pwc-archive/papers-with-abstracts huggingface dataset:
Effect of a Manager in Relational Contracts with Multiple Workers
Authors: Beomjun Kim
Date: 2025-04-30 00:00:00
Tasks: Management

This paper considers the optimal management structure about hiring a manager and providing the manager with a separate salary and bonus using a relational contract among an owner, a manager, and workers, assuming that the manager can observe individual worker performances while the owner can observe only overall team performance. I derive optimal contracts for the two cases in which the manage's salary and bonus are integrated into total team bonus or provided separately. I compare situations of having the manager distribute bonuses based on individual worker performance to the situation of equal bonus distribution based on overall team performance without a manager. Only a contract with a manager who receives a separate bonus is feasible for low discount factor. Making the manager to distribute the salary and bonus including himself is best with intermediate discount factor. Providing an equal bonus without a manager is optimal with high discount factor.
Positively Homogeneous Saddle-Functions and Euler's Theorem in Games
Authors: Joseph M. Ostroy, Joon Song
Date: 2025-04-28 00:00:00

Connections are made between solution concepts for games in characteristic function form and Euler's Theorem underlying the neo-classical theory of distribution in which the total output produced is imputed to the marginal products of the inputs producing it. The assumptions for Euler's Theorem are constant returns (positive homogeneity) and differentiability of the production function. Representing characteristic functions in a vector space setting, marginal products of commodity inputs are translated as marginal products of individuals. Marginal products for discrete (resp. infinitesimal) individuals are defined by discrete (resp. infinitesimal) directional derivates. Additivity of directional derivatives underlies the definition of differentiability in both discrete and infinitesimal settings. A key distinction is between characteristic functions defined by von Neumann and Morgenstern (vNM) which do not necessarily exhibit concavity and characteristic function that do. A modification of the definition, interpreted as introducing ``property rights,'' implies concavity. The Shapley value is a redefinition of an individual's marginal product for a (vNM) characteristic function. Concave characteristic functions do not require such redefinition. Concave characteristic functions imply the existence of positively homogeneous saddle-function functions whose saddle-points represent equilbria of the game. The saddle-point property applies to games with populations consisting of any number of individuals each type. When there is a small integer number of each type, the saddle-point property is often, but not always, inconsistent with the Euler condition, that each individual receives its marginal contribution. Conversely, the saddle-point condition is typically, but not always, consistent with the Euler condition when there are a large number of each type.
Hua-Chen New Theory of Economic Optimization
Authors: Bin Chen, Yingchao Xie, Ting Yang, Qin Zhou
Date: 2025-04-27 00:00:00
Tasks: Automated Theorem Proving, Survey

Between 1957-1985, Chinese mathematician Loo-Keng Hua pioneered economic optimization theory through three key contributions: establishing economic stability's fundamental theorem, proving the uniqueness of equilibrium solutions in economic systems, and developing a consumption-integrated model 50 days before his death. Since 1988, Mu-Fa Chen has been working on Hua's theory. He introduced stochastics, namely Markov chains, to economic optimization theory. He updated and developed Hua's model and came up with a new model (Chen's model) which has become the starting point of a new economic optimization theory. Chen's theory can be applied to economic stability test, bankruptcy prediction, product ranking and classification, economic prediction and adjustment, economic structure optimization. Chen's theory can also provide efficient algorithms that are programmable and intelligent. {Stochastics} is the cornerstone of Chen's theory. There is no overlap between Chen's theory, and the existing mathematical economy theory and the economics developments that were awarded Nobel Prizes in Economics between 1969 and 2024. The distinguished features of Chen's theory from the existing theories are quantitative, calculable, predictable, optimizable, programmable and can be intelligent. This survey provides a theoretical overview of the newly published monograph \cite{5rw24}. Specifically, the invariant of the economic structure matrix, also known as the Chen's invariant, was first published in this survey.
Robust Contracting for Sequential Search
Authors: Théo Durandard, Udayan Vaidya, Boli Xu
Date: 2025-04-24 00:00:00

