pwc-archive/papers-with-abstracts huggingface dataset:
Network Heterogeneity and Value of Information
Authors: Kota Murayama
Date: 2025-06-21 00:00:00

This paper studies how payoff heterogeneity affects the value of information in beauty contest games. I show that public information provision is detrimental to welfare if and only if agents' Katz-Bonacich centralities exhibit specific forms of heterogeneity, stemming from the network of coordination motives. A key insight is that agents may value the commonality of information so differently that some are harmed by their neighbors knowing what others know. Leveraging this insight, I also show that when the commonality of information is endogenously determined through information sharing, the equilibrium degree of information sharing can be inefficiently low, even without sharing costs.
Capturing Misalignment
Authors: Pierfrancesco Guarino, Gabriel Ziegler
Date: 2025-06-20 00:00:00

We introduce and formalize misalignment, a phenomenon of interactive environments perceived from an analyst's perspective where an agent holds beliefs about another agent's beliefs that do not correspond to the actual beliefs of the latter. We demonstrate that standard frameworks, such as type structures, fail to capture misalignment, necessitating new tools to analyze this phenomenon. To this end, we characterize misalignment through non-belief-closed state spaces and introduce agent-dependent type structures, which provide a flexible tool to understand the varying degrees of misalignment. Furthermore, we establish that appropriately adapted modal operators on agent-dependent type structures behave consistently with standard properties, enabling us to explore the implications of misalignment for interactive reasoning. Finally, we show how speculative trade can arise under misalignment, even when imposing the corresponding assumptions that rule out such trades in standard environments.
A House Monotone, Coherent, and Droop Proportional Ranked Candidate Voting Method
Authors: Ross Hyman
Date: 2025-06-14 00:00:00

A Ranked candidate voting method based on Phragmen's procedure is described that can be used to produce a top-down proportional candidate list. The method complies with the Droop proportionality criterion satisfied by Single Transferable Vote. It also complies with house monotonicity and coherence, which are the ranked-candidate analogs of the divisor methods properties of always avoiding the Alabama and New State paradoxes. The highest ranked candidate in the list is the Instant Runoff winner, which is in at least one Droop proportional set of N winners for all N.
Do You Know What I Mean? A Syntactic Representation for Differential Bounded Awareness
Authors: Ani Guerdjikova, Evan Piermont, John Quiggin
Date: 2025-06-20 00:00:00
Tasks: Translation

Without the assumption of complete, shared awareness, it is necessary to consider communication between agents who may entertain different representations of the world. A syntactic (language-based) approach provides powerful tools to address this problem. In this paper, we define translation operators between two languages which provide a ``best approximation'' for the meaning of propositions in the target language subject to its expressive power. We show that, in general, the translation operators preserve some, but not all, logical operations. We derive necessary and sufficient conditions for the existence of a joint state space and a joint language, in which the subjective state spaces of each agent, and their individual languages, may be embedded. This approach allows us to compare languages with respect to their expressiveness and thus, with respect to the properties of the associated state space.
AI Plays? δ-Rationality Games with Nash Equilibrium as Special Case
Authors: Fang-Fang Tang, Yongsheng Xu
Date: 2025-06-19 00:00:00

A distortion function, which captures the payoff gap between a player's actual payoff and her true payoff, is introduced and used to analyze games. In our proposed framework, we argue that players' actual payoff functions should be used to explain and predict their behaviors, while their true payoff functions should be used to conduct welfare analysis of the outcomes.
Two-Person Cooperative Games with delta-Rationality
Authors: Fang-Fang Tang, Yongsheng Xu
Date: 2025-06-19 00:00:00

A player's payoff is modeled as consisting of two parts: a rational-value part and a distortion-value part. It is argued that the (total) payoff function should be used to explain and predict the behaviors of the players, while the rational value function should be used to conduct welfare analysis of the final outcome. We use the Nash demand game to illustrate our model.
The Implementability of Liberalism
Authors: Héctor Hermida-Rivera
Date: 2025-06-19 00:00:00

