pwc-archive/papers-with-abstracts huggingface dataset:
Beyond Credibility: Understanding the Mediators Between Electronic Word-of-Mouth and Purchase Intention
Authors: Anastasiei Bogdan, Nicoleta Dospinescu, Octavian Dospinescu
Date: 2025-04-07 00:00:00

This study examines the mediating role of five variables in the relationship between eWOM credibility and purchase intention. Data were collected via an online survey of 359 Romanian participants and analyzed using Structural Equation Modeling. The results show that perceived product quality, emotional response to eWOM and perceived buying risk fully mediate this relationship, while brand trust and brand engagement do not. These findings suggest that effective eWOM communication should enhance quality perceptions, evoke positive emotions, and boost customer confidence by reducing perceived purchasing risks.
Optimal macroprudential policy with preemptive bailouts
Authors: Aliaksandr Zaretski
Date: 2025-04-06 00:00:00

I study the optimal regulation of a financial sector where individual banks face self-enforcing constraints countering their default incentives. The constrained-efficient social planner can improve over the unregulated equilibrium in two dimensions. First, by internalizing the impact of banks' portfolio decisions on the prices of assets and liabilities that affect the enforcement constraints. Second, by redistributing future net worth from new entrants to surviving banks, which increases the current forward-looking value of all banks, relaxing their enforcement constraints and decreasing the probability of banking crises. The latter can be accomplished with systemic preemptive bailouts that are time consistent and unambiguously welfare improving. Unregulated banks can be both overleveraged and underleveraged depending on the state of the economy, thus macroprudential policy requires both taxes and subsidies, while minimum bank capital requirements are generally ineffective.
Sustainable Aviation Fuels: Opportunities, Alternatives and Challenges for Decarbonizing the Aviation Industry and Foster the Renewable Chemicals
Authors: Wesley Bonicontro Ambrosio, Bruna Araújo de Sousa, João Marcos Kanieski, Priscila Marchiorie, Gustavo Mockaitis
Date: 2025-04-04 00:00:00

Sustainable Aviation Fuels (SAF) are pivotal in the global effort to decarbonize the aviation sector and meet greenhouse gas (GHG) reduction targets established by international frameworks such as CORSIA and Brazil ProBioQAV. This study evaluates SAF potential to reduce lifecycle carbon emissions by up to 80% while being compatible with existing aviation infrastructure. Through bibliometric analysis, scenario evaluation, legal and regulatory framework analysis and economic modeling, the research examines two key SAF production technologies: Hydroprocessed Esters and Fatty Acids Synthetic Paraffinic Kerosene (HEFA-SPK) and Alcohol-to-Jet (ATJ) pathways in the Brazilian context. The findings reveal significant economic challenges, particularly high feedstock and production costs, which hinder SAF competitiveness with fossil fuels at recent and current market prices in Brazil, leading to the analysis of potential incentives and commercial conditions aiming to increase economic attractiveness of SAF production. Based on interviews with relevant stakeholders and decision makers in the industry, scenarios incorporating tax incentives, carbon credits, capital grants, and premium pricing for SAF and its biogenic by-products demonstrate that combined policy interventions and commercial arrangements, along with a regulated Carbon Market are essential for SAF economic viability. Future research is suggested to look at regional assessments of feedstock availability, supply chain logistics, and global market eligibility. This research provides insights for guiding public policy and private investment to support the transition to sustainable aviation in Brazil and beyond.
Who Gets to Come In? How Political Engagement Shapes Views on Legal Immigration
Authors: Muhammad Hassan Bin Afzal, Foluke Omosun
Date: 2025-04-02 00:00:00

