pwc-archive/papers-with-abstracts huggingface dataset:
Machine-learning Growth at Risk
Authors: Tobias Adrian, Hongqi Chen, Max-Sebastian Dovì, Ji Hyung Lee
Date: 2025-05-31 00:00:00
Tasks: Model Selection, Time Series

We analyse growth vulnerabilities in the US using quantile partial correlation regression, a selection-based machine-learning method that achieves model selection consistency under time series. We find that downside risk is primarily driven by financial, labour-market, and housing variables, with their importance changing over time. Decomposing downside risk into its individual components, we construct sector-specific indices that predict it, while controlling for information from other sectors, thereby isolating the downside risks emanating from each sector.
Frictions and Welfare in Monopolistic Competition
Authors: Francesco Del Prato, Paolo Zacchia
Date: 2025-05-30 00:00:00

In a heterogeneous firm economy with monopolistic competition, could informational asymmetries between entrepreneurs and financial intermediaries sometimes improve welfare? We study this question by developing a model where banks finance entrepreneurs under asymmetric information. While aggregate productivity decreases with informational frictions, we find that welfare can be maximized at intermediate levels of information asymmetry due to a trade-off between productivity and product variety. Additionally, moderate input cost distortions can improve welfare when financial frictions are severe by offsetting the resulting weak firm selection.
Climate impacts and monetary costs of healthy diets worldwide
Authors: Yan Bai, Elena M. Martinez, Mizuki Yamanaka, Marko Rissanen, Anna Herforth, William A. Masters
Date: 2025-05-30 00:00:00
Tasks: Nutrition

About 2.8 billion people worldwide cannot afford the least expensive foods required for a healthy diet. Since 2020, the Cost and Affordability of a Healthy Diet (CoAHD) has been published for all countries by FAO and the World Bank and is widely used to guide social protection, agricultural, and public health and nutrition policies. Here, we measure how healthy diets could be obtained with the lowest possible greenhouse gas (GHG) emissions, in ways that could further inform food choice and policy decisions toward sustainability goals. We find that the lowest possible GHG emissions for a healthy diet in 2021 would emit 0.67 kg CO2e (SD=0.10) and cost USD 6.95 (SD=1.86) per day, while each country's lowest-priced items would emit 1.65 kg CO2e (SD=0.56) and cost USD 3.68 (SD=0.75). Healthy diets with foods in proportions actually consumed in each country would emit 2.44 kg CO2e (SD=1.27) and cost USD 9.96 (SD=4.92). Differences in emissions are driven by item selection within animal-source foods, and starchy staples to a lesser extent, with only minor differences in other food groups. Results show how changes in agricultural policy and food choice can most cost-effectively support healthier and more sustainable diets worldwide.
Winners vs. Losers: Momentum-based Strategies with Intertemporal Choice for ESG Portfolios
Authors: Ayush Jha, Abootaleb Shirvani, Ali Jaffri, Svetlozar T. Rachev, Frank J. Fabozzi
Date: 2025-05-30 00:00:00

This paper introduces a state-dependent momentum framework that integrates ESG regime switching with tail-risk-aware reward-risk metrics. Using a dynamic programming approach and solving a finite-horizon Bellman equation, we construct long-short momentum portfolios that adjust to changing ESG sentiment regimes. Unlike traditional momentum strategies based on historical returns, our approach incorporates the Stable Tail Adjusted Return ratio and Rachev ratio to better capture downside risk in turbulent markets. We apply this framework across three asset classes, Russell 3000 equities, Dow Jones~30 stocks, and cryptocurrencies, under both pro- and anti-ESG market regimes. We find that ESG-loser portfolios significantly outperform ESG-winner portfolios in pro-ESG regimes, a counterintuitive result suggesting that market overreaction to ESG sentiment creates short-term pricing inefficiencies. This pattern is robust across tail-sensitive performance metrics and is most pronounced under a two-week formation and holding period. Our framework highlights how ESG considerations and sentiment regimes alter return dynamics, offering practical guidance for investors seeking to implement responsive momentum strategies under sustainability constraints. These findings challenge conventional assumptions about ESG investing and underscore the importance of dynamic, regime-aware portfolio construction in environments shaped by regulatory signals, investor flows, and behavioral biases.
Rethinking Growth: An Extension of the Solow-Swan Model
Authors: Timothy F. Power, Roman G. Smirnov
Date: 2025-06-12 00:00:00

