.8% more likely to have shifted their marketing strategy in a new direction. Moreover, consistent with this mechanism of inducing strategic business changes, the results show that entrepreneurs who receive virtual coaching tend to do better when they (ex ante) lack strategic focus. These results have important implications for the development of marketing strategies by entrepreneurs and multinational managers, as well as for policy makers interested in improving the performance of small firms in emerging markets and beyond. History: Avi Goldfarb served as the senior editor. Funding: This research was supported by grants from: Private Enterprise Development in Low-Income Countries (PEDL); the London School of Economics Marshall Institute; the Deloitte Institute for Innovation and Entrepreneurship (DIIE); London Business School (LBS) and the LBS Leadership Institute (LI); the UK Department for International Development (DFID) and Economic and Social Research Council's (ESRC) Joint Growth Research Program; Chicago Booth School of Business: Social Enterprise Initiative (SEI), Initiative on Global Markets (IGM), and Kilts Center for Marketing; and Stanford University: the Graduate School of Business (GSB), the Stanford Institute for Innovation in Developing Economies (SEED), the Stanford Center on Global Poverty and Development (SCGPD), and the John A. and Cynthia Fry Gunn Faculty Scholar award. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mksc.2019.0121. [ABSTRACT FROM AUTHOR]

This paper studies the cost of capital effect of a major regulatory technology, or RegTech, event: the staggered implementation of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system of the Securities and Exchange Commission in the period from 1993 to 1996. This event represents a largely exogenous shock to corporate information dissemination technologies, resulting in a considerable reduction in information acquisition costs for investors. Using a difference-in-differences research design, we show that the cost of equity capital declines substantially after a firm switches from paper filing to mandatory electronic filing in EDGAR. The effect is stronger for small firms and firms with low analyst coverage and low institutional ownership. We identify three channels through which EDGAR affects a firm's cost of capital: the liquidity, risk-taking, and corporate governance channels. EDGAR implementation also improves a firm's investment efficiency significantly. We find evidence that the marginal value of a firm's capital investment and cash is higher during the post-EDGAR period. This paper was accepted by Suraj Srinivasan, accounting. Funding: C. Lin acknowledges financial support from the National Science Foundation of China [Grant 72192841] and the Research Grants Council of the Hong Kong Special Administration Region, China [Project HKU R7054-18 and T35/710/20R]. S. Lai acknowledges financial support from the Ministry of Science and Technology, Taiwan [Grants 107-2410-H-002-038-MY3 and 110-2410-H-002-079-MY2] and the E. Sun Academic Award. X. Ma acknowledges research support from the University of Macau [Grants SRG2019-00158-FBA and MYRG2020-00259-FBA]. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2022.4660. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the reasons some female mathematicians give to justify their choice to not work in academia after finishing their doctoral studies. Nine female mathematicians who finished a PhD in Sweden answered a written questionnaire. Through collective narrative analysis, two main tracks were identified. One narrative described the struggle with self-identity in a gendered structure which included implicit power, while the other was more positive about exposure to discrimination, and highlighted the desire to work with applied mathematics. Through deductive thematic analysis, the results show that the main obstacle raised was the difficulty of getting a job in academia after their doctoral studies, especially permanent positions, without support. Compared to previous research, the lack of family-oriented political policies was not considered a main problem. Instead, the reasons provided by the respondents are structural problems, such as access to post-doc positions, and the stress of having to get research grants, as well as cultural aspects within the structure, including implicit and explicit use of power. [ABSTRACT FROM AUTHOR]

Aeromonas spp are the most common devastating fish bacterial pathogens associated with a wide range of cultured freshwater fish resulting in mass mortality. The present study plots the Aeromonas research output in fish during 1998–2020 by means of the Web of Science (WoS) database. A total of 1944 publications were used for this investigation to evaluate the scientific productivity in this selected subject area. Global publication share, rank, and its development in terms of research and bibliometric indices such as overall citations, h-index, and average citation per paper were also analysed. China ranked first in terms of total publications among the most productive countries and accounted for 26.18% of the total publications. The network visualisation map revealed that the lead collaborating country was China; however, the collaboration between countries was found to be very less. Lokta's law was noticed not to be valid for Aeromonas research. The study showed that the funding granted to the research investigations and their research productivity in terms of total publications were correlated positively. The results of the current study displayed the trend of "Aeromonas infection in fish" related research investigations published during 1998–2020. This study also highlighted the focused research areas on this topic on which the future areas of research can be determined by fellow researchers in this specific field. This study also suggests enhancing the research efforts on this bacterial pathogen which has the potential to implicate huge economic loss to the fish farming population in the world. [ABSTRACT FROM AUTHOR]

Although previous studies show that the emergence of evaluation criteria for a new technology improves the life chances of well-performing firms, we theorize that consensus in such criteria among technology experts increases investments to all firms in the new sector. We provide a variety of supportive evidence for this claim. First, in an experiment with 80 Chinese investors (Study 1), we provide evidence of a causal relation between evaluation consensus and investments. We follow this with a second experiment with 412 U.S. participants (Study 2), showing that evaluation criteria consensus increases participants' propensity to view a firm as technologically competent and to expect others to favor investing in the firm. Analyses of longitudinal archival data on investment in artificial intelligence technology firms in the United States (Study 3a) and China (Study 3b) support the generalizability of our findings. By exploring the social-cognitive processes that link evaluation criteria consensus to investors' decisions to invest in firms in nascent technology fields, this paper advances the scholarly understanding of the microfoundations of the institutionalization processes in new market sectors. History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory. Funding: This work was supported by Cornell University (Innovation, Entrepreneurship, and Technology Theme Research Grant, Jeffrey S. Lehman Fund for Scholarly Exchange with China). Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1493. [ABSTRACT FROM AUTHOR]

