pwc-archive/papers-with-abstracts huggingface dataset:
A comprehensive representation of selection at loci with multiple alleles that allows complex forms of genotypic fitness
Authors: Nikolas Vellnow, Toni I. Gossmann, David Waxman
Date: 2024-11-10 00:00:00
Tasks: Diversity

Genetic diversity is central to the process of evolution. Both natural selection and random genetic drift are influenced by the level of genetic diversity of a population; selection acts on diversity while drift samples from it. At a given locus in a diploid population, each individual carries only two alleles, but the population as a whole can possess a much larger number of alleles, with the upper limit constrained by twice the population size. This allows for many possible types of homozygotes and heterozygotes. Moreover, there are biologically important loci, for example those related to the MHC complex, the ABO blood types, and cystic fibrosis, that exhibit a large number of alleles. Despite this, much of population genetic theory, and data analysis, are limited to considering biallelic loci. However, to the present, what is lacking is a flexible expression for the force of selection that allows an arbitrary number of alleles (and hence an arbitrary number of heterozygotes), along with a variety of forms of fitness. In this work, we remedy this absence by giving an analytical representation of the force of selection that emphasises the very different roles played by the diversity of the population, and the fitnesses of different genotypes. The result presented facilitates our understanding and applies in a variety of different situations involving multiple alleles. This includes situations where fitnesses are: additive, multiplicative, randomly fluctuating, frequency-dependent, and it allows fitnesses which involve explicit gene interactions, such as heterozygote advantage.
Towards Seamless Integration of Magnetic Tracking into Fluoroscopy-guided Interventions
Authors: Shuwei Xing, Mateen Mirzaei, Wenyao Xia, Inaara Ahmed-Fazal, Utsav Pardasani, Uditha Jarayathne, Scott Illsley, Leandro Cardarelli Leite, Aaron Fenster, Terry M. Peters, Elvis C. S. Chen
Date: 2024-11-12 00:00:00
Tasks: Pose Estimation

The 2D projective nature of X-ray radiography presents significant limitations in fluoroscopy-guided interventions, particularly the loss of depth perception and prolonged radiation exposure. Integrating magnetic trackers into these workflows is promising; however, it remains challenging and under-explored in current research and practice. To address this, we employed a radiolucent magnetic field generator (FG) prototype as a foundational step towards seamless magnetic tracking (MT) integration. A two-layer FG mounting frame was designed for compatibility with various C-arm X-ray systems, ensuring smooth installation and optimal tracking accuracy. To overcome technical challenges, including accurate C-arm pose estimation, robust fluoro-CT registration, and 3D navigation, we proposed the incorporation of external aluminum fiducials without disrupting conventional workflows. Experimental evaluation showed no clinically significant impact of the aluminum fiducials and the C-arm on MT accuracy. Our fluoro-CT registration demonstrated high accuracy (mean projection distance approxiamtely 0.7 mm, robustness (wide capture range), and generalizability across local and public datasets. In a phantom targeting experiment, needle insertion error was between 2 mm and 3 mm, with real-time guidance using enhanced 2D and 3D navigation. Overall, our results demonstrated the efficacy and clinical applicability of the MT-assisted approach. To the best of our knowledge, this is the first study to integrate a radiolucent FG into a fluoroscopy-guided workflow.
Prion-ViT: Prions-Inspired Vision Transformers for Temperature prediction with Specklegrams
Authors: Abhishek Sebastian, Pragna R, Sonaa Rajagopal, Muralikrishnan Mani
Date: 2024-11-06 00:00:00
Tasks: Temperature Prediction Using Specklegrams