A principal contracts with an agent who can sequentially search over projects to generate a prize. The principal knows only one of the agent's available projects and evaluates a contract by its worst-case performance. We characterize the set of robustly optimal contracts, all of which involve a minimum debt level, i.e., the agent only receives payment if the prize exceeds a certain threshold. This debt requirement is essential to prevent the agent from terminating the search too early. Our characterization encompasses several commonly observed contract formats, including pure debt, debt-plus-equity, and tranches. We also study situations where each of these contracts emerges as the unique prediction. In contrast to much of the existing robust contracting literature, linear contracts are strictly sub-optimal because they dampen the agent's search incentive.
Matching with regional constraints: An equivalence
Authors: Elizabeth Nanami Aoi
Date: 2025-04-24 00:00:00

In two-sided matching market, when the regional constraints are present, the deferred acceptance (DA) algorithm suffers from undesirable inefficiency due to the artificial allocation of the regional caps among hospitals. We show that, given preferences, there exist allocations that guarantee the efficiency of the DA algorithm. Furthermore, it is equivalent to the FDA algorithm developed by Kamada and Kojima (2015), which endows the latter with an interpretation as a tool for endogenous capacity design. Our proof applies the optimality within the matching with contracts (Hatfield and Milgrom 2005) framework, offering a broadly applicable method for establishing equivalence among DA-based mechanisms.
Robust Maximum Likelihood Updating
Authors: Elchin Suleymanov
Date: 2025-04-24 00:00:00

There is a large body of evidence that decision makers frequently depart from Bayesian updating. This paper introduces a model, robust maximum likelihood (RML) updating, where deviations from Bayesian updating are due to multiple priors/ambiguity. Using the decision maker's preferences over acts before and after the arrival of new information as the primitive of the analysis, I axiomatically characterize a representation where the decision maker's probability assessment can be described by a benchmark prior, which is reflected in her ex ante ranking of acts, and a set of plausible priors, which is revealed from her updated preferences. When new information is received, the decision maker revises her benchmark prior within the set of plausible priors via the maximum likelihood principle in a way that ensures maximally dynamically consistent behavior, and updates the new prior using Bayes' rule. RML updating accommodates most commonly observed biases in probabilistic reasoning.
On the stability of utilitarian aggregation
Authors: Leandro Nascimento
Date: 2025-04-23 00:00:00

In the context of aggregating von Neumann-Morgenstern utilities, we show that bounded violations of the Pareto conditions characterize aggregation rules that are approximately utilitarian. When a single utility function is intended to represent the preference judgments of a group of individuals and the Pareto principles are nearly satisfied, we prove that its distance from a weighted sum of individual cardinal utilities does not exceed half of the positive parameter that differentiates our weaker versions of the Pareto conditions from their conventional forms. This result suggests the stability of Harsanyi's (1955) aggregation theorem, in that small deviations from the Pareto principles lead to aggregation rules that remain close to utilitarian aggregation.
Axiomatic Equilibrium Selection: The Case of Generic Extensive Form Games
Authors: Srihari Govindan, Robert B. Wilson
Date: 2025-04-23 00:00:00
Tasks: Form

A solution concept that is a refinement of Nash equilibria selects for each finite game a nonempty collection of closed and connected subsets of Nash equilibria as solutions. We impose three axioms for such solution concepts. The axiom of backward induction requires each solution to contain a quasi-perfect equilibrium. Two invariance axioms posit that solutions of a game are the same as those of a game obtained by the addition of strategically irrelevant strategies and players. Stability satisfies these axioms; and any solution concept that satisfies them must, for generic extensive-form games, select from among its stable outcomes. A strengthening of the two invariance axioms provides an analogous axiomatization of components of equilibria with a nonzero index.
Bertrand Menu Competition
Authors: Fuhito Kojima, Bobak Pakzad-Hurson
Date: 2025-04-23 00:00:00