This study examines how political engagement shapes public attitudes toward legal immigration in the United States. Using nationally weighted data from the 2024 ANES Pilot Study, we construct a novel Political Engagement Index (PAX) based on five civic actions: discussing politics, online sharing, attending rallies, wearing political symbols, and campaign volunteering. By applying weighted ordered logistic regression models, we find that higher engagement predicts greater support for easing legal immigration, even after adjusting for education, gender, age, partisanship, income, urban residence, and generalized social trust. To capture the substantive effect, we visualize predicted probabilities across levels of engagement. In full-sample models, the likelihood of supporting "a lot harder" immigration drops from 26% to 13% as engagement rises, while support for "a lot easier" increases from 10% to 21%. Subgroup analyses by partisanship show consistent directionality, with notable shifts among Republicans. Social trust and education are also consistently associated with more open attitudes, while older respondents tend to support less lenient pathways to legal immigration policies. These findings suggest that a cumulative increase in political participation is linked to support for legal immigration pathways, with varying intensity across partisan identities and socio-demographic characteristics.
Robust Social Planning
Authors: Florian Mudekereza
Date: 2025-04-10 00:00:00
Tasks: Model Selection

This paper analyzes a society composed of individuals who have diverse sets of beliefs (or models) and diverse tastes (or utility functions). It characterizes the model selection process of a social planner who wishes to aggregate individuals' beliefs and tastes but is concerned that their beliefs are misspecified (or distorted). A novel impossibility result emerges: a utilitarian social planner who seeks robustness to misspecification never aggregates individuals' beliefs but instead behaves systematically as a dictator by selecting a single individual's belief. This tension between robustness and aggregation exists because aggregation yields policy-contingent beliefs, which are very sensitive to policy outcomes. Restoring possibility of belief aggregation requires individuals to have heterogeneous tastes and some common beliefs. This analysis reveals that misspecification has significant economic implications for welfare aggregation. These implications are illustrated in treatment choice, asset pricing, and dynamic macroeconomics.
A Game Theoretic Treatment of Contagion in Trade Networks
Authors: John S. McAlister, Jesse L. Brunner, Danielle J. Galvin, Nina H. Fefferman
Date: 2025-04-09 00:00:00

Global trade of material goods involves the potential to create pathways for the spread of infectious pathogens. One trade sector in which this synergy is clearly critical is that of wildlife trade networks. This highly complex system involves important and understudied bidirectional coupling between the economic decision making of the stakeholders and the contagion dynamics on the emergent trade network. While each of these components are independently well studied, there is a meaningful gap in understanding the feedback dynamics that can arise between them. In the present study, we describe a general game theoretic model for trade networks of goods susceptible to contagion. The primary result relies on the acyclic nature of the trade network and shows that, through the course of trading with stochastic infections, the probability of infection converges to a directly computable fixed point. This allows us to compute best responses and thus identify equilibria in the game. We present ways to use this model to describe and evaluate trade networks in terms of global and individual risk of infection under a wide variety of structural or individual modifications to the trade network. In capturing the bidirectional coupling of the system, we provide critical insight into the global and individual drivers and consequences for risks of infection inherent in and arising from the global wildlife trade, and any economic trade network with associated contagion risks.
More connection, less community: network formation and local public goods provision
Authors: Alastair Langtry
Date: 2025-04-09 00:00:00

This paper presents a model of network formation and public goods provision in local communities. Here, networks can sustain public good provision by spreading information about people's behaviour. I find a critical threshold in network connectedness at which public good provision drops sharply, even though agents are highly heterogeneous. Technology change can tear a community's social fabric by pushing high-skilled workers to withdraw from their local community. This can help explain rising resentment toward perceived ``elites'' -- their withdrawal actively harms those left behind. Moreover, well-meaning policies that upskill workers can make them worse off by reducing network connectedness.
Ranking alternatives from opinions on criteria
Authors: Takahiro Suzuki, Stefano Moretti, Michele Aleandri
Date: 2025-04-09 00:00:00
Tasks: Decision Making

