pwc-archive/papers-with-abstracts huggingface dataset:
Intensinist Social Welfare and Ordinal Intensity-Efficient Allocations
Authors: Georgios Gerasimou
Date: 2020-11-09 00:00:00

This paper studies social welfare and allocation efficiency in situations where, in addition to having ordinal preferences, agents also have *ordinal intensities*: they can make comparisons such as "I prefer a to b more than I prefer c to d" without necessarily being able to quantify them. In this new informational environment for social choice, the paper first introduces a rank-based criterion for interpersonal comparisons of such ordinal intensities. Building on it, the "intensinist" social welfare function is introduced. This maps profiles of ordinal intensities to weak orders over social alternatives using a scoring method which generalizes that of the classic Borda count in a way that allows for differences in agents' intensities to be reflected in preference aggregation more accurately and in a much richer class of situations. Building on the same comparability criterion, the paper also studies the classic assignment problem by defining an allocation to be "intensity-efficient" if it is Pareto efficient with respect to the preferences induced by the agents' intensities and also such that, when another allocation assigns the same pairs of items to the same pairs of agents but in a "flipped" way, the former allocation assigns the commonly preferred item in every such pair to the agent who prefers it more. Some first results on the (non-)existence of such allocations are presented without imposing restrictions on preferences or intensities other than strictness, and the relation -- or lack thereof -- is studied between intensity-efficient and classical utilitarian allocations.
Necessity of Hyperbolic Absolute Risk Aversion for the Concavity of Consumption Functions
Authors: Alexis Akira Toda
Date: 2020-09-28 00:00:00

Carroll and Kimball (1996) have shown that, in the class of utility functions that are strictly increasing, strictly concave, and have nonnegative third derivatives, hyperbolic absolute risk aversion (HARA) is sufficient for the concavity of consumption functions in general consumption-saving problems. This paper shows that HARA is necessary, implying the concavity of consumption is not a robust prediction outside the HARA class.
Screening and Information-Sharing Externalities
Authors: Quitzé Valenzuela-Stookey
Date: 2020-11-08 00:00:00

In many settings, multiple uninformed agents bargain simultaneously with a single informed agent in each of multiple periods. For example, workers and firms negotiate each year over salaries, and the firm has private information about the value of workers' output. I study the effects of transparency in these settings; uninformed agents may observe others' past bargaining outcomes, e.g. wages. I show that in equilibrium, each uninformed agent will choose in each period whether to try to separate the informed agent's types (screen) or receive the same outcome regardless of type (pool). In other words, the agents engage in a form of experimentation via their bargaining strategies. There are two main theoretical insights. First, there is a complementary screening effect: the more agents screen in equilibrium, the lower the information rents that each will have to pay. Second, the payoff of the informed agent will have a certain supermodularity property, which implies that equilibria with screening are "fragile" to deviations by uninformed agents. I apply the results to study pay-secrecy regulations and anti-discrimination policy. I show that, surprisingly, penalties for pay discrimination have no impact on bargaining outcomes. I discuss how this result depends on the legal framework for discrimination cases, and suggest changes to enhance the efficacy of anti-discrimination regulations. In particular, anti-discrimination law should preclude the so-called "salary negotiation defense".
Platform-Mediated Competition
Authors: Quitzé Valenzuela-Stookey
Date: 2020-11-08 00:00:00

Cross-group externalities and network effects in two-sided platform markets shape market structure and competition policy, and are the subject of extensive study. Less understood are the within-group externalities that arise when the platform designs many-to-many matchings: the value to agent $i$ of matching with agent $j$ may depend on the set of agents with which $j$ is matched. These effects are present in a wide range of settings in which firms compete for individuals' custom or attention. I characterize platform-optimal matchings in a general model of many-to-many matching with within-group externalities. I prove a set of comparative statics results for optimal matchings, and show how these can be used to analyze the welfare effects various changes, including vertical integration by the platform, horizontal mergers between firms on one side of the market, and changes in the platform's information structure. I then explore market structure and regulation in two in-depth applications. The first is monopolistic competition between firms on a retail platform such as Amazon. The second is a multi-channel video program distributor (MVPD) negotiating transfer fees with television channels and bundling these to sell to individuals.
Constrained Pseudo-market Equilibrium
Authors: Federico Echenique, Antonio Miralles, Jun Zhang
Date: 2019-09-12 00:00:00
Tasks: Diversity, Scheduling

We propose a pseudo-market solution to resource allocation problems subject to constraints. Our treatment of constraints is general: including bihierarchical constraints due to considerations of diversity in school choice, or scheduling in course allocation; and other forms of constraints needed to model, for example, the market for roommates, and combinatorial assignment problems. Constraints give rise to pecuniary externalities, which are internalized via prices. Agents pay to the extent that their purchases affect the value of relevant constraints at equilibrium prices. The result is a constrained efficient market equilibrium outcome. The outcome is fair whenever the constraints do not single out individual agents. Our result can be extended to economies with endowments, and address participation constraints.
Evolution of Risk-Taking Behaviour and Status Preferences in Anti-Coordination Games
Authors: Manuel Staab
Date: 2020-11-05 00:00:00