This note shows that under the unrestricted domain, there exists a choice liberal and Nash implementable social choice rule if and only if there are at least three players and the outcome set is at least twice as large as the player set. A social choice rule is choice liberal if and only if for every player, there exists at least one pair of outcomes such that if this player strictly prefers one over the other, the one he prefers is socially desirable and the other one is not. A social choice rule is Nash implementable if and only if there exists a mechanism such that at every preference profile, the set of Nash equilibrium outcomes coincides with the set of socially desirable ones. The proof constructs an intuitive Nash implementing mechanism.
Learning in Random Utility Models Via Online Decision Problems
Authors: Emerson Melo
Date: 2025-06-19 00:00:00
Tasks: Decision Making

This paper examines the Random Utility Model (RUM) in repeated stochastic choice settings where decision-makers lack full information about payoffs. We propose a gradient-based learning algorithm that embeds RUM into an online decision-making framework. Our analysis establishes Hannan consistency for a broad class of RUMs, meaning the average regret relative to the best fixed action in hindsight vanishes over time. We also show that our algorithm is equivalent to the Follow-The-Regularized-Leader (FTRL) method, offering an economically grounded approach to online optimization. Applications include modeling recency bias and characterizing coarse correlated equilibria in normal-form games
Minimal Stable Voting Rules
Authors: Héctor Hermida-Rivera
Date: 2025-06-18 00:00:00

In this paper, I characterize minimal stable voting rules and minimal self-stable constitutions (i.e., pairs of voting rules) for societies in which only power matters. To do so, I first let players' preference profiles over voting rules satisfy four natural axioms commonly used in the analysis of power: non-dominance, anonymity, null player and swing player. I then provide simple notions of minimal stability and minimal self-stability, and show that the families of minimal stable voting rules and minimal self-stable constitutions are fairly small. Finally, I conclude that political parties have evolved to ensure the minimal self-stability of otherwise not minimal self-stable constitutions.
Self-Equivalent Voting Rules
Authors: Héctor Hermida-Rivera
Date: 2025-06-18 00:00:00

In this paper, I introduce a novel stability axiom for stochastic voting rules, called self-equivalence, by which a society considering whether to replace its voting rule using itself will choose not do so. I then show that under the unrestricted strict preference domain, a voting rule satisfying the democratic principles of anonymity, optimality, monotonicity, and neutrality is self-equivalent if and only if it assigns to every voter equal probability of being a dictator (i.e., uniform random dictatorship). Thus, any society that desires stability and adheres to the aforementioned democratic principles is bound to either employ the uniform random dictatorship or decide whether to change its voting rule using a voting rule other than itself.
Binary Self-Selective Voting Rules
Authors: Héctor Hermida-Rivera, Toygar T. Kerman
Date: 2025-06-18 00:00:00

This paper introduces a novel binary stability property for voting rules-called binary self-selectivity-by which a society considering whether to replace its voting rule using itself in pairwise elections will choose not to do so. In Theorem 1, we show that a neutral voting rule is binary self-selective if and only if it is universally self-selective. We then use this equivalence to show, in Corollary 1, that under the unrestricted strict preference domain, a unanimous and neutral voting rule is binary self-selective if and only if it is dictatorial. In Theorem 2 and Corollary 2, we show that whenever there is a strong Condorcet winner; a unanimous, neutral and anonymous voting rule is binary self-selective (or universally self-selective) if and only if it is the Condorcet voting rule.
Efficient reallocation of indivisible resources: Pair-efficiency versus Pareto-efficiency
Authors: Pinaki Mandal
Date: 2025-06-18 00:00:00