The aggregate Cobb-Douglas production function stands as a central element in the renowned Solow-Swan model in economics, providing a crucial theoretical framework for comprehending the determinants of economic growth. This model not only guides policymakers and economists but also influences their decisions, fostering sustainable and inclusive development. In this study, we utilize a one-input version of a new generalization of the Cobb-Douglas production function proposed recently, thereby extending the Solow-Swan model to incorporate energy production as a factor. We offer a rationale for this extension and conduct a comprehensive analysis employing advanced mathematical tools to explore solutions to this new model. This approach allows us to effectively integrate environmental considerations related to energy production into economic growth strategies, fostering long-term sustainability.
Cursed Equilibria and Knightian Uncertainty in a Trading Game
Authors: Jurek Preker
Date: 2025-06-12 00:00:00

We introduce a novel equilibrium concept that incorporates Knightian uncertainty into the cursed equilibrium (Eyster and Rabin, 2005). This concept is then applied to a two-player game in which agents can engage in trade or refuse to do so. While the Bayesian Nash equilibrium predicts that trade never happens, players do trade in a cursed equilibrium. The inclusion of uncertainty enhances this effect for cursed and uncertainty averse players. This contrasts general findings that uncertainty reduces trade but is consistent with behavior that has been observed in experiments.
Seeding an Uncertain Technology
Authors: Eric Gao
Date: 2025-06-12 00:00:00

I study how a startup with uncertainty over product quality and no knowledge of the underlying diffusion network optimally chooses initial seeds. To ensure widespread adoption when the product is good while minimizing negative perceptions when it is bad, the optimal number of initial seeds should grow logarithmically with network size. When there are agents of different types that govern their connectivity, it is asymptotically optimal to seed agents of a single type: the type that minimizes the marginal cost per probability of making the product go viral. These results rationalize startup behavior in practice.
The (Mis)use of Information in Decentralised Markets
Authors: D. Carlos Akkar
Date: 2025-06-07 00:00:00

A seller offers an asset in a decentralised market. Buyers have private signals about their common value. I study whether the market becomes allocatively more efficient with (i) more buyers, (ii) better-informed buyers. Both increase the information available about buyers' common value, but also the adverse selection each buyer faces. With more buyers, trade surplus eventually increases and converges to the full-information upper bound if and only if the likelihood ratios of buyers' signals are unbounded from above. Otherwise, it eventually decreases and converges to the no-information lower bound. With better information about trades buyers would have accepted, trade surplus increases. With better information about trades they would have rejected, trade surplus decreases--unless adverse selection is irrelevant. For binary signals, a sharper characterisation emerges: stronger good news increase total surplus, but stronger bad news eventually decrease it.
Limits of Disclosure in Search Markets
Authors: Raphael Boleslavsky, Silvana Krasteva
Date: 2025-05-28 00:00:00
Tasks: Informativeness

This paper examines competitive information disclosure in search markets with a mix of savvy consumers, who search costlessly, and inexperienced consumers, who face positive search costs. Savvy consumers incentivize truthful disclosure; inexperienced consumers, concealment. With both types, equilibrium features partial disclosure, which persists despite intense competition: in large markets, firms always conceal low valuations. Inexperienced consumers may search actively, but only in small markets. While savvy consumers benefit from increased competition, inexperienced consumers may be harmed. Changes in search costs have non-monotone effects: when costs are low, sufficient reductions increase informativeness and welfare; when costs are high, the opposite.
From Axioms to Algorithms: Mechanized Proofs of the vNM Utility Theorem
Authors: Li Jingyuan
Date: 2025-06-08 00:00:00
Tasks: Management