This paper analyses how risk-taking behaviour and preferences over consumption rank can emerge as a neutrally stable equilibrium when individuals face an anti-coordination task. If in an otherwise homogeneous society information about relative consumption becomes available, this cannot be ignored. Despite concavity in the objective function, stable types must be willing to accept risky gambles to differentiate themselves, and thus allow for coordination. Relative consumption acts as a form of costly communication. This suggests status preferences to be salient in settings where miscoordination is particularly costly.
Constrained Serial Rule on the Full Preference Domain
Authors: Priyanka Shende
Date: 2020-11-02 00:00:00
Tasks: Fairness

We study the problem of assigning objects to agents in the presence of arbitrary linear constraints when agents are allowed to be indifferent between objects. Our main contribution is the generalization of the (Extended) Probabilistic Serial mechanism via a new mechanism called the Constrained Serial Rule. This mechanism is computationally efficient and maintains desirable efficiency and fairness properties namely constrained ordinal efficiency and envy-freeness among agents of the same type. Our mechanism is based on a linear programming approach that accounts for all constraints and provides a re-interpretation of the bottleneck set of agents that form a crucial part of the Extended Probabilistic Serial mechanism.
The Frequency of Convergent Games under Best-Response Dynamics
Authors: Samuel C. Wiese, Torsten Heinrich
Date: 2020-11-02 00:00:00

Generating payoff matrices of normal-form games at random, we calculate the frequency of games with a unique pure strategy Nash equilibrium in the ensemble of $n$-player, $m$-strategy games. These are perfectly predictable as they must converge to the Nash equilibrium. We then consider a wider class of games that converge under a best-response dynamic, in which each player chooses their optimal pure strategy successively. We show that the frequency of convergent games goes to zero as the number of players or the number of strategies goes to infinity. In the $2$-player case, we show that for large games with at least $10$ strategies, convergent games with multiple pure strategy Nash equilibria are more likely than games with a unique Nash equilibrium. Our novel approach uses an $n$-partite graph to describe games.
Some game theoretic marketing attribution models
Authors: Elisenda Molina, Juan Tejada, Tom Weiss
Date: 2020-11-02 00:00:00
Tasks: Marketing

In this paper, we propose and analyse two game theoretical models useful to design marketing channels attribution mechanisms based on cooperative TU games and bankruptcy problems, respectively. First, we analyse the Sum Game, a coalitional game introduced by Morales (2016). We extend the ideas introduced in Zhao et al. (2018) and Cano-Berlanga et al. (2017) to the case in which the order and the repetition of channels on the paths to conversion are taken into account. In all studied cases, the Shapley value is proposed as the attribution mechanism. Second, a bankruptcy problem approach is proposed, and a similar analysis is developed relying on the Constrained Equal Loss (CEL) and Proportional (PROP) rules as attribution mechanisms. In particular, it is relevant to note that the class of attribution bankruptcy problems is a proper subclass of bankruptcy problems.
Reputation Building under Observational Learning
Authors: Harry Pei
Date: 2020-06-15 00:00:00

I study a social learning model in which the object to learn is a strategic player's endogenous actions rather than an exogenous state. A patient seller faces a sequence of buyers and decides whether to build a reputation for supplying high quality products. Each buyer does not have access to the seller's complete records, but can observe all previous buyers' actions, and some informative private signal about the seller's actions. I examine how the buyers' private signals affect the speed of social learning and the seller's incentives to establish reputations. When each buyer privately observes a bounded subset of the seller's past actions, the speed of learning is strictly positive but can vanish to zero as the seller becomes patient. As a result, reputation building can lead to low payoff for the patient seller and low social welfare. When each buyer observes an unboundedly informative private signal about the seller's current-period action, the speed of learning is uniformly bounded from below and a patient seller can secure high returns from building reputations. My results shed light on the effectiveness of various policies in accelerating social learning and encouraging sellers to establish good reputations.
Social networks, confirmation bias and shock elections
Authors: Edoardo Gallo, Alastair Langtry
Date: 2020-11-01 00:00:00