A primary challenge in collective decision-making is that achieving unanimous agreement is difficult, even at the level of criteria. The history of social choice theory illustrates this: numerous normative criteria on voting rules have been proposed; however, disagreements persist regarding which criteria should take precedence. This study addresses the problem of ranking alternatives based on the aggregation of opinions over criteria that the alternatives might fulfill. Using the opinion aggregation model, we propose a new rule, termed the Intersection Initial Segment (IIS) rule, and characterize it using five axioms: neutrality, independence of the worst set, independence of the best set, weak intersection very important player, and independence of non-unanimous improvement. We illustrate our approach on a running example where the objective is to rank voting rules, showing that our opinion aggregation model is particularly well-suited to this context, and that the IIS rule is a counterpart to the method discussed in Nurmi's paper (2015).
On the merit principle in strategic exchange
Authors: Peng Liu, Huaxia Zeng
Date: 2025-04-08 00:00:00
Tasks: Fairness

New fairness notions in align with the merit principle are proposed for designing exchange rules. We show that, for an obviously strategy-proof, efficient and individually rational rule, an upper bound of fairness attainable is that, if two agents possess objects considered the best by all others divided into two equal groups, then at least one receives her favorite object. Notably, it is not possible to guarantee them both having favorites. Our results thus indicate an unambiguous trade-off between incentives and fairness in the design of exchange rules.
Network Effects of Tariffs
Authors: Paolo Pin
Date: 2025-04-07 00:00:00

We develop a model in which country-specific tariffs shape trade flows, prices, and welfare in a global economy with one homogeneous good. Trade flows form a Directed Acyclic Graph (DAG), and tariffs influence not only market outcomes but also the structure of the global trade network. A numerical example illustrates how tariffs may eliminate targeted imports, divert trade flows toward third markets, expose domestic firms to intensified foreign competition abroad, reduce consumer welfare, and ultimately harm the country imposing the tariff.
Verifiable affirmative action in Chinese high school admissions
Authors: Xinquan Hu, Jun Zhang
Date: 2025-04-07 00:00:00

We study the affirmative action policy in Chinese high school admissions, where high schools reserve a proportion of their quotas for graduates of low-performing middle schools. In line with the tradition of accountability, Chinese policymakers aim to ensure a verifiable allocation of reserved seats. We recognize the verifiability of widely used sequential mechanisms, which allocate reserved and open seats in separate stages. However, these mechanisms suffer from strategic complexity, leading to inefficiency and unfairness in the outcome. We then formalize a concept of verifiability for simultaneous mechanisms, which allocate all seats in one stage. We prove that a mechanism is individually rational, strategy-proof, and verifiable if and only if it is the deferred acceptance mechanism associated with one of two choice rules we characterize. We recommend a specific choice rule and mechanism, and discuss their benefits.
Competitive Information Disclosure with Heterogeneous Consumer Search
Authors: Dongjin Hwang, Ilwoo Hwang
Date: 2025-04-07 00:00:00
Tasks: Informativeness

We study a model of competitive information design in an oligopoly search market with heterogeneous consumer search costs. A unique class of equilibria -- upper-censorship equilibria -- emerges under intense competition. In equilibrium, firms balance competitive pressure with local monopoly power granted by search frictions. Notably, firms disclose only partial information even as the number of firms approaches infinity. The maximal informativeness of equilibrium decreases under first-order shifts in the search cost distribution, but varies non-monotonically under mean-preserving spreads. The model converges to the full-disclosure benchmark as search frictions vanish, and to the no-disclosure benchmark as search costs become homogeneous.
Preferences with Multiple Forecasts
Authors: Kensei Nakamura, Shohei Yanagita
Date: 2025-04-06 00:00:00
Tasks: Decision Making

When a collective decision maker presents a menu of uncertain prospects to her group members, each member's choice depends on their predictions about payoff-relevant states. In reality, however, these members hold different predictions; more precisely, they have different prior beliefs about states and predictions about the information they will receive. In this paper, we develop an axiomatic framework to examine collective decision making under such disagreements. First, we characterize two classes of representations: Bewley multiple learning (BML) representations, which are unanimity rules among predictions, and justifiable multiple learning (JML) representations, where a single prediction has veto power. Furthermore, we characterize a general class of representations called hierarchical multiple learning representations, which includes BML and JML representations as special cases. Finally, motivated by the fact that these representations violate completeness or intransitivity due to multiple predictions, we propose a rationalization procedure for constructing complete and transitive preferences from them.
Uncertainty, Single Crossing Property, and Stochastic Choice Data
Authors: Tanay Raj Bhatt
Date: 2025-04-04 00:00:00