This paper presents a comprehensive formalization of the von Neumann-Morgenstern (vNM) expected utility theorem using the Lean 4 interactive theorem prover. We implement the classical axioms of preference-completeness, transitivity, continuity, and independence-enabling machine-verified proofs of both the existence and uniqueness of utility representations. Our formalization captures the mathematical structure of preference relations over lotteries, verifying that preferences satisfying the vNM axioms can be represented by expected utility maximization. Our contributions include a granular implementation of the independence axiom, formally verified proofs of fundamental claims about mixture lotteries, constructive demonstrations of utility existence, and computational experiments validating the results. We prove equivalence to classical presentations while offering greater precision at decision boundaries. This formalization provides a rigorous foundation for applications in economic modeling, AI alignment, and management decision systems, bridging the gap between theoretical decision theory and computational implementation.
A Tale of Two Monopolies
Authors: Yi-Chun Chen, Zhengqing Gui
Date: 2025-06-07 00:00:00

We apply marginal analysis \`a la Bulow and Roberts (1989) to characterize the revenue-maximizing selling mechanism for a multiproduct monopoly. Specifically, we derive the revenue change due to a price perturbation on any subset of bundles holding the prices of other bundles fixed. In an optimal mechanism, total revenue must not increase with any small price change for bundles with positive demand, nor with a small price decrease for bundles with zero demand. For any symmetric two-dimensional type distribution satisfying mild regularity conditions, the marginal analysis fully characterizes the optimal mechanism, whether the buyer's valuations are additive or exhibit complementarity or substitutability. For general type distributions, the analysis identifies which bundles must carry positive or zero demand and provides conditions under which randomization is necessary.
Propose or Vote: A simple Democratic Procedure
Authors: Hans Gersbach
Date: 2025-06-06 00:00:00

This paper introduces a simple democratic procedure. In a first stage, all members of a polity decide whether to apply for proposal-making or later vote on proposals made in the second stage. This procedure is called Propose or Vote (PoV). With appropriate default points and majority voting over two randomly selected proposals, the PoV procedure can implement the Condorcet winner with only one round of voting if a Condorcet winner exists. We explore ways to establish uniqueness, alternative voting procedures over the selected alternatives, and the application to elections. In the latter case, agents can decide whether to stand for election to an office or to vote on the set of candidates.
A note on the Diversity Owen values
Authors: Songtao He, Erfang Shan, Xinyu Sun
Date: 2025-05-30 00:00:00
Tasks: Diversity, Fairness

B\'eal et al. (Int J Game Theory 54, 2025) introduce the Diversity Owen value for TU-games with diversity constraints, and provide axiomatic characterizations using the axioms of fairness and balanced contributions. However, there exist logical flaws in the proofs of the uniqueness of these characterizations. In this note we provide the corrected proofs of the characterizations by introducing the null player for diverse games axiom. Also, we establish an alternative characterization of the Diversity Owen value by modifying the axioms of the above characterizations.
No Trade Under Verifiable Information
Authors: Spyros Galanis
Date: 2025-06-05 00:00:00

No trade theorems examine conditions under which agents cannot agree to disagree on the value of a security which pays according to some state of nature, thus preventing any mutual agreement to trade. A large literature has examined conditions which imply no trade, such as relaxing the common prior and common knowledge assumptions, as well as allowing for agents who are boundedly rational or ambiguity averse. We contribute to this literature by examining conditions on the private information of agents that reveals, or verifies, the true value of the security. We argue that these conditions can offer insights in three different settings: insider trading, the connection of low liquidity in markets with no trade, and trading using public blockchains and oracles.
Distributionally Robust Auction Design with Deferred Inspection
Authors: Halil I. Bayrak, Martin Bichler
Date: 2025-06-05 00:00:00