This paper proposes and develops a new Newton-type algorithm to solve subdifferential inclusions defined by subgradients of extended real-valued prox-regular functions. The proposed algorithm is formulated in terms of the second order subdifferential of such functions that enjoy extensive calculus rules and can be efficiently computed for broad classes of extended real-valued functions. Based on this and on the metric regularity and subregularity properties of subgradient mappings, we establish verifiable conditions ensuring the well-posedness of the proposed algorithm and its local superlinear convergence. The obtained results are also new for the class of equations defined by continuously differentiable functions with Lipschitzian gradients ( C 1 , 1 functions), which is the underlying case of our consideration. The developed algorithms for prox-regular functions and their extension to a structured class of composite functions are formulated in terms of proximal mappings and forward–backward envelopes. Besides numerous illustrative examples and comparison with known algorithms for C 1 , 1 functions and generalized equations, the paper presents applications of the proposed algorithms to regularized least square problems arising in statistics, machine learning, and related disciplines. Funding: Research of P. D. Khanh is funded by Ho Chi Minh City University of Education Foundation for Science and Technology [Grant CS.2022.19.20TD]. Research of B. Mordukhovich and V. T. Phat was partly supported by the U.S. National Science Foundation [Grants DMS-1808978 and DMS-2204519]. The research of B. Mordukhovich was also supported by the Air Force Office of Scientific Research [Grant 15RT0462] and the Australian Research Council under Discovery Project DP-190100555. This work was supported by the Air Force Office of Scientific Research [Grant 15RT0462]. [ABSTRACT FROM AUTHOR]

Policy learning using historical observational data are an important problem that has widespread applications. Examples include selecting offers, prices, or advertisements for consumers; choosing bids in contextual first-price auctions; and selecting medication based on patients' characteristics. However, existing literature rests on the crucial assumption that the future environment where the learned policy will be deployed is the same as the past environment that has generated the data: an assumption that is often false or too coarse an approximation. In this paper, we lift this assumption and aim to learn a distributionally robust policy with incomplete observational data. We first present a policy evaluation procedure that allows us to assess how well the policy does under worst-case environment shift. We then establish a central limit theorem type guarantee for this proposed policy evaluation scheme. Leveraging this evaluation scheme, we further propose a novel learning algorithm that is able to learn a policy that is robust to adversarial perturbations and unknown covariate shifts with a performance guarantee based on the theory of uniform convergence. Finally, we empirically test the effectiveness of our proposed algorithm in synthetic datasets and demonstrate that it provides the robustness that is missing using standard policy learning algorithms. We conclude the paper by providing a comprehensive application of our methods in the context of a real-world voting data set. This paper was accepted by Hamid Nazerzadeh, data science. Funding: This work was supported by the National Science Foundation [Grant CCF-2106508] and the Air Force Office of Scientific Research [Award FA9550-20-1-0397]. Z. Zhou also gratefully acknowledges the JP Morgan AI Research Grant and the New York University's Center for Global Economy and Business faculty research grant for support on this work. Additional support is gratefully acknowledged from the National Science Foundation [Grants 1915967 and 2118199]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4678. [ABSTRACT FROM AUTHOR]

The Impact of the Prohibition of Trade-through In the paper titled "Does the Prohibition of Trade-Through Hurt Liquidity Demanders?" the authors explore the effects of the order protect rule (OPR) in the United States, which prohibits any trade-through in the stock market that does not execute at the best possible price among fast trading venues. The study found that, whereas trade-throughs may offer flexible trading strategies that can benefit liquidity demanders, the benefits are insignificant for most cases, especially for small trades and stocks with fast resilience. The paper suggests that the current separate regulations for fast and slow venues may be extended to differentiate stocks with fast and slow resilience speeds. The results are supported by a case study using the data from the Australian Securities Exchange. Overall, this study supports the regulation of the OPR. The order protect rule (OPR) in the United States generally prohibits any trade-through, that is, a market order that is not executed at the best possible price among fast (electronic and automated) trading venues. By deriving upper and lower bounds for the difference in the execution costs in a dynamic model, we find that, although trade-through allows for flexible trading strategies and may benefit the liquidity demander, the benefit is insignificant in most cases, especially for small trades and stocks with fast resilience. Therefore, considering other benefits of the OPR studied in the literature, this study supports the regulation and suggests that the current separate regulations for fast and slow venues may be extended to differentiate stocks with fast and slow resilience speeds. Funding: P. Gao received financial support from the National Natural Science Foundation of China [Grants 72201234 and 72192805] and the Research Grant Council of Hong Kong (the Collaborative Research Fund) [Grant C6032-21G]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2454. [ABSTRACT FROM AUTHOR]

The article presents announcements from the Association for the Sociology of Religion (ASR), including an annual meeting to be held by the ASR from August 19-21, 2023 and the nominations being sought for its Joseph H. Fichter Research Grants and the Robert J. McNamara Student Paper Award.