In the object reallocation problem, achieving Pareto-efficiency is desirable, but may be too demanding for implementation purposes. In contrast, pair-efficiency, which is the minimal efficiency requirement, is more suitable. Despite being a significant relaxation, however, pair-efficiency ensures Pareto-efficiency for any strategy-proof and individually rational rule when agents' preferences are unrestricted. What if agents' preferences have specific restricted structures, such as single-peakedness or single-dippedness? We often encounter such situations in real-world scenarios. This study aims to investigate whether pair-efficiency is sufficient to ensure Pareto-efficiency in such cases. Our main contribution in this paper is establishing the equivalence between pair-efficiency and Pareto-efficiency when dealing with single-peaked or single-dipped preference profiles. This equivalence holds without needing to assume any other properties of the rule. We further show that both the single-peaked domain and the single-dipped domain are the "maximal" domains where this equivalence holds.
Upstream competition and exclusive content provision in media markets
Authors: Kiho Yoon
Date: 2025-06-18 00:00:00

With a multilateral vertical contracting model of media markets, we examine upstream competition and contractual arrangements in content provision. We analyze the trade of content by the Nash bargaining solution and the downstream competition by the Hotelling location model. We characterize the equilibrium outcomes and the contractual arrangements for various vertical structures. We show that the possibility of exclusive contracts rises when the value of the premium content increases, the degree of horizontal differentiation in the downstream market decreases, the importance of advertising revenue decreases, and the relative bargaining power of upstream firm decreases.
Smart contracts and reaction-function games
Authors: Jens Gudmundsson, Jens Leth Hougaard
Date: 2025-06-17 00:00:00

Blockchain-based smart contracts offer a new take on credible commitment, where players can commit to actions in reaction to actions of others. Such reaction-function games extend on strategic games with players choosing reaction functions instead of strategies. We formalize a solution concept in terms of fixed points for such games, akin to Nash equilibrium, and prove equilibrium existence. Reaction functions can mimic "trigger" strategies from folk theorems on infinitely repeated games -- but now in a one-shot setting. We introduce a refinement in terms of safe play. We apply our theoretical framework to symmetric investment games, which includes two prominent classes of games, namely weakest-link and public-good games. In both cases, we identify a safe and optimal reaction function. In this way, our findings highlight how blockchain-based commitment can overcome trust and free-riding barriers.
Who and How? Adverse Selection and flexible Moral Hazard
Authors: Henrique Castro-Pires, Deniz Kattwinkel, Jan Knoepfle
Date: 2025-06-15 00:00:00

We characterize the set of incentive compatible mechanisms in problems with hidden productivity types and flexible hidden actions. We demonstrate the tractability of the characterization with applications.
Selling Certification, Content Moderation, and Attention
Authors: Heski Bar-Isaac, Rahul Deb, Matthew Mitchell
Date: 2025-06-14 00:00:00

We introduce a model of content moderation for sale, where a platform can channel attention in two ways: direct steering that makes content visible to consumers and certification that controls what consumers know about the content. The platform optimally price discriminates using both instruments. Content from higher willingness-to-pay providers enjoys higher quality certification and more views. The platform cross-subsidizes content: the same certificate is assigned to content from low willingness-to-pay providers that appeals to consumers and content from higher willingness-to-pay providers that does not. Cross-subsidization can benefit consumers by making content more diverse; regulation enforcing accurate certification may be harmful.
Who's in? Household-targeted Government Policies and the Role of Financial Literacy in Market Participation
Authors: Maria Elena Filippin
Date: 2025-06-14 00:00:00

This paper examines how household-targeted government policies influence financial market participation conditional on financial literacy, focusing on potential Central Bank Digital Currency (CBDC) adoption. Due to the lack of empirical CBDC data, I use the introduction of retail Treasury bonds in Italy as a proxy to investigate how financial literacy affects households' likelihood to engage with the new instrument. Using the Bank of Italy's Survey on Household Income and Wealth, I explore how financial literacy influenced households' participation in the Treasury bond market following the 2012 introduction of retail Treasury bonds, showing that households with some but low financial literacy are more likely to participate than other household groups. Based on these findings, I develop a theoretical model to explore the potential implications of financial literacy for CBDC adoption, showing that low-literate households with limited access to risky assets allocate more wealth to CBDC, while high-literate households use risky assets to safeguard against income risk. These results highlight the role of financial literacy in shaping portfolio choices and CBDC adoption.
Artificial Intelligence in Team Dynamics: Who Gets Replaced and Why?
Authors: Xienan Cheng, Mustafa Dogan, Pinar Yildirim
Date: 2025-06-14 00:00:00