In a typical model of private information and choice under uncertainty, a decision maker observes a signal, updates her prior beliefs using Bayes rule, and maximizes her expected utility. If the decision maker's utility function satisfies the single crossing property, and the information structure is ordered according to the monotone likelihood ratio, then the comparative statics exhibit monotonicity with respect to signals. We consider the restrictions placed by this model of signal processing on state conditional stochastic choice data. In particular, we show that this model rationalizes a state conditional stochastic choice dataset if and only if the dataset itself is ordered according to the monotone likelihood ratio.
Renewable Natural Resources with Tipping Points
Authors: Ted To
Date: 2025-04-02 00:00:00

Many of the world's renewable resources are in decline. Optimal harvests with smooth recruitment is well studied but in recent years, ecologists have concluded that tipping points in recruitment are common. Recruitment with a tipping point has low-fecundity below the tipping point and high-fecundity above. When the incremental value of high-fecundity is sufficiently high, there is a high-fecundity steady-state. This steady-state is stable but in some cases, small perturbations may result in large, temporary reductions in recruitment and harvests. Below the tipping point, a low-fecundity steady-state need not exist. When a low-fecundity steady-state does exist, there is an endogenous tipping (Skiba) point: below, harvests converge to the low-fecundity steady-state and above, an austere harvest policy transitions the renewable resource to high-fecundity recruitment. If there is hysteresis in recruitment, the high steady-state may not be stable. Moreover, if the high-/low-fecundity differential is large then following a downward perturbation, fecundity optimally remains low.
Categorical Unsupervised Variational Acoustic Clustering
Authors: Luan Vinícius Fiorio, Ivana Nikoloska, Ronald M. Aarts
Date: 2025-04-10 00:00:00
Tasks: Clustering

We propose a categorical approach for unsupervised variational acoustic clustering of audio data in the time-frequency domain. The consideration of a categorical distribution enforces sharper clustering even when data points strongly overlap in time and frequency, which is the case for most datasets of urban acoustic scenes. To this end, we use a Gumbel-Softmax distribution as a soft approximation to the categorical distribution, allowing for training via backpropagation. In this settings, the softmax temperature serves as the main mechanism to tune clustering performance. The results show that the proposed model can obtain impressive clustering performance for all considered datasets, even when data points strongly overlap in time and frequency.
Visual-Aware Speech Recognition for Noisy Scenarios
Authors: Lakshmipathi Balaji, Karan Singla
Date: 2025-04-09 00:00:00
Tasks: Audio-Visual Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Visual Speech Recognition

Humans have the ability to utilize visual cues, such as lip movements and visual scenes, to enhance auditory perception, particularly in noisy environments. However, current Automatic Speech Recognition (ASR) or Audio-Visual Speech Recognition (AVSR) models often struggle in noisy scenarios. To solve this task, we propose a model that improves transcription by correlating noise sources to visual cues. Unlike works that rely on lip motion and require the speaker's visibility, we exploit broader visual information from the environment. This allows our model to naturally filter speech from noise and improve transcription, much like humans do in noisy scenarios. Our method re-purposes pretrained speech and visual encoders, linking them with multi-headed attention. This approach enables the transcription of speech and the prediction of noise labels in video inputs. We introduce a scalable pipeline to develop audio-visual datasets, where visual cues correlate to noise in the audio. We show significant improvements over existing audio-only models in noisy scenarios. Results also highlight that visual cues play a vital role in improved transcription accuracy.
Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches
Authors: Eloi Moliner, Michal Švento, Alec Wright, Lauri Juvela, Pavel Rajmic, Vesa Välimäki
Date: 2025-04-07 00:00:00