Mechanism design with inspection has received increasing attention due to its applications in the field. For example, large warehouses have started to auction scarce capacity. This capacity shall be allocated in a way that maximizes the seller's revenue. In such mechanism design problems, the seller can inspect the true value of a buyer and his realized sales in the next period without cost. Prior work on mechanism design with deferred inspection is based on the assumption of a common prior distribution. We design a mechanism with a deferred inspection that is (distributionally) robustly optimal either when the ambiguity-averse mechanism designer wants to maximize her worst-case expected payoff or when she wants to minimize her worst-case expected regret. It is a relatively simple mechanism with a concave allocation and linear payment rules. We also propose another robustly optimal mechanism that has the same concave allocation function but extracts the maximal payment from all the types of the agent, which can have a strictly higher expected payoff under non-worst-case distributions compared to the robustly optimal mechanism with the linear payment rule. We show that multi-bidder monotonous mechanisms might not exist.
Correlated equilibrium implementation: Navigating toward social optima with learning dynamics
Authors: Soumen Banerjee, Yi-Chun Chen, Yifei Sun
Date: 2025-06-04 00:00:00

Implementation theory has made significant advances in characterizing which social choice functions can be implemented in Nash equilibrium, but these results typically assume sophisticated strategic reasoning by agents. However, evidence exists to show that agents frequently cannot perform such reasoning. In this paper, we present a finite mechanism which fully implements Maskin-monotonic social choice functions as the outcome of the unique correlated equilibrium of the induced game. Due to the results in Hart and MasColell (2000), this yields that even when agents use a simple adaptive heuristic like regret minimization rather than computing equilibrium strategies, the designer can expect to implement the SCF correctly. We demonstrate the mechanism's effectiveness through simulations in a bilateral trade environment, where agents using regret matching converge to the desired outcomes despite having no knowledge of others' preferences or the equilibrium structure. The mechanism does not use integer games or modulo games.
Elasticity of substitution and general model of economic growth
Authors: Constantin Chilarescu
Date: 2025-06-03 00:00:00
Tasks: Position

The main purpose of this paper is to generalize some recent results obtained by Chilarescu and Manuel Gomez. Essentially, we are trying to study the effect of elasticity of substitution on the parameters of economic growth, based on its two possible values - lower and higher than one. We show that a higher elasticity of substitution increases per capita income, the relative share of physical capital, the common growth rate and the share of human capital allocated to the production sector, and this property is not affected by the position of the elasticity of substitution - below or above one.
Generative AI and Organizational Structure in the Knowledge Economy
Authors: Fasheng Xu, Jing Hou, Wei Chen, Karen Xie
Date: 2025-05-31 00:00:00
Tasks: Hallucination

The adoption of GenAI is fundamentally reshaping organizations in the knowledge economy. GenAI can significantly enhance workers' problem-solving abilities and productivity, yet it also presents a major reliability challenge: hallucinations, or errors presented as plausible outputs. This study develops a theoretical model to examine GenAI's impact on organizational structure and the role of human-in-the-loop oversight. Our findings indicate that successful GenAI adoption hinges primarily on maintaining hallucination rates below a critical level. After adoption, as GenAI advances in capability or reliability, organizations optimize their workforce by reducing worker knowledge requirements while preserving operational effectiveness through GenAI augmentation-a phenomenon known as deskilling. Unexpectedly, enhanced capability or reliability of GenAI may actually narrow the span of control, increasing the demand for managers rather than flattening organizational hierarchies. To effectively mitigate hallucination risks, many firms implement human-in-the-loop validation, where managers review GenAI-enhanced outputs before implementation. While the validation increases managerial workload, it can, surprisingly, expand the span of control, reducing the number of managers needed. Furthermore, human-in-the-loop validation influences GenAI adoption differently based on validation costs and hallucination rates, deterring adoption in low-error, high-cost scenarios, while promoting it in high-error, low-cost cases. Finally, productivity improvements from GenAI yield distinctive organizational shifts: as productivity increases, firms tend to employ fewer but more knowledgeable workers, gradually expanding managerial spans of control.
Random Utility with Aggregated Alternatives
Authors: Yuexin Liao, Kota Saito, Alec Sandroni
Date: 2025-05-31 00:00:00