In recent years online social networks have become increasingly prominent in political campaigns and, concurrently, several countries have experienced shock election outcomes. This paper proposes a model that links these two phenomena. In our set-up, the process of learning from others on a network is influenced by confirmation bias, i.e. the tendency to ignore contrary evidence and interpret it as consistent with one's own belief. When agents pay enough attention to themselves, confirmation bias leads to slower learning in any symmetric network, and it increases polarization in society. We identify a subset of agents that become more/less influential with confirmation bias. The socially optimal network structure depends critically on the information available to the social planner. When she cannot observe agents' beliefs, the optimal network is symmetric, vertex-transitive and has no self-loops. We explore the implications of these results for electoral outcomes and media markets. Confirmation bias increases the likelihood of shock elections, and it pushes fringe media to take a more extreme ideology.
Robust perfect equilibrium in large games
Authors: Enxian Chen, Lei Qiao, Xiang Sun, Yeneng Sun
Date: 2019-12-30 00:00:00

This paper proposes a new equilibrium concept "robust perfect equilibrium" for non-cooperative games with a continuum of players, incorporating three types of perturbations. Such an equilibrium is shown to exist (in symmetric mixed strategies and in pure strategies) and satisfy the important properties of admissibility, aggregate robustness, and ex post robust perfection. These properties strengthen relevant equilibrium results in an extensive literature on strategic interactions among a large number of agents. Illustrative applications to congestion games and potential games are presented. In the particular case of a congestion game with strictly increasing cost functions, we show that there is a unique symmetric robust perfect equilibrium.
Informed Principal Problems in Bilateral Trading
Authors: Takeshi Nishimura
Date: 2019-06-25 00:00:00

We study bilateral trade with interdependent values as an informed-principal problem. The mechanism-selection game has multiple equilibria that differ with respect to principal's payoff and trading surplus. We characterize the equilibrium that is worst for every type of principal, and characterize the conditions under which there are no equilibria with different payoffs for the principal. We also show that this is the unique equilibrium that survives the intuitive criterion.
Debreu's open gap lemma for semiorders
Authors: A. Estevan
Date: 2020-10-01 00:00:00
Tasks: LEMMA

The problem of finding a (continuous) utility function for a semiorder has been studied since in 1956 R.D. Luce introduced in \emph{Econometrica} the notion. There was almost no results on the continuity of the representation. A similar result to Debreu's Lemma, but for semiorders, was never achieved. Recently, some necessary conditions for the existence of a continuous representation as well as some conjectures were presented by A. Estevan. In the present paper we prove these conjectures, achieving the desired version of Debreu's Open Gap Lemma for bounded semiorders. This result allows to remove the open-closed and closed-open gaps of a subset $S\subseteq \mathbb{R}$, but now keeping the constant threshold, so that $x+1<y$ if and only if $g(x)+1<g(y) \, (x,y\in S)$. Therefore, the continuous representation (in the sense of Scott-Suppes) of bounded semiorders is characterized. These results are achieved thanks to the key notion of $\epsilon$-continuity, which generalizes the idea of continuity for semiorders.
Libri-adhoc40: A dataset collected from synchronized ad-hoc microphone arrays
Authors: Shanzheng Guan, Shupei Liu, Junqi Chen, Wenbo Zhu, Shengqiang Li, Xu Tan, Ziye Yang, Menglong Xu, Yijiang Chen, Jianyu Wang, Xiao-Lei Zhang
Date: 2021-03-28 00:00:00
Tasks: speech-recognition, Speech Recognition

Recently, there is a research trend on ad-hoc microphone arrays. However, most research was conducted on simulated data. Although some data sets were collected with a small number of distributed devices, they were not synchronized which hinders the fundamental theoretical research to ad-hoc microphone arrays. To address this issue, this paper presents a synchronized speech corpus, named Libri-adhoc40, which collects the replayed Librispeech data from loudspeakers by ad-hoc microphone arrays of 40 strongly synchronized distributed nodes in a real office environment. Besides, to provide the evaluation target for speech frontend processing and other applications, we also recorded the replayed speech in an anechoic chamber. We trained several multi-device speech recognition systems on both the Libri-adhoc40 dataset and a simulated dataset. Experimental results demonstrate the validness of the proposed corpus which can be used as a benchmark to reflect the trend and difference of the models with different ad-hoc microphone arrays. The dataset is online available at https://github.com/ISmallFish/Libri-adhoc40.
A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection
Authors: Xin Wang, Junich Yamagishi
Date: 2021-03-21 00:00:00
Tasks: Synthetic Speech Detection

A great deal of recent research effort on speech spoofing countermeasures has been invested into back-end neural networks and training criteria. We contribute to this effort with a comparative perspective in this study. Our comparison of countermeasure models on the ASVspoof 2019 logical access task takes into account recently proposed margin-based training criteria, widely used front ends, and common strategies to deal with varied-length input trials. We also measured intra-model differences through multiple training-evaluation rounds with random initialization. Our statistical analysis demonstrates that the performance of the same model may be significantly different when just changing the random initial seed. Thus, we recommend similar analysis or multiple training-evaluation rounds for further research on the database. Despite the intra-model differences, we observed a few promising techniques such as the average pooling to process varied-length inputs and a new hyper-parameter-free loss function. The two techniques led to the best single model in our experiment, which achieved an equal error rate of 1.92% and was significantly different in statistical sense from most of the other experimental models.
AEC in a NetShell: On Target and Topology Choices for FCRN Acoustic Echo Cancellation
Authors: Jan Franzen, Ernst Seidel, Tim Fingscheidt
Date: 2021-03-16 00:00:00
Tasks: Acoustic echo cancellation