Fiber Specklegram Sensors (FSS) are vital for environmental monitoring due to their high temperature sensitivity, but their complex data poses challenges for predictive models. This study introduces Prion-ViT, a prion-inspired Vision Transformer model, inspired by biological prion memory mechanisms, to improve long-term dependency modeling and temperature prediction accuracy using FSS data. Prion-ViT leverages a persistent memory state to retain and propagate key features across layers, reducing mean absolute error (MAE) to 0.71$^\circ$C and outperforming models like ResNet, Inception Net V2, and Standard Vision Transformers. This paper also discusses Explainable AI (XAI) techniques, providing a perspective on specklegrams through attention and saliency maps, which highlight key regions contributing to predictions
Matching $\leq$ Hybrid $\leq$ Difference in Differences
Authors: Yechan Park, Yuya Sasaki
Date: 2024-11-12 00:00:00

Since LaLonde's (1986) seminal paper, there has been ongoing interest in estimating treatment effects using pre- and post-intervention data. Scholars have traditionally used experimental benchmarks to evaluate the accuracy of alternative econometric methods, including Matching, Difference-in-Differences (DID), and their hybrid forms (e.g., Heckman et al., 1998b; Dehejia and Wahba, 2002; Smith and Todd, 2005). We revisit these methodologies in the evaluation of job training and educational programs using four datasets (LaLonde, 1986; Heckman et al., 1998a; Smith and Todd, 2005; Chetty et al., 2014a; Athey et al., 2020), and show that the inequality relationship, Matching $\leq$ Hybrid $\leq$ DID, appears as a consistent norm, rather than a mere coincidence. We provide a formal theoretical justification for this puzzling phenomenon under plausible conditions such as negative selection, by generalizing the classical bracketing (Angrist and Pischke, 2009, Section 5). Consequently, when treatments are expected to be non-negative, DID tends to provide optimistic estimates, while Matching offers more conservative ones.
Spatial Competition on Psychological Pricing Strategies: Preliminary Evidence from an Online Marketplace
Authors: Magdalena Schindl, Felix Reichel
Date: 2024-11-12 00:00:00

According zu Kadir et al. (2023) online marketplaces are used to buy and sell products and services, as well as to exchange money and data between users or the platform. Due to the large product selection, low costs and the ease of shopping without physical restrictions as well as the technical possibilities, online marketplaces have grown rapidly Kadir et al. (2023). Online marketplaces are also used in the consumer-to-consumer (C2C) sector and thus offer a broad user group a marketplace, for example for used products. This article focuses on Willhaben.at (2024), a leading C2C marketplace in Austria, as stated by Obersteiner, Schmied, and Pamperl (2023). The empirical analysis in this course essay centers around the offer ads of Woom Bikes, a standardised product which is sold on Willhaben. Through web scraping, a dataset of approximately 826 observations was created, focusing on mid-to-high price segment bicycles, which are characterized by price stability and uniformity as we claim. This analysis aims to create analyse ad listing prices through predictive models using willhaben product listing attributes and using the spatial distribution of one of the product attributes.
Dynamic Evolutionary Game Analysis of How Fintech in Banking Mitigates Risks in Agricultural Supply Chain Finance
Authors: Qiang Wan, Jun Cui
Date: 2024-11-12 00:00:00

This paper explores the impact of banking fintech on reducing financial risks in the agricultural supply chain, focusing on the secondary allocation of commercial credit. The study constructs a three-player evolutionary game model involving banks, core enterprises, and SMEs to analyze how fintech innovations, such as big data credit assessment, blockchain, and AI-driven risk evaluation, influence financial risks and access to credit. The findings reveal that banking fintech reduces financing costs and mitigates financial risks by improving transaction reliability, enhancing risk identification, and minimizing information asymmetry. By optimizing cooperation between banks, core enterprises, and SMEs, fintech solutions enhance the stability of the agricultural supply chain, contributing to rural revitalization goals and sustainable agricultural development. The study provides new theoretical insights and practical recommendations for improving agricultural finance systems and reducing financial risks. Keywords: banking fintech, agricultural supply chain, financial risk, commercial credit, SMEs, evolutionary game model, big data, blockchain, AI-driven risk evaluation.
Return-forecasting and Volatility-forecasting Power of On-chain Activities in the Cryptocurrency Market
Authors: Yeguang Chi, Qionghua, Chu, Wenyan Hao
Date: 2024-11-10 00:00:00