This paper assesses the research literature on OFC in LMIC over the last decade across various geographical settings and project focus of the indexed literature. A scoping review of the indexed literature was performed using a set of predefined keywords. The articles were filtered by a ten‐year time frame (2010‐2019) and a strict inclusive‐exclusive criterion. Two authors screened the titles/abstracts/full text of the final included papers and input the desired data (year of publication, type of publication, geographical country/region and project focus to a coded spreadsheet). Six hundred and twenty publications were inventoried from the indexed literature on OFC in LMIC settings over the 10‐year period. Five hundred and eighty‐three derived from single LMIC countries and 37 from multi‐settings. More than half of the articles were reported from Asia (57%), then from Americas (22.8%), Africa (15.4%) and the rest from cross‐regional, Europe and Oceania (4.9%). The top 3 LMIC contributors towards OFC publications were China (21.5%), Brazil (13.1%) and India (11.6%). The most discussed OFC project themes were prevalence, surgical repair, aetiology and genetics. This study helps OFC researchers, humanitarian missions and research grant funders to identify gaps in the literature on issues impacting on children and adults born with OFC, in which issues were subjected to research and which were less explored in which LMIC regions. In addition, this study offers recommendations for established OFC researchers and international research bodies to identify areas of deficiency in the literature and what information is required to support LMIC governments achieve SDGs by 2030. [ABSTRACT FROM AUTHOR]

This paper points out the error in the HJB equation in a previous published paper and presents the correct HJB equation. We show that the Hamilton-Jacobi-Bellman (HJB) equation for the salesperson's problem is not correct because of the effect of a positive discount factor for the infinite-horizon risk-sensitive control. As a result, their analyses and insights are incorrect. We present the correct HJB equation for the salesperson's problem, which is a partial differential equation. Thus, further investigation on the correct solutions is needed to better understand the problem. History: K. Sudhir served as the senior editor for this article. Funding: X. Kong acknowledges financial support from the Natural Science Foundation of China [Grants 72101243 and 72091212/72091210] and the Fundamental Research Funds for the Central Universities [Grant WK2040000033]. The research of Y. Yu is partially supported by the Hong Kong Research Grants General Proposal Fund [Grant 9043432], CityU [Grants 7005793 and 7005591], and the Laboratory for AI-Powered Financial Technologies. [ABSTRACT FROM AUTHOR]

The value at risk (VaR) and the conditional value at risk (CVaR) are two popular risk measures to hedge against the uncertainty of data. In this paper, we provide a computational toolbox for solving high-dimensional sparse linear regression problems under either VaR or CVaR measures, the former being nonconvex and the latter convex. Unlike the empirical risk (neutral) minimization models in which the overall losses are decomposable across data, the aforementioned risk-sensitive models have nonseparable objective functions so that typical first order algorithms are not easy to scale. We address this scaling issue by adopting a semismooth Newton-based proximal augmented Lagrangian method of the convex CVaR linear regression problem. The matrix structures of the Newton systems are carefully explored to reduce the computational cost per iteration. The method is further embedded in a majorization–minimization algorithm as a subroutine to tackle the nonconvex VaR-based regression problem. We also discuss an adaptive sieving strategy to iteratively guess and adjust the effective problem dimension, which is particularly useful when a solution path associated with a sequence of tuning parameters is needed. Extensive numerical experiments on both synthetic and real data demonstrate the effectiveness of our proposed methods. In particular, they are about 53 times faster than the commercial package Gurobi for the CVaR-based sparse linear regression with 4,265,669 features and 16,087 observations. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Funding: This work was supported in part by the NSF, the Division of Computing and Communication Foundations [Grant 2153352], the National Natural Science Foundation of China [Grant 12271187], and the Hong Kong Research Grant Council [Grant 15304019]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1282) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0012) at (http://dx.doi.org/10.5281/zenodo.7483279). [ABSTRACT FROM AUTHOR]

One of the most prevalent demand models in the revenue management literature is based on dividing the selling horizon into a number of time periods such that there is at most one customer arrival at each time period. This demand model is equivalent to using a discrete-time approximation to a Poisson process, but it has an important shortcoming. If the mean number of customer arrivals is large, then the coefficient of variation of the number of customer arrivals has to be small. In other words, large demand volume and large demand variability cannot coexist in this demand model. In this paper, we start with a revenue management model that incorporates general mean and variance for the number of customer arrivals. This revenue management model has a random selling horizon length, capturing the distribution of the number of customer arrivals. The question we seek to answer is the form of the fluid approximation that corresponds to this revenue management model. It is tempting to construct the fluid approximation by computing the expected consumption of the resource capacities in the constraints and the total expected revenue in the objective function through the distribution of the number of customer arrivals. We demonstrate that this answer is wrong in the sense that it yields a fluid approximation that is not asymptotically tight as the resource capacities get large. We give an alternative fluid approximation where perhaps surprisingly, the distribution of the number of customer arrivals does not play any role in the constraints. We show that this fluid approximation is asymptotically tight as the resource capacities get large. A numerical study also demonstrates that the policies driven by the latter fluid approximation perform substantially better, so there is practical value in getting the fluid approximation right under high-variance demand. This paper was accepted by Omar Besbes, revenue management and market analytics. Funding: The work of the O. El Housni and H. Topaloglu was supported by a seed grant from Urban Tech research hub at Cornell Tech. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4769. [ABSTRACT FROM AUTHOR]

Considering a rejection rate of 80–90%, the preparation of a research grant is often considered a daunting task since it is resource intensive and there is no guarantee of success, even for seasoned researchers. This commentary provides a summary of the key points a researcher needs to consider when writing a research grant proposal, outlining: (1) how to conceptualise the research idea; (2) how to find the right funding call; (3) the importance of planning; (4) how to write; (5) what to write, and (6) key questions for reflection during preparation. It attempts to explain the difficulties associated with finding calls in clinical pharmacy and advanced pharmacy practice, and how to overcome them. The commentary aims to assist all pharmacy practice and health services research colleagues new to the grant application process, as well as experienced researchers striving to improve their grant review scores. The guidance in this paper is part of ESCP's commitment to stimulate "innovative and high-quality research in all areas of clinical pharmacy". [ABSTRACT FROM AUTHOR]