Acoustic echo cancellation (AEC) algorithms have a long-term steady role in signal processing, with approaches improving the performance of applications such as automotive hands-free systems, smart home and loudspeaker devices, or web conference systems. Just recently, very first deep neural network (DNN)-based approaches were proposed with a DNN for joint AEC and residual echo suppression (RES)/noise reduction, showing significant improvements in terms of echo suppression performance. Noise reduction algorithms, on the other hand, have enjoyed already a lot of attention with regard to DNN approaches, with the fully convolutional recurrent network (FCRN) architecture being among state of the art topologies. The recently published impressive echo cancellation performance of joint AEC/RES DNNs, however, so far came along with an undeniable impairment of speech quality. In this work we will heal this issue and significantly improve the near-end speech component quality over existing approaches. Also, we propose for the first time-to the best of our knowledge-a pure DNN AEC in the form of an echo estimator, that is based on a competitive FCRN structure and delivers a quality useful for practical applications.
Remote Monitoring of Patient Respiration with Mask Attachment -- A Pragmatic Solution for Medical Facilities
Authors: Vivian Ci Ai Koh, Yi Yang Ang, Wee Ser, Rex Xiao Tan
Date: 2021-03-16 00:00:00

Remote monitoring of vital signs in infectious patients minimizes the risks of viral transmissions to healthcare professionals. Evidence indicates that donning face masks reduces the risk of viral transmissions and is now the norm in medical facilities. We propose attaching an acoustic-sensing device onto face masks to assist medical facilities in monitoring patients' respiration remotely. Usability and functionality studies of the modified face mask were evaluated on 16 healthy participants, who were blindfolded throughout the data collection. Around half of the participants noticed the difference between the modified and unmodified masks but they also reported there was no discomfort in using the modified mask. Respiratory rates of the participants were evaluated for one minute and the mean error of respiratory rate was found to be 2.0 +/- 1.3 breath per minute. As all participants were healthy, the wheeze detection algorithm was assessed by playing 176 wheezes and 176 normal breaths through a foam mannequin. The recordings were played at three different times to account for varying environmental noise. The overall accuracy of the wheeze detection algorithm was 91.9%. The current findings support and suggest the use of the mask attachment in medical facilities.
Towards Robust Speaker Verification with Target Speaker Enhancement
Authors: Chunlei Zhang, Meng Yu, Chao Weng, Dong Yu
Date: 2021-03-16 00:00:00
Tasks: Speaker Verification, Speech Enhancement

This paper proposes the target speaker enhancement based speaker verification network (TASE-SVNet), an all neural model that couples target speaker enhancement and speaker embedding extraction for robust speaker verification (SV). Specifically, an enrollment speaker conditioned speech enhancement module is employed as the front-end for extracting target speaker from its mixture with interfering speakers and environmental noises. Compared with the conventional target speaker enhancement models, nontarget speaker/interference suppression should draw additional attention for SV. Therefore, an effective nontarget speaker sampling strategy is explored. To improve speaker embedding extraction with a light-weighted model, a teacher-student (T/S) training is proposed to distill speaker discriminative information from large models to small models. Iterative inference is investigated to address the noisy speaker enrollment problem. We evaluate the proposed method on two SV tasks, i.e., one heavily overlapped speech and the other one with comprehensive noise types in vehicle environments. Experiments show significant and consistent improvements in Equal Error Rate (EER) over the state-of-the-art baselines.
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning
Authors: Nilaksh Das, Sravan Bodapati, Monica Sunkara, Sundararajan Srinivasan, Duen Horng Chau
Date: 2021-03-10 00:00:00
Tasks: Accented Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Transfer Learning

Training deep neural networks for automatic speech recognition (ASR) requires large amounts of transcribed speech. This becomes a bottleneck for training robust models for accented speech which typically contains high variability in pronunciation and other semantics, since obtaining large amounts of annotated accented data is both tedious and costly. Often, we only have access to large amounts of unannotated speech from different accents. In this work, we leverage this unannotated data to provide semantic regularization to an ASR model that has been trained only on one accent, to improve its performance for multiple accents. We propose Accent Pre-Training (Acc-PT), a semi-supervised training strategy that combines transfer learning and adversarial training. Our approach improves the performance of a state-of-the-art ASR model by 33% on average over the baseline across multiple accents, training only on annotated samples from one standard accent, and as little as 105 minutes of unannotated speech from a target accent.