We investigate the return-forecasting and volatility-forecasting power of intraday on-chain flow data for BTC, ETH, and USDT, and the associated option strategies. First, we find that USDT net inflow into cryptocurrency exchanges positively forecasts future returns of both BTC and ETH, with the strongest effect at the 1-hour frequency. Second, we find that ETH net inflow into cryptocurrency exchanges negatively forecasts future returns of ETH. Third, we find that BTC net inflow into cryptocurrency exchanges does not significantly forecast future returns of BTC. Finally, we confirm that selling 0DTE ETH call options is a profitable trading strategy when the net inflow into cryptocurrency exchanges is high. Our study lends new insights into the emerging literature that studies the on-chain activities and their asset-pricing impact in the cryptocurrency market.
NGO Activism: Exposure vs. Influence
Authors: Michele Fioretti, Victor Saint-Jean, Simon C. Smith
Date: 2024-11-11 00:00:00

We analyze the timing of NGO campaigns to shed light on NGOs' strategies and how they evolve over time. Data from 2,500 campaigns show that NGOs are six times more likely to launch campaigns on their target's Annual General Meeting (AGM) date. Although this strategy increases media exposure and stakeholder scrutiny, resulting in consumer backlash and related shareholder proposals at the following AGM of the targeted firm, it has no impact on current AGM votes. As NGOs build reputational capital, they adjust their timing to influence AGM votes, revealing the trade-offs they face to drive corporate change.
The Impact of the General Data Protection Regulation (GDPR) on Online Tracking
Authors: Klaus M. Miller, Karlo Lukic, Bernd Skiera
Date: 2024-11-11 00:00:00

This study explores the impact of the General Data Protection Regulation (GDPR), introduced on May 25th, 2018, on online trackers, vital elements in the online advertising ecosystem. Using a difference-in-differences approach with a balanced panel of 294 publishers, we compare publishers subject to the GDPR with those unaffected (the control group). Drawing on data from WhoTracks.me, which spans 32 months from May 2017 to December 2019, we analyze how the number of trackers used by publishers changed before and after the GDPR. The findings reveal that although online tracking increased for both groups, the rise was less significant for EU-based publishers subject to the GDPR. Specifically, the GDPR reduced about four trackers per publisher, equating to a 14.79% decrease compared to the control group. The GDPR was particularly effective in curbing privacy invasive trackers that collect and share personal data, thereby strengthening user privacy. However, it had a limited impact on advertising trackers and only slightly reduced the presence of analytics trackers.
The labour and resource use requirements of a good life for all
Authors: Chris McElroy, Daniel W. O'Neill
Date: 2024-11-10 00:00:00
Tasks: All

We use multi-regional input-output analysis to calculate the paid labour, energy, emissions, and material use required to provide basic needs for all people. We calculate two different low-consumption scenarios, using the UK as a case study: (1) a "decent living" scenario, which includes only the bare necessities, and (2) a "good life" scenario, which is based on the minimum living standards demanded by UK residents. We compare the resulting footprints to the current footprint of the UK, and to the footprints of the US, China, India, and a global average. Labour footprints are disaggregated by sector, skill level, and region of origin. We find that both low-consumption scenarios would still require an unsustainable amount of labour and resources at the global scale. The decent living scenario would require a 26-hour working week, and on a per capita basis, 89 GJ of energy use, 5.9 tonnes of emissions, and 5.7 tonnes of used materials per year. The more socially sustainable good life scenario would require a 53-hour working week, 165 GJ of energy use, 9.9 tonnes of emissions, and 11.5 tonnes of used materials per capita. Both scenarios represent substantial reductions from the UK's current labour footprint of 68 hours per week, which the UK is only able to sustain by importing a substantial portion of its labour from other countries. We conclude that reducing consumption to the level of basic needs is not enough to achieve either social or environmental sustainability. Dramatic improvements in provisioning systems are also required.
Axiomatic characterizations of some simple risk-sharing rules
Authors: Jan Dhaene, Rodrigue Kazzi, Emiliano A. Valdez
Date: 2024-11-09 00:00:00
Tasks: Fairness