We study a variation of the price competition model a la Bertrand, in which firms must offer menus of contracts that obey monotonicity constraints, e.g., wages that rise with worker productivity to comport with equal pay legislation. While such constraints limit firms' ability to undercut their competitors, we show that Bertrand's classic result still holds: competition drives firm profits to zero and leads to efficient allocations without rationing. Our findings suggest that Bertrand's logic extends to a broader variety of markets, including labor and product markets that are subject to real-world constraints on pricing across workers and products.
Existence of Bayesian Equilibria in Incomplete Information Games without Common Priors
Authors: Denis Kojevnikov, Kyungchul Song
Date: 2025-04-22 00:00:00

We consider incomplete information finite-player games where players may hold mutually inconsistent beliefs without a common prior. We introduce absolute continuity of beliefs, extending the classical notion of absolutely continuous information in Milgrom and Weber (1985), and prove that a Bayesian equilibrium exists under broad conditions. Applying these results to games with rich type spaces that accommodate infinite belief hierarchies, we show that when the analyst's game has a type space satisfying absolute continuity of beliefs, the actual game played according to the belief hierarchies induced by the type space has a Bayesian equilibrium for a wide class of games. We provide examples that illustrate practical applications of our findings.
A Lagrangian Approach to Optimal Lotteries in Non-Convex Economies
Authors: Chengfeng Shen, Felix Kübler, Yucheng Yang, Zhennan Zhou
Date: 2025-04-22 00:00:00

We develop a new method to efficiently solve for optimal lotteries in models with non-convexities. In order to employ a Lagrangian framework, we prove that the value of the saddle point that characterizes the optimal lottery is the same as the value of the dual of the deterministic problem. Our algorithm solves the dual of the deterministic problem via sub-gradient descent. We prove that the optimal lottery can be directly computed from the deterministic optima that occur along the iterations. We analyze the computational complexity of our algorithm and show that the worst-case complexity is often orders of magnitude better than the one arising from a linear programming approach. We apply the method to two canonical problems with private information. First, we solve a principal-agent moral-hazard problem, demonstrating that our approach delivers substantial improvements in speed and scalability over traditional linear programming methods. Second, we study an optimal taxation problem with hidden types, which was previously considered computationally infeasible, and examine under which conditions the optimal contract will involve lotteries.
Barter and Hierarchy: A Practical Perspective on Food, Society, and Knowledge in the Inca Empire
Authors: Luis-Felipe Arizmendi
Date: 2025-04-22 00:00:00

The Inca Empire developed a sophisticated food production system, social organisation, and knowledge transmission without money or writing. The article introduces the concept of a barter economy structured through hierarchical cooperation and examines the Inca model from a practice-based (heuristical) perspective.
Optimal Procurement Design: A Reduced Form Approach
Authors: Kun Zhang
Date: 2025-04-22 00:00:00
Tasks: Form

Standard procurement models assume that the buyer knows the quality of the good at the time of procurement; however, in many settings, the quality is learned only long after the transaction. We study procurement problems in which the buyer's valuation of the supplied good depends directly on its quality, which is unverifiable and unobservable to the buyer. For a broad class of procurement problems, we identify procurement mechanisms maximizing any weighted average of the buyer's expected payoff and social surplus. The optimal mechanism can be implemented by an auction that restricts sellers to submit bids within specific intervals.
Highly mutually dependent unions and new axiomatizations of the Owen value
Authors: Songtao He, Erfang Shan, Hanqi Zhou
Date: 2025-04-19 00:00:00

The Owen value is an well-known allocation rule for cooperative games with coalition structure.In this paper, we introduce the concept of highly mutually dependent unions. Two unions in a cooperative game with coalition structure are said to be highly mutually dependent if any pair of players, with one from each of the two unions, are mutually dependent in the game.Based on this concept, we introduce two axioms: weak mutually dependent between unions and differential marginality of inter-mutually dependent unions. Furthermore, we also propose another two axioms: super inter-unions marginality and invariance across games, where the former one is based on the concept of the inter-unions marginal contribution. By using the axioms and combining with some standard axioms, we present three axiomatic characterizations of the Owen value.
Fairness of Automatic Speech Recognition in Cleft Lip and Palate Speech
Authors: Susmita Bhattacharjee, Jagabandhu Mishra, H. S. Shekhawat, S. R. Mahadeva Prasanna
Date: 2025-05-06 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Fairness, speech-recognition, Speech Recognition