This study investigates the effects of artificial intelligence (AI) adoption in organizations. We ask: (1) How should a principal optimally deploy limited AI resources to replace workers in a team? (2) In a sequential workflow, which workers face the highest risk of AI replacement? (3) How does substitution with AI affect both the replaced and non-replaced workers' wages? We develop a sequential team production model in which a principal can use peer monitoring -- where each worker observes the effort of their predecessor -- to discipline team members. The principal may replace some workers with AI agents, whose actions are not subject to moral hazard. Our analysis yields four key results. First, the optimal AI strategy involves the stochastic use of AI to replace workers. Second, the principal replaces workers at the beginning and at the end of the workflow, but does not replace the middle worker, since this worker is crucial for sustaining the flow of information obtained by peer monitoring. Third, the principal may choose not to fully exhaust the AI capacity at her discretion. Fourth, the optimal AI adoption increases average wages and reduces intra-team wage inequality.
Nondistortionary belief elicitation
Authors: Marcin Pęski, Colin Stewart
Date: 2025-06-13 00:00:00

A researcher wants to ask a decision-maker about a belief related to a choice the decision-maker made; examples include eliciting confidence or cognitive uncertainty. When can the researcher provide incentives for the decision-maker to report her belief truthfully without distorting her choice? We identify necessary and sufficient conditions for nondistortionary elicitation and fully characterize all incentivizable questions in three canonical classes of problems. For these problems, we show how to elicit beliefs using variants of the Becker-DeGroot-Marschak mechanism.
What Pareto-Efficiency Adjustments Cannot Fix
Authors: Josue Ortega, Gabriel Ziegler, R. Pablo Arribillaga, Geng Zhao
Date: 2025-06-13 00:00:00

The Deferred Acceptance (DA) algorithm is stable and strategy-proof, but can produce outcomes that are Pareto-inefficient for students, and thus several alternative mechanisms have been proposed to correct this inefficiency. However, we show that these mechanisms cannot correct DA's rank-inefficiency and inequality, because these shortcomings can arise even in cases where DA is Pareto-efficient. We also examine students' segregation in settings with advantaged and marginalized students. We prove that the demographic composition of every school is perfectly preserved under any Pareto-efficient mechanism that dominates DA, and consequently fully segregated schools under DA maintain their extreme homogeneity.
Rethinking Competition as a Non-Beneficial Mechanism in Economic Systems
Authors: Marcelo S. Tedesco, Gonzalo Marquez
Date: 2025-06-13 00:00:00

Persistent economic competition is often justified as a mechanism of innovation, efficiency, and welfare maximization. Yet empirical evidence across disciplines reveals that competition systematically generates fragility, inequality, and ecological degradation, emergent outcomes not of isolated failures but of underlying systemic dynamics. This work reconceptualizes economic ecosystems as real complex adaptive systems, structurally isomorphic with biological and social ecosystems. Integrating complexity science, evolutionary biology, ecology, and economic and business theory, we classify economic interactions according to their systemic effects and propose a theoretical model of ecosystemic equilibrium based on the predominance of beneficial versus non-beneficial relationships. Recognizing economies as ecologically embedded and structurally interdependent systems provides a novel framework for analyzing systemic resilience, reframing competition as a non-beneficial mechanism.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4
Authors: Jongyeon Park, Joonhee Lee, Do-Hyeon Lim, Hong Kook Kim, Hyeongcheol Geum, Jeong Eun Lim
Date: 2025-06-26 00:00:00
Tasks: Audio Tagging, Semantic Segmentation