In this paper, we present axiomatic characterizations of some simple risk-sharing (RS) rules, such as the uniform, the mean-proportional and the covariance-based linear RS rules. These characterizations make it easier to understand the underlying principles when applying these rules. Such principles typically include maintaining some degree of anonymity regarding participants' data and/or incident-specific data, adopting non-punitive processes and ensuring the equitability and fairness of risk sharing. By formalizing key concepts such as the reshuffling property, the source-anonymous contributions property and the strongly aggregate contributions property, along with their generalizations, we develop a comprehensive framework that expresses these principles clearly and defines the relevant rules. To illustrate, we demonstrate that the uniform RS rule, a simple mechanism in which risks are shared equally, is the only RS rule that satisfies both the reshuffling property and the source-anonymous contributions property. This straightforward axiomatic characterization of the uniform RS rule serves as a foundation for exploring similar principles in two broad classes of risk-sharing rules, which we baptize the $q$-proportional RS rules and the $(q_1,q_2)$-based linear RS rules, respectively. This framework also allows us to introduce novel particular RS rules, such as the scenario-based RS rules.
Can ESG Investment and the Implementation of the New Environmental Protection Law Enhance Public Subjective Well-being?
Authors: Hambur Wang
Date: 2024-11-09 00:00:00

Air pollution has emerged as a serious challenge for China, posing a threat to public health and hindering the progress of sustainable economic development. In response to air pollution and other environmental issues, the Chinese government introduced a new Environmental Protection Law in 2015. This paper investigates the impact of the new Environmental Protection Law's implementation and corporate Environmental, Social, and Governance (ESG) investments on air pollution and public subjective well-being. Using panel data at the macro level, we employ a difference-in-differences (DID) model, with Chinese provinces and municipalities as units of analysis, to examine the combined effects of the new Environmental Protection Law and changes in corporate ESG investment intensity. The study evaluates their impacts on air quality and public subjective well-being. Findings indicate that these policies and investment behaviors significantly improve public subjective well-being by reducing air pollution. Notably, an increase in ESG investment significantly reduces air pollution levels and is positively associated with enhanced well-being. These results underscore the critical role of environmental legislation and corporate social responsibility in improving public quality of life and provide empirical support for promoting sustainable development in China and beyond.
Distinguishing Strong from Weak Signals in Economic Forecasts
Authors: Eric Vansteenberghe
Date: 2024-11-08 00:00:00

We analyze the information embedded in forecasts by examining the moments of subjective probability distributions. Specifically, we propose that a forecast conveys a strong upward signal when the median exceeds a target, such as a central bank's 2% inflation threshold, coupled with positive skewness. Conversely, a median below the target with negative skewness signals strong downward expectations. In cases where the median and skewness diverge, the signal weakens, indicating mixed expectations. To formalize these insights, we develop a Signal Strength Indicator (SSI) that quantifies the consistency and directional alignment of forecast signals, assessing its predictive power within a Growth-at-Risk framework. Importantly, the SSI can be estimated without relying on parametric assumptions. Our findings indicate that the SSI offers valuable insights, suggesting it could serve as a practical tool for central banks to monitor expectations in real time.
Forecasting Company Fundamentals
Authors: Felix Divo, Eric Endress, Kevin Endler, Kristian Kersting, Devendra Singh Dhami
Date: 2024-10-21 00:00:00
Tasks: Econometrics

Company fundamentals are key to assessing companies' financial and overall success and stability. Forecasting them is important in multiple fields, including investing and econometrics. While statistical and contemporary machine learning methods have been applied to many time series tasks, there is a lack of comparison of these approaches on this particularly challenging data regime. To this end, we try to bridge this gap and thoroughly evaluate the theoretical properties and practical performance of 24 deterministic and probabilistic company fundamentals forecasting models on real company data. We observe that deep learning models provide superior forecasting performance to classical models, in particular when considering uncertainty estimation. To validate the findings, we compare them to human analyst expectations and find that their accuracy is comparable to the automatic forecasts. We further show how these high-quality forecasts can benefit automated stock allocation. We close by presenting possible ways of integrating domain experts to further improve performance and increase reliability.
Mechanisms for a dynamic many-to-many school choice problem
Authors: Adriana Amieva, Agustín G. Bonifacio, Pablo Neme
Date: 2024-11-12 00:00:00