The Distributed System of Scientific Collections (DiSSCo) is a new world-class Research Infrastructure (RI) for Natural Science Collections. The DiSSCo RI aims to create a new business model for one European collection that digitally unifies all European natural science assets under common access, curation, policies and practices that ensure that all the data is easily Findable, Accessible, Interoperable and Reusable (FAIR principles). DiSSCo represents the largest ever formal agreement between natural history museums, botanic gardens and collection-holding institutions in the world. DiSSCo entered the European Roadmap for Research Infrastructures in 2018 and launched its main preparatory phase project (DiSSCo Prepare) in 2020. DiSSCo Prepare is the primary vehicle through which DiSSCo reaches the overall maturity necessary for its construction and eventual operation. DiSSCo Prepare raises DiSSCo's implementation readiness level (IRL) across the five dimensions: technical, scientific, data, organisational and financial. Each dimension of implementation readiness is separately addressed by specific Work Packages (WP) with distinct targets, actions and tasks that will deliver DiSSCo's Construction Masterplan. This comprehensive and integrated Masterplan will be the product of the outputs of all of its content related tasks and will be the project's final output. It will serve as the blueprint for construction of the DiSSCo RI, including establishing it as a legal entity. DiSSCo Prepare builds on the successful completion of DiSSCo's design study, ICEDIG and the outcomes of other DiSSCo-linked projects such as SYNTHESYS+ and MOBILISE. This paper is an abridged version of the original DiSSCo Prepare grant proposal. It contains the overarching scientific case for DiSSCo Prepare, alongside a description of our major activities. [ABSTRACT FROM AUTHOR]

Incentivizing reviewers to write product reviews is a widespread yet controversial practice. Whereas outright fake reviews are clearly unacceptable and should be removed from any review platform, reviews contributed by incentivized consumers with otherwise authentic product experiences fall in a gray area. This paper offers a fresh perspective for us to understand conceptually the relationship between incentivized reviews and its two counterparts (i.e., organic reviews and advertisements) on the two ends of the spectrum, and it studies whether incentivized reviews are of higher text quality. The authors argue that incentivized reviewers may shift their "writing mode" from back stage to front stage and may also "compensate" for their reduced impartiality through better text quality. Drawing on recent advancements in computational linguistics, the authors demonstrate that incentivized reviews tend to have more coherent writing and cover more details, suggesting higher review text quality. Their finding highlights the often-overlooked value of incentivized reviews, which can complement an organic review system and also alleviate the cold-start problem for new products, thereby promoting healthy competition in the e-commerce era. For review platforms, the authors suggest they explicitly group and label incentivized reviews without their numerical ratings and separate them from organic reviews. In this way, we can take the best of both worlds. Incentivized reviews have become increasingly prevalent on product review sites such as Amazon. Whereas outright fake reviews are clearly unacceptable and should be removed from review platforms, reviews by incentivized consumers with otherwise authentic product experiences fall into a gray area. On the one hand, many critics and researchers have warned of their harm by pointing out their biased ratings. On the other hand, these reviews might complement organic reviews with review text of higher quality. The current paper studies whether incentivized reviews on Amazon are more coherent and offer richer detail. We use Amazon's platform-incentivized reviews, known as Vine reviews, for our primary sample and use seller-incentivized reviews for checking robustness. Estimations from a two-way fixed-effect model consistently show that incentivized reviews do compensate for their reduced impartiality through better text quality, measured by discourse coherence and level of relevant detail. This finding is further supported by a randomized experiment using Amazon Mechanical Turk. Hence, current literature findings on the poor quality of text of incentivized reviews, based on review length and lexical complexity, portray only an incomplete picture of incentivized reviews. Given that numerical ratings for products via incentivized reviews are likely biased whereas their text content is of high quality, a natural way to embrace incentivized reviews is to keep their text content, suppress their numerical ratings, and always highlight the label of "incentivized reviews." History: Juan Feng, Senior Editor; Jianqing Chen, Associate Editor. Funding: This work was supported by Singapore Ministry of Education Tier 1 Research Grant [Grant A-0008174-00-00]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1146. [ABSTRACT FROM AUTHOR]

Background: The Research Project Grant (R01) is the oldest grant mechanism used by the National Institutes of Health (NIH). Receiving an R01 award is often taken as a sign of scientific success. We presented normative data on multiple productivity and impact metrics for a more objective assessment of funded grants' scientific success. Methods: All initial R01 grants awarded by NIH in the year 2000 were prospectively followed and evaluated using the numbers of publications and citations, as well as the h-indices at the grant level. We examined the variability, time trends, and relations among these metrics to better understand the funded projects' cumulative output and impact. Results: In the 20 years since initial funding, 4451 R01 grants generated a total of 55,053 publications. These publications were cumulatively cited 3,705,553 times over 736,811 citation years. The median number of publications was 8 (25th, 75th percentiles 4, 17) per grant for the entire 20-year duration. The median number of citations and the median h-index were 441 (25th, 75th percentiles 156, 1061) and 7 (25th, 75th percentiles 4, 13) per grant, respectively. The time courses of publication, citation, and accumulation of h-index were highly variable among the awarded grants. Although the metrics were correlated within an award, they reflected the grant's success in different domains. Conclusion: Numbers of publications, citations, and h-indices vary greatly among funded R01 grants. When used together, these metrics provide a more complete picture of the productivity and long-term impact of a funded grant. [ABSTRACT FROM AUTHOR]

This paper investigates gender bias (if any) when teams are formed. We use data from the European Science Foundation to estimate if female scientists have the same opportunities as their male colleagues to join a team when applying for funds. To assess gender bias, we construct a control group of scientists with the competencies for being invited to join the team but who do not join. By comparing the proportion of female scientists in the control group with the one in the observed teams, we find a gender bias against female scientists. Exploring heterogeneity across teams, we find that gender bias is less pronounced in teams led by women, junior scientists, and in more recently formed teams. We also observe differences across disciplines. [ABSTRACT FROM AUTHOR]