This technical report presents submission systems for Task 4 of the DCASE 2025 Challenge. This model incorporates additional audio features (spectral roll-off and chroma features) into the embedding feature extracted from the mel-spectral feature to im-prove the classification capabilities of an audio-tagging model in the spatial semantic segmentation of sound scenes (S5) system. This approach is motivated by the fact that mixed audio often contains subtle cues that are difficult to capture with mel-spectrograms alone. Thus, these additional features offer alterna-tive perspectives for the model. Second, an agent-based label correction system is applied to the outputs processed by the S5 system. This system reduces false positives, improving the final class-aware signal-to-distortion ratio improvement (CA-SDRi) metric. Finally, we refine the training dataset to enhance the classi-fication accuracy of low-performing classes by removing irrele-vant samples and incorporating external data. That is, audio mix-tures are generated from a limited number of data points; thus, even a small number of out-of-class data points could degrade model performance. The experiments demonstrate that the submit-ted systems employing these approaches relatively improve CA-SDRi by up to 14.7% compared to the baseline of DCASE 2025 Challenge Task 4.
Post-training for Deepfake Speech Detection
Authors: Wanying Ge, Xin Wang, Xuechen Liu, Junichi Yamagishi
Date: 2025-06-26 00:00:00
Tasks: Face Swapping, Self-Supervised Learning

We introduce a post-training approach that adapts self-supervised learning (SSL) models for deepfake speech detection by bridging the gap between general pre-training and domain-specific fine-tuning. We present AntiDeepfake models, a series of post-trained models developed using a large-scale multilingual speech dataset containing over 56,000 hours of genuine speech and 18,000 hours of speech with various artifacts in over one hundred languages. Experimental results show that the post-trained models already exhibit strong robustness and generalization to unseen deepfake speech. When they are further fine-tuned on the Deepfake-Eval-2024 dataset, these models consistently surpass existing state-of-the-art detectors that do not leverage post-training. Model checkpoints and source code are available online.
Improved Topology-Independent Distributed Adaptive Node-Specific Signal Estimation for Wireless Acoustic Sensor Networks
Authors: Paul Didier, Toon van Waterschoot, Simon Doclo, Jörg Bitzer, Marc Moonen
Date: 2025-06-24 00:00:00

This paper addresses the challenge of topology-independent (TI) distributed adaptive node-specific signal estimation (DANSE) in wireless acoustic sensor networks (WASNs) where sensor nodes exchange only fused versions of their local signals. An algorithm named TI-DANSE has previously been presented to handle non-fully connected WASNs. However, its slow iterative convergence towards the optimal solution limits its applicability. To address this, we propose in this paper the TI-DANSE+ algorithm. At each iteration in TI-DANSE+, the node set to update its local parameters is allowed to exploit each individual partial in-network sums transmitted by its neighbors in its local estimation problem, increasing the available degrees of freedom and accelerating convergence with respect to TI-DANSE. Additionally, a tree-pruning strategy is proposed to further increase convergence speed. TI-DANSE+ converges as fast as the DANSE algorithm in fully connected WASNs while reducing transmit power usage. The convergence properties of TI-DANSE+ are demonstrated in numerical simulations.
JCAPT: A Joint Modeling Approach for CAPT
Authors: Tzu-Hsuan Yang, Yue-Yang He, Berlin Chen
Date: 2025-06-24 00:00:00
Tasks: Mamba

Effective pronunciation feedback is critical in second language (L2) learning, for which computer-assisted pronunciation training (CAPT) systems often encompass two key tasks: automatic pronunciation assessment (APA) and mispronunciation detection and diagnosis (MDD). Recent work has shown that joint modeling of these two tasks can yield mutual benefits. Our unified framework leverages Mamba, a selective state space model (SSM), while integrating phonological features and think token strategies to jointly enhance interpretability and fine-grained temporal reasoning in APA and MDD. To our knowledge, this is the first study to combine phonological attribution, SSM-based modeling, and prompting in CAPT. A series of experiments conducted on the speechocean762 benchmark demonstrate that our model consistently outperforms prior methods, particularly on the MDD task.