We examine the problem of assigning teachers to public schools over time when teachers have tenured positions and can work simultaneously in multiple schools. To do this, we investigate a dynamic many-to-many school choice problem where public schools have priorities over teachers and teachers hold path-independent choice functions selecting subsets of schools. We introduce a new concept of dynamic stability that recognizes the tenured positions of teachers and we prove that a dynamically stable matching always exists. We propose the Tenure-Respecting Deferred Acceptance (TRDA) mechanism, which produces a dynamically stable matching that is constrained-efficient within the class of dynamically stable matchings and minimizes unjustified claims. To improve efficiency beyond this class, we also propose the Tenure-Respecting Efficiency-Adjusted Deferred Acceptance (TREADA) mechanism, an adaptation of the Efficiency-Adjusted Deferred Acceptance mechanism to our dynamic context. We demonstrate that the outcome of the TREADA mechanism Pareto-dominates any dynamically stable matching and achieves efficiency when all teachers consent. Additionally, we examine the issue of manipulability, showing that although the TRDA and TREADA mechanisms can be manipulated, they remain non-obviously dynamically manipulable under specific conditions on schools' priorities.
Implementing Dynamic Pricing Across Multiple Pricing Groups in Real Estate
Authors: Lev Razumovskiy, Mariya Gerasimova, Nikolay Karenin, Mikhail Safro
Date: 2024-11-12 00:00:00

This article presents a mathematical model of dynamic pricing for real estate (RE) that incorporates multiple pricing groups, thereby expanding the capabilities of existing models. The developed model solves the problem of maximizing aggregate cumulative revenue at the end of the sales period while meeting the revenue and sales goals. A method is proposed for distributing aggregate cumulative revenue goals across different RE pricing groups. The model is further modified to account for the time value of money and the real estate value increase as construction progresses. The algorithm for constructing a pricing policy for multiple pricing groups is described, and numerical simulations are performed to demonstrate how the algorithm operates.
Voting behind the Veil of Ignorance
Authors: Boris Ginzburg
Date: 2024-11-11 00:00:00

A committee consisting of two factions is considering a project whose distributive consequences are unknown. This uncertainty can be resolved at some unknown future time. By delaying approval, the committee can gradually learn which faction benefits from the project. Because support of both factions is required for approval, it can only happen when there is sufficient amount of uncertainty about the identities of winners and losers. I show that in many situations, a project is more likely to be approved if it gives a lower payoff to everyone. The probability of approval and expected payoffs of both factions are higher if the project is ex ante less likely to benefit the faction that tends to receive good news faster. Equilibrium amount of learning is excessive, and a deadline on adopting the project is often optimal.
Allocating Positional Goods: A Mechanism Design Approach
Authors: Peiran Xiao
Date: 2024-11-09 00:00:00

I study the optimal allocation of positional goods with externalities and one-sided transfers. Because consumers care about their relative positions in consumption, allocating an item to one buyer has externalities on others. Using a mechanism design approach, I characterize the externalities by a feasibility condition. I find the revenue-maximizing mechanism excludes some low types and fully separates the rest if and only if the buyer's type distribution satisfies Myerson's regularity. The seller can guarantee at least half the maximal revenue by offering one level of positional goods, and the approximation can be arbitrarily close if the distribution is sufficiently concave. Moreover, if the distribution has an increasing (decreasing) failure rate, total pooling (full separation) without exclusion maximizes the consumer surplus, and the consumer surplus is decreasing (increasing) in the number of positional good levels. Applications include education, priority services, luxury goods, and organizational design.