Accurately estimating nonlinear audio effects without access to paired input-output signals remains a challenging problem.This work studies unsupervised probabilistic approaches for solving this task. We introduce a method, novel for this application, based on diffusion generative models for blind system identification, enabling the estimation of unknown nonlinear effects using black- and gray-box models. This study compares this method with a previously proposed adversarial approach, analyzing the performance of both methods under different parameterizations of the effect operator and varying lengths of available effected recordings.Through experiments on guitar distortion effects, we show that the diffusion-based approach provides more stable results and is less sensitive to data availability, while the adversarial approach is superior at estimating more pronounced distortion effects. Our findings contribute to the robust unsupervised blind estimation of audio effects, demonstrating the potential of diffusion models for system identification in music technology.
Bridging the Gap between Continuous and Informative Discrete Representations by Random Product Quantization
Authors: Xueqing Li, Zehan Li, Boyu Zhu, Ruihao Jing, Jian Kang, Jie Li, Xiao-Lei Zhang, Xuelong Li
Date: 2025-04-07 00:00:00
Tasks: Quantization, Self-Supervised Learning

Self-supervised learning has become a core technique in speech processing, but the high dimensionality of its representations makes discretization essential for improving efficiency. However, existing discretization methods still suffer from significant information loss, resulting in a notable performance gap compared to continuous representations. To overcome these limitations, we propose two quantization-based discretization methods: Product Quantization (PQ) and Random Product Quantization (RPQ). PQ partitions the original feature space into multiple subspaces and independently quantizes each sub-vector, producing a fused set of discrete units that retain diverse information from different subspaces, thus mitigating the loss associated with single-cluster quantization. RPQ further enhances representation diversity by randomly sampling a fixed proportion of feature dimensions multiple times to construct sub-vectors, thereby better capturing the variability in the data distribution. Theoretical analysis shows that RPQ reduces the correlation coefficient rho (where 0 <= rho <= 1) between sub-quantizers. Its quantization error is lower-bounded by the product of rho and epsilon-kms, where epsilon-kms denotes the quantization error of a single K-means quantizer. Experimental results on a combined dataset built from LibriSpeech and ML-SUPERB show that PQ and RPQ outperform standard K-means discretization, achieving relative improvements of 21.8 percent and 20.0 percent in WER on LibriSpeech, and 24.1 percent and 19.6 percent in CER on ML-SUPERB, respectively. Moreover, their performance is competitive with, and in some cases even surpasses, that of continuous SSL representations.
Trainable Adaptive Score Normalization for Automatic Speaker Verification
Authors: Jeong-Hwan Choi, Ju-Seok Seong, Ye-Rin Jeoung, Joon-Hyuk Chang
Date: 2025-04-06 00:00:00
Tasks: Speaker Verification

Adaptive S-norm (AS-norm) calibrates automatic speaker verification (ASV) scores by normalizing them utilize the scores of impostors which are similar to the input speaker. However, AS-norm does not involve any learning process, limiting its ability to provide appropriate regularization strength for various evaluation utterances. To address this limitation, we propose a trainable AS-norm (TAS-norm) that leverages learnable impostor embeddings (LIEs), which are used to compose the cohort. These LIEs are initialized to represent each speaker in a training dataset consisting of impostor speakers. Subsequently, LIEs are fine-tuned by simulating an ASV evaluation. We utilize a margin penalty during top-scoring IEs selection in fine-tuning to prevent non-impostor speakers from being selected. In our experiments with ECAPA-TDNN, the proposed TAS-norm observed 4.11% and 10.62% relative improvement in equal error rate and minimum detection cost function, respectively, on VoxCeleb1-O trial compared with standard AS-norm without using proposed LIEs. We further validated the effectiveness of the TAS-norm on additional ASV datasets comprising Persian and Chinese, demonstrating its robustness across different languages.