Speech produced by individuals with cleft lip and palate (CLP) is often highly nasalized and breathy due to structural anomalies, causing shifts in formant structure that affect automatic speech recognition (ASR) performance and fairness. This study hypothesizes that publicly available ASR systems exhibit reduced fairness for CLP speech and confirms this through experiments. Despite formant disruptions, mild and moderate CLP speech retains some spectro-temporal alignment with normal speech, motivating augmentation strategies to enhance fairness. The study systematically explores augmenting CLP speech with normal speech across severity levels and evaluates its impact on ASR fairness. Three ASR models-GMM-HMM, Whisper, and XLS-R-were tested on AIISH and NMCPC datasets. Results indicate that training with normal speech and testing on mixed data improves word error rate (WER). Notably, WER decreased from $22.64\%$ to $18.76\%$ (GMM-HMM, AIISH) and $28.45\%$ to $18.89\%$ (Whisper, NMCPC). The superior performance of GMM-HMM on AIISH may be due to its suitability for Kannada children's speech, a challenge for foundation models like XLS-R and Whisper. To assess fairness, a fairness score was introduced, revealing improvements of $17.89\%$ (AIISH) and $47.50\%$ (NMCPC) with augmentation.
The Search for Squawk: Agile Modeling in Bioacoustics
Authors: Vincent Dumoulin, Otilia Stretcu, Jenny Hamer, Lauren Harrell, Rob Laber, Hugo Larochelle, Bart van Merriënboer, Amanda Navine, Patrick Hart, Ben Williams, Timothy A. C. Lamont, Tries B. Razak, Mars Coral Restoration Team, Sheryn Brodie, Brendan Doohan, Phil Eichinski, Paul Roe, Lin Schwarzkopf, Tom Denton
Date: 2025-05-05 00:00:00
Tasks: Active Learning

Passive acoustic monitoring (PAM) has shown great promise in helping ecologists understand the health of animal populations and ecosystems. However, extracting insights from millions of hours of audio recordings requires the development of specialized recognizers. This is typically a challenging task, necessitating large amounts of training data and machine learning expertise. In this work, we introduce a general, scalable and data-efficient system for developing recognizers for novel bioacoustic problems in under an hour. Our system consists of several key components that tackle problems in previous bioacoustic workflows: 1) highly generalizable acoustic embeddings pre-trained for birdsong classification minimize data hunger; 2) indexed audio search allows the efficient creation of classifier training datasets, and 3) precomputation of embeddings enables an efficient active learning loop, improving classifier quality iteratively with minimal wait time. Ecologists employed our system in three novel case studies: analyzing coral reef health through unidentified sounds; identifying juvenile Hawaiian bird calls to quantify breeding success and improve endangered species monitoring; and Christmas Island bird occupancy modeling. We augment the case studies with simulated experiments which explore the range of design decisions in a structured way and help establish best practices. Altogether these experiments showcase our system's scalability, efficiency, and generalizability, enabling scientists to quickly address new bioacoustic challenges.
Towards Flow-Matching-based TTS without Classifier-Free Guidance
Authors: Yuzhe Liang, Wenzhe Liu, Chunyu Qiang, Zhikang Niu, Yushen Chen, Ziyang Ma, Wenxi Chen, Nan Li, Chen Zhang, Xie Chen
Date: 2025-04-29 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