This paper studies when discrete choice data involving aggregated alternatives such as categorical data or an outside option can be rationalized by a random utility model (RUM). Aggregation introduces ambiguity in composition: the underlying alternatives may differ across individuals and remain unobserved by the analyst. We characterize the observable implications of RUMs under such ambiguity and show that they are surprisingly weak, implying only monotonicity with respect to adding aggregated alternatives and standard RUM consistency on unaggregated menus. These are insufficient to justify the use of an aggregated RUM. We identify two sufficient conditions that restore full rationalizability: non-overlapping preferences and menu-independent aggregation. Simulations show that violations of these conditions generate estimation bias, highlighting the practical importance of how aggregated alternatives are defined.
Note on the size of a stable matching
Authors: Gregory Z. Gutin, Philip R. Neary, Anders Yeo
Date: 2025-05-30 00:00:00
Tasks: Position

Consider a one-to-one two-sided matching market with workers on one side and single-position firms on the other, and suppose that the largest individually rational matching contains $n$ pairs. We show that the number of workers employed and positions filled in every stable matching is bounded from below by $\lceil\frac{n}{2}\rceil$ and we characterise the class of preferences that attain the bound.
FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition
Authors: Jongsuk Kim, Jaemyung Yu, Minchan Kwon, Junmo Kim
Date: 2025-06-12 00:00:00
Tasks: Automatic Speech Recognition, Contrastive Learning, Fairness, speech-recognition, Speech Recognition

Large-scale ASR models have achieved remarkable gains in accuracy and robustness. However, fairness issues remain largely unaddressed despite their critical importance in real-world applications. In this work, we introduce FairASR, a system that mitigates demographic bias by learning representations that are uninformative about group membership, enabling fair generalization across demographic groups. Leveraging a multi-demographic dataset, our approach employs a gradient reversal layer to suppress demographic-discriminative features while maintaining the ability to capture generalizable speech patterns through an unsupervised contrastive loss. Experimental results show that FairASR delivers competitive overall ASR performance while significantly reducing performance disparities across different demographic groups.
Multi-Distillation from Speech and Music Representation Models
Authors: Jui-Chiang Wei, Yi-Cheng Lin, Fabian Ritter-Gutierrez, Hung-Yi Lee
Date: 2025-06-08 00:00:00
Tasks: Few-Shot Learning

Real-world audio often mixes speech and music, yet models typically handle only one domain. This paper introduces a multi-teacher distillation framework that unifies speech and music models into a single one while significantly reducing model size. Our approach leverages the strengths of domain-specific teacher models, such as HuBERT for speech and MERT for music, and explores various strategies to balance both domains. Experiments across diverse tasks demonstrate that our model matches the performance of domain-specific models, showing the effectiveness of cross-domain distillation. Additionally, we conduct few-shot learning experiments, highlighting the need for general models in real-world scenarios where labeled data is limited. Our results show that our model not only performs on par with specialized models but also outperforms them in few-shot scenarios, proving that a cross-domain approach is essential and effective for diverse tasks with limited data.
Recognizing Every Voice: Towards Inclusive ASR for Rural Bhojpuri Women
Authors: Sakshi Joshi, Eldho Ittan George, Tahir Javed, Kaushal Bhogale, Nikhil Narasimhan, Mitesh M. Khapra
Date: 2025-06-11 00:00:00

Digital inclusion remains a challenge for marginalized communities, especially rural women in low-resource language regions like Bhojpuri. Voice-based access to agricultural services, financial transactions, government schemes, and healthcare is vital for their empowerment, yet existing ASR systems for this group remain largely untested. To address this gap, we create SRUTI ,a benchmark consisting of rural Bhojpuri women speakers. Evaluation of current ASR models on SRUTI shows poor performance due to data scarcity, which is difficult to overcome due to social and cultural barriers that hinder large-scale data collection. To overcome this, we propose generating synthetic speech using just 25-30 seconds of audio per speaker from approximately 100 rural women. Augmenting existing datasets with this synthetic data achieves an improvement of 4.7 WER, providing a scalable, minimally intrusive solution to enhance ASR and promote digital inclusion in low-resource language.