We explore the consequences of limiting executive pay on voluntary executive turnover by exploiting a Chinese government policy restricting executive pay at a subset of firms. Affected firms experience an increase in voluntary executive turnover, with executives increasingly moving to firms not bound by the pay policy. Executives that leave are of higher quality as suggested by their stronger stock and accounting performance in the year prior to the turnover. Affected firms suffer firm value losses, especially when talented executives leave. Our findings demonstrate the importance of compensation contracts in retaining executives and suggest a need for regulatory caution, since mandated constraints aimed at trying to "fix" executive pay can hurt firm value and ultimately, hurt shareholders. This paper was accepted by Suraj Srinivasan, accounting. Funding: S. Silveri acknowledges research support in the form of a summer research grant from the Fogelman College of Business and Economics at the University of Memphis. This research support does not imply endorsement of the research results by either Fogelman College or the University of Memphis. K. Wang is grateful for the financial support provided by the Tsinghua University Initiative Scientific Research Program and the research project of the Institute for State-Owned Enterprises, Tsinghua University. L. Zhao is grateful for the financial support provided by the Asia Research Center in Nankai University [Project AS2206]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4812. [ABSTRACT FROM AUTHOR]

Recent financialization in commodity markets makes it easier for institutional investors to trade a portfolio of commodities via various commodity-indexed products. We present several pieces of novel causal evidence that daily exposure to such index trading results in price overshoots and reversals, as reflected in negative daily return autocorrelations, only among commodities in that index. This is because index trading propagates nonfundamental noise to all indexed commodities. We present direct evidence for such noise propagation using commodity news sentiment data. This paper was accepted by Bruno Biais, finance. Funding: Z. Da acknowledges financial support from the Beijing Outstanding Young Scientist Program [Grant BJJWZYJH01201910034034] and the 111 Project [Grant B20094]. K. Tang acknowledges financial support from the National Natural Science Foundation of China [Grants 71973075 and 72192802]. Y. Tao acknowledges financial support from the Start-up Research Grant of University of Macau [Grant SRG2022-00016-FSS]. L. Yang acknowledges the Social Sciences and Humanities Research Council of Canada for financial support [Grants 430-2018-00173 and 435-2021-0040]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4797. [ABSTRACT FROM AUTHOR]

The funder had no role in study design, data collection, software development, data analysis, data interpretation, writing of the report, or decision to submit the paper." The funder had no role in study design, data collection, software development, data analysis, data interpretation, writing of the report, or decision to submit the paper." now reads: "This work was supported by a 2-Year Research Grant of Pusan National University. [Extracted from the article]

Peer review is widely used throughout academia, most notably in the publication of journal articles and the allocation of research grants. Yet peer review has been subject to much criticism, including being slow, unreliable, subjective and potentially prone to bias. This paper contributes to this literature by investigating the consistency of peer reviews and the impact they have upon a high-stakes outcome (whether a research grant is funded). Analysing data from 4,000 social science grant proposals and 15,000 reviews, this paper illustrates how the peer review scores assigned by different reviewers have only low levels of consistency (a correlation between reviewer scores of only 0.2). Reviews provided by "nominated reviewers" (i.e. reviewers selected by the grant applicant) appear to be overly generous and do not correlate with the evaluations provided by independent reviewers. Yet a positive review from a nominated reviewer is strongly linked to whether a grant is awarded. Finally, a single negative peer review is shown to reduce the chances of a proposal being funding from around 55% to around 25% (even when it has otherwise been rated highly). [ABSTRACT FROM AUTHOR]

This paper investigates whether the polarization of political ideology extends to consumers' preferences, intentions, and purchases. In light of the widely discussed political divide and increasing societal polarization, we investigate in this paper whether the polarization of political ideology extends to consumers' preferences, intentions, and purchases. Using three different data sets—the publicly available social media data of over three million brand followerships of Twitter users, a YouGov brand-preference survey data set, and Nielsen scanner panel data—we assess the evolution of brand-preference polarization. We find that the apparent polarization in political ideologies after the election of Donald Trump in 2016 stretches further to the daily lives of consumers. We observe increased polarization in preferences, behavioral intentions, and actual purchase decisions for consumer brands. Consistent with compensatory consumption theory, we find that the increase in polarization following the election of Donald Trump was stronger for liberals relative to conservatives, and that this asymmetric polarization is driven by consumers' demand for "Democratic brands" rather than the supply of such brands. From a brand perspective, there is evidence that brands that took a political stance observed a shift in their customer base in terms of their customers' political affiliation. We provide publicly available (http://www.social-listening.org) access to the unique Twitter-based brand political affiliation scores. History: K. Sudhir served as the senior editor and Juanjuan Zhang served as associate editor for this article. This paper was accepted through the Marketing Science: Frontiers review process. Funding: The authors thank the Marketing Science Institute for the 2018–2020 Research Priorities Research Grant Competition grant and Bocconi University for the Junior Research Grant 2019–2020 that provided funding for this project. Supplemental Material: Data and the web appendix are available at https://doi.org/10.1287/mksc.2022.1408. [ABSTRACT FROM AUTHOR]