Flow matching has demonstrated strong generative capabilities and has become a core component in modern Text-to-Speech (TTS) systems. To ensure high-quality speech synthesis, Classifier-Free Guidance (CFG) is widely used during the inference of flow-matching-based TTS models. However, CFG incurs substantial computational cost as it requires two forward passes, which hinders its applicability in real-time scenarios. In this paper, we explore removing CFG from flow-matching-based TTS models to improve inference efficiency, while maintaining performance. Specifically, we reformulated the flow matching training target to directly approximate the CFG optimization trajectory. This training method eliminates the need for unconditional model evaluation and guided tuning during inference, effectively cutting the computational overhead in half. Furthermore, It can be seamlessly integrated with existing optimized sampling strategies. We validate our approach using the F5-TTS model on the LibriTTS dataset. Experimental results show that our method achieves a 9$\times$ inference speed-up compared to the baseline F5-TTS, while preserving comparable speech quality. We will release the code and models to support reproducibility and foster further research in this area.
Physics-Informed Neural Network-Driven Sparse Field Discretization Method for Near-Field Acoustic Holography
Authors: Xinmeng Luan, Mirco Pezzoli, Fabio Antonacci, Augusto Sarti
Date: 2025-05-01 00:00:00

We propose the Physics-Informed Neural Network-driven Sparse Field Discretization method (PINN-SFD), a novel self-supervised, physics-informed deep learning approach for addressing the Near-Field Acoustic Holography (NAH) problem. Unlike existing deep learning methods for NAH, which are predominantly supervised by large datasets, our approach does not require a training phase and it is physics-informed. The wave propagation field is discretized into sparse regions, a process referred to as field discretization, which includes a series of set of source planes, to address the inverse problem. Our method employs the discretized Kirchhoff-Helmholtz integral as the wave propagation model. By incorporating virtual planes, additional constraints are enforced near the actual sound source, improving the reconstruction process. Optimization is carried out using Physics-Informed Neural Networks (PINNs), where physics-based constraints are integrated into the loss functions to account for both direct (from equivalent source plane to hologram plane) and additional (from virtual planes to hologram plane) wave propagation paths. Additionally, sparsity is enforced on the velocity of the equivalent sources. Our comprehensive validation across various rectangular and violin top plates, covering a wide range of vibrational modes, demonstrates that PINN-SFD consistently outperforms the conventional Compressive-Equivalent Source Method (C-ESM), particularly in terms of reconstruction accuracy for complex vibrational patterns. Significantly, this method demonstrates reduced sensitivity to regularization parameters compared to C-ESM.
Perceptual Implications of Automatic Anonymization in Pathological Speech
Authors: Soroosh Tayebi Arasteh, Saba Afza, Tri-Thien Nguyen, Lukas Buess, Maryam Parvin, Tomas Arias-Vergara, Paula Andrea Perez-Toro, Hiu Ching Hung, Mahshad Lotfinia, Thomas Gorges, Elmar Noeth, Maria Schuster, Seung Hee Yang, Andreas Maier
Date: 2025-05-01 00:00:00
Tasks: Diagnostic

Automatic anonymization techniques are essential for ethical sharing of pathological speech data, yet their perceptual consequences remain understudied. This study presents the first comprehensive human-centered analysis of anonymized pathological speech, using a structured perceptual protocol involving ten native and non-native German listeners with diverse linguistic, clinical, and technical backgrounds. Listeners evaluated anonymized-original utterance pairs from 180 speakers spanning Cleft Lip and Palate, Dysarthria, Dysglossia, Dysphonia, and age-matched healthy controls. Speech was anonymized using state-of-the-art automatic methods (equal error rates in the range of 30-40%). Listeners completed Turing-style discrimination and quality rating tasks under zero-shot (single-exposure) and few-shot (repeated-exposure) conditions. Discrimination accuracy was high overall (91% zero-shot; 93% few-shot), but varied by disorder (repeated-measures ANOVA: p=0.007), ranging from 96% (Dysarthria) to 86% (Dysphonia). Anonymization consistently reduced perceived quality (from 83% to 59%, p<0.001), with pathology-specific degradation patterns (one-way ANOVA: p=0.005). Native listeners rated original speech slightly higher than non-native listeners (Delta=4%, p=0.199), but this difference nearly disappeared after anonymization (Delta=1%, p=0.724). No significant gender-based bias was observed. Critically, human perceptual outcomes did not correlate with automatic privacy or clinical utility metrics. These results underscore the need for listener-informed, disorder- and context-specific anonymization strategies that preserve privacy while maintaining interpretability, communicative functions, and diagnostic utility, especially for vulnerable populations such as children.