The paper develops a new dynamic choice modeling framework and applies it to examine owner decisions in P2P sharing markets. Peer-to-peer (P2P) sharing marketplaces enable sharing of idle resources. When a renter requests an owner's resource, the owner needs to decide whether to accept the request: accepting it helps the owner fill up the idle periods of the resource and generate a payoff but reduces the flexibility to serve a future request for a longer duration. This paper develops a framework to uncover the tradeoffs faced by owners on these platforms when making acceptance decisions, which can be used by owners to optimize their decisions and by platforms to improve their operations. The model explicitly accommodates two types of owners: some are attentive to the availability states of their cars and forward-looking, whereas others myopically make the acceptance decisions. Applying the model to unique data from a leading peer-to-peer car sharing platform in China, we obtain similar sizes of both types of owners and find that female, experienced, and younger owners are more likely to be strategic. The results also reveal the differentiated preferences of the two types of owners toward their renters. Building on model estimates, we calibrate the option value of each day in the future (i.e., the value of having the day available) for strategic owners and find it to first increase, then decrease. Two counterfactual analyses are conducted. The first analysis shows that if the platform imposes a minimum rental duration, strategic owners may become more reluctant to accept requests, even if the current availability state entails a higher expected payoff. The second analysis shows that with better understanding of its owners, the platform can greatly improve the matching efficiency by optimal (re)allocation of rental requests, a move that benefits almost all participants in the business. History: K. Sudhir served as the senior editor for this article. Funding: This work was supported by the Hong Kong Polytechnic University [Grant A0038957] and the National University of Singapore [Research Grant R-316-000-104-133]. Supplemental Material: Data and the online appendices are available at https://doi.org/10.1287/mksc.2022.1369. [ABSTRACT FROM AUTHOR]

Research funding organizations routinely encourage SMEs to collaborate with organizations of different institutional backgrounds, such as other firms, universities, or research organizations. These collaborations are supposed to involve SMEs in knowledge flows across boundaries and generate innovative solutions to complex problems. However, we know little about how the project partners determine the priorities of their joint research in the first place and how the institutional composition influences priority setting. In this paper, we study attention coordination among SMEs and their partners for the emergence of priorities in joint research grant proposals. Applying content analysis to 207 grant proposals of innovation consortia that have received funding in the EU's Horizon 2020 program, we find that increasingly diverse consortia shift attention away from technological novelty and market creation towards more consideration for the innovation ecosystem. Plain English Summary: SMEs in publicly funded research consortia often benefit from the diversity of partners but diversity also influences how partners write up the grant proposal. They put more emphasis on embeddedness in the ecosystem but less on technological novelty. SMEs often collaborate with organizations of different institutional backgrounds, such as other firms, universities, or research organizations, in publicly funded innovation projects. While these collaborations are often beneficial, SMEs need to coordinate with their project partners about how to set priorities in the grant proposal. Our analysis, which is based on the proposal texts of 207 EU-funded research consortia, reveals that increasingly diverse consortia put more emphasis on the embeddedness into an innovation ecosystem but less on achieving technological novelty. Our research has implications for SMEs that seek to collaborate for innovation and for public funding bodies that can better assess the likely innovation impact of a grant proposal. [ABSTRACT FROM AUTHOR]

Online matching platforms require new approaches to market design because firms can now control many aspects of the search and interaction process through various IT-enabled features. Although choice capacity—the number of candidates a user can view and select—is a key design feature of online matching platforms, its effect on engagement and matching outcomes remains unclear. We examine the effect of different choice capacities on market performance by conducting a randomized field experiment in collaboration with an online dating platform. Specifically, we design four treatment groups with different choice capacities in which users can only interact with other users in the same group and randomly assign the users to the treatment groups. We find that providing more choice capacity to male and female users has different effects on choice behaviors and matching outcomes. Although increasing the choice capacity of male users yields the highest engagement, increasing the choice capacity of female users is the most effective method to increase matching outcomes. We empirically demonstrate four mechanisms underlying the effectiveness of different choice capacity designs and generalize our findings by discussing how choice capacity can be designed to increase engagement and matching outcomes. Online matching platforms require new approaches to market design because firms can now control many aspects of the search and interaction process through various IT-enabled features. Although choice capacity—the number of candidates a user can view and select—is a key design feature of online matching platforms, its effect on engagement and matching outcomes remains unclear. We examine the effect of different choice capacities on the number of choices and matches made on a platform by conducting a randomized field experiment in collaboration with an online dating platform. Specifically, we (1) select users who are of a similar age and live in the same geographical location, (2) design four treatment groups with different choice capacities in which users can only interact with other users in the same group, and (3) randomly assign the users to the treatment groups. We find that providing more choice capacity to male and female users has different effects on choice behaviors and matching outcomes. Although increasing the choice capacity of male users yields the highest engagement, increasing the choice capacity of female users is the most effective method to increase matching outcomes. We posit and empirically demonstrate four mechanisms underlying the effectiveness of different choice capacity designs. Furthermore, we generalize our findings to other online matching platforms and discuss how choice capacity can be designed to increase engagement and matching outcomes. History: Ravi Bapna, Martin Bichler, Bob Day, and Wolfgang Ketter, Senior Editors; Thayer Morrill, Associate Editor. This paper has been accepted for the Information Systems Research Special Section on Market Design and Analytics. Funding: The work described in this paper was substantially supported by the Early Career Scheme grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. HKUST 26504318]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.1028. [ABSTRACT FROM AUTHOR]

Dr Aarts has advisory board / consultancy honoraria from Amgen, Bristol Myers Squibb, Novartis, MSD-Merck, Merck-Pfizer, Pierre Fabre, Sanofi, Astellas, Bayer. Prof vd Eertwegh has advisory relationships with Amgen, Bristol Myers Squibb, Roche, Novartis, MSD, Pierre Fabre, Sanofi, Pfizer, Ipsen, Merck and has received research study grants not related to this paper from Sanofi, Roche, Bristol Myers Squibb, Idera and TEVA and has received travel expenses from MSD Oncology, Roche, Pfizer and Sanofi and has received speaker honoraria from BMS and Novartis. Dr Suijkerbuijk has advisory relationships with Bristol Myers Squibb, Novartis, MSD, Pierre Fabre, Abbvie and received honoraria from Novartis, MSD and Roche. [Extracted from the article]

With the increasing reliance on competitive grants to fund research, we see a review system under pressure. While peer review has long been perceived as the cornerstone of self-governance in science, researchers have expressed distrust in the peer review procedures of funding agencies. This paper draws on literature pointing out ability, benevolence, and integrity as important for trustworthiness and explores the conditions under which researchers have confidence in grant review. Based on rich survey material, we find that researchers trust grant reviewers far less than they trust journal peer reviewers or their colleagues' ability to assess their research. Yet, scholars who have success with grant proposals or serve on grant review panels appear to have more trust in grant reviewers. We conclude that transparency and reviewers with field competencies are crucial for trust in grant review and discuss how this can be ensured. [ABSTRACT FROM AUTHOR]

We study the problem of dynamic batch learning in high-dimensional sparse linear contextual bandits, where a decision maker, under a given maximum-number-of-batch constraint and only able to observe rewards at the end of each batch, can dynamically decide how many individuals to include in the next batch (at the end of the current batch) and what personalized action-selection scheme to adopt within each batch. Such batch constraints are ubiquitous in a variety of practical contexts, including personalized product offerings in marketing and medical treatment selection in clinical trials. We characterize the fundamental learning limit in this problem via a regret lower bound and provide a matching upper bound (up to log factors), thus prescribing an optimal scheme for this problem. To the best of our knowledge, our work provides the first inroad into a theoretical understanding of dynamic batch learning in high-dimensional sparse linear contextual bandits. Notably, even a special case of our result—when no batch constraint is present—yields that the simple exploration-free algorithm using the LASSO estimator already achieves the minimax optimal O˜(s0T) regret bound (s0 is the sparsity parameter or an upper bound thereof and T is the learning horizon) for standard online learning in high-dimensional linear contextual bandits (for the no-margin case), a result that appears unknown in the emerging literature of high-dimensional contextual bandits. This paper was accepted by Baris Ata, stochastic models and simulation. Funding: This work is supported by the National Science Foundation [Grant CCF-2106508]. Z. Zhou gratefully acknowledges the Digital Twin research grant from Bain & Company and the New York University's 2022-2023 Center for Global Economy and Business faculty research grant for support on this work. Z. Ren was supported by the National Science Foundation [Grant OAC 1934578] and by the Discovery Innovation Fund for Biomedical Data Sciences. [ABSTRACT FROM AUTHOR]

In this paper, we consider online learning in generalized linear contextual bandits where rewards are not immediately observed. Instead, rewards are available to the decision maker only after some delay, which is unknown and stochastic. Such delayed feedback occurs in several active learning settings, including product recommendation, personalized medical treatment selection, bidding in first-price auctions, and bond trading in over-the-counter markets. We study the performance of two well-known algorithms adapted to this delayed setting: one based on upper confidence bounds and the other based on Thompson sampling. We describe modifications on how these two algorithms should be adapted to handle delays and give regret characterizations for both algorithms. To the best of our knowledge, our regret bounds provide the first theoretical characterizations in generalized linear contextual bandits with large delays. Our results contribute to the broad landscape of contextual bandits literature by establishing that both algorithms can be made to be robust to delays, thereby helping clarify and reaffirm the empirical success of these two algorithms, which are widely deployed in modern recommendation engines. Funding: This work was supported by the National Science Foundation [Grants 2118199, 1915967, and CCF-2106508], the Air Force Office of Scientific Research [Award FA9550-20-1-0397], a Digital Twin research grant from Bain & Company, and a faculty research grant from New York University's Center for Global Economy and Business. [ABSTRACT FROM AUTHOR]

In this paper, we consider optimization problems over closed embedded submanifolds of Rn , which are defined by the constraints c(x) = 0. We propose a class of constraint-dissolving approaches for these Riemannian optimization problems. In these proposed approaches, solving a Riemannian optimization problem is transferred into the unconstrained minimization of a constraint-dissolving function (CDF). Different from existing exact penalty functions, the exact gradient and Hessian of CDF are easy to compute. We study the theoretical properties of CDF and prove that the original problem and CDF have the same first-order and second-order stationary points, local minimizers, and Łojasiewicz exponents in a neighborhood of the feasible region. Remarkably, the convergence properties of our proposed constraint-dissolving approaches can be directly inherited from the existing rich results in unconstrained optimization. Therefore, the proposed constraint-dissolving approaches build up short cuts from unconstrained optimization to Riemannian optimization. Several illustrative examples further demonstrate the potential of our proposed constraint-dissolving approaches. Funding: The research of N. Xiao and K.-C. Toh is supported by the Ministry of Education of Singapore Academic Research Fund Tier 3 [Grant MOE-2019-T3-1-010]. The research of X. Liu is supported in part by the National Natural Science Foundation of China [Grants 12125108, 11971466, 12288201, 12021001, and 11991021]; the National Key R&D Program of China [Grants 2020YFA0711900 and 2020YFA0711904]; and the Key Research Program of Frontier Sciences, Chinese Academy of Sciences [Grant ZDBS-LY-7022]. [ABSTRACT FROM AUTHOR]

Bilateral trade, a fundamental topic in economics, models the problem of intermediating between two strategic agents, a seller and a buyer, willing to trade a good for which they hold private valuations. In this paper, we cast the bilateral trade problem in a regret minimization framework over T rounds of seller/buyer interactions, with no prior knowledge on their private valuations. Our main contribution is a complete characterization of the regret regimes for fixed-price mechanisms with different feedback models and private valuations, using as a benchmark the best fixed price in hindsight. More precisely, we prove the following tight bounds on the regret: Θ(T) for full-feedback (i.e., direct revelation mechanisms). Θ(T2/3) for realistic feedback (i.e., posted-price mechanisms) and independent seller/buyer valuations with bounded densities. Θ(T) for realistic feedback and seller/buyer valuations with bounded densities. Θ(T) for realistic feedback and independent seller/buyer valuations. Θ(T) for the adversarial setting. Funding: This work was partially supported by the European Research Council Advanced [Grant 788893] AMDROMA "Algorithmic and Mechanism Design Research in Online Markets", the Ministero dell'Istruzione, dell'Università e della Ricerca PRIN project ALGADIMAR "Algorithms, Games, and Digital Markets", the AI Interdisciplinary Institute ANITI (funded by the French "Investing for the Future—PIA3" program under the [Grant agreement ANR-19-PI3A-0004], the project BOLD from the French national research agency (ANR), the EU Horizon 2020 ICT-48 research and innovation action ELISE (European Learning and Intelligent Systems Excellence, [Grant agreement 951847]. [ABSTRACT FROM AUTHOR]

More than 40 years ago, Lions and Mercier introduced in a seminal paper the Douglas–Rachford algorithm. Today, this method is well-recognized as a classic and highly successful splitting method to find minimizers of the sum of two (not necessarily smooth) convex functions. Whereas the underlying theory has matured, one case remains a mystery: the behavior of the shadow sequence when the given functions have disjoint domains. Building on previous work, we establish for the first time weak and value convergence of the shadow sequence generated by the Douglas–Rachford algorithm in a setting of unprecedented generality. The weak limit point is shown to solve the associated normal problem, which is a minimal perturbation of the original optimization problem. We also present new results on the geometry of the minimal displacement vector. Funding: The research of H. H. Bauschke and W. M. Moursi was partially supported by Discovery Grants of the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2018-03703 and RGPIN-2019-04803], respectively. [ABSTRACT FROM AUTHOR]

This paper studies the optimal design of a paywall when consumers endogenously determine their amount of content consumption. Digital content platforms increasingly implement paywalls to generate subscription revenue, but putting content behind paywalls can reduce consumption and advertising revenue. We analyze optimal paywall design when consumers are heterogenous in their valuation for and satiation with content and endogenously determine their consumption. We find that, under moderate ad rates, a metered paywall offering a limited amount of free content is optimal when consumers display sufficient heterogeneity in satiation. When ad rates increase, one might expect platforms to offer more free content to generate higher ad revenue. Instead, we find that sometimes offering less free content and lowering the subscription price is optimal to attract new subscribers. Further, depending on ad rates, the optimal amount of free content may increase or decrease in consumers' valuation for content. As valuation increases, consumers' willingness to pay for subscription and desire for content consumption increases. The former effect dominates under low ad rates and incentivizes the platform to provide less free content, but the opposite is true under high ad rates. Counterintuitively, we also find that total content consumption can increase with the proportion of users who become satiated relatively faster because the platform may strategically raise its meter limit to increase ad revenues from nonsubscribers. We also explore the impact of a more appealing outside option for consumers, competition between media platforms, and correlation between valuation and satiation on the optimality of paywalls. History: Yuxin Chen served as the senior editor. Funding: This work was supported by the INFORMS Society for Marketing Science (ISMS) through its ISMS Doctoral Dissertation Early Stage Research Grants. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mksc.2023.1444. [ABSTRACT FROM AUTHOR]

We determined weights for a multi-criteria tool for assessing the relative merits of clinical-trial research proposals, and investigated whether the weights vary across relevant stakeholder groups. A cross-sectional, adaptive discrete choice experiment using 1000minds online software was administered to consumers, researchers and funders affiliated with the Australian Clinical Trials Alliance (ACTA). We identified weights for four criteria—Appropriateness, Significance, Relevance, Feasibility—and their levels, representing their relative importance, so that research proposals can be scored between 0% (nil or very low merit) and 100% (very high merit). From 220 complete survey responses, the most important criterion was Appropriateness (adjusted for differences between stakeholder groups, mean weight 28.9%) and the least important was Feasibility (adjusted mean weight 19.5%). Consumers tended to weight Relevance more highly (2.7% points difference) and Feasibility less highly (3.1% points difference) than researchers. The research or grant writing experience of researchers or consumers was not associated with the weights. A multi-criteria tool for evaluating research proposals that reflects stakeholders' preferences was created. The tool can be used to assess the relative merits of clinical trial research proposals and rank them, to help identify the best proposals for funding. [ABSTRACT FROM AUTHOR]

The conditions of mainstream research funding constrain risky, novel research. However, alternative grants are emerging. We study grantees of a double-blinded funding scheme targeting risky, novel research: The Villum Experiment (VE). Without prompting, scientists juxtaposed the experience of performing research under these conditions with that of performing research funded by mainstream grants: fun and less fun. The conditions of VE felt less intrusive and appealed to their self-perceptions and idealized views of scientific work, which shaped how they conducted the funded research. This paper makes three contributions: (1) it reaffirms that how researchers experience grant conditions affects whether a scheme affords what it intends, (2) it highlights that the affordances of research funding are relative to other concurrent funding options, and (3) it shows that small, more broadly allocatable grants can afford scientists a protected space for autonomous research, usually associated with elusive tenure positions or European Research Council (ERC) grants. [ABSTRACT FROM AUTHOR]