Wage Bargaining under the National Labor Relations Act

Sections 8(a)(3) and 8(a)(5) of the National Labor Relations Act prevent a firm from unilaterally increasing the wage it pays the union during the negotiation of a new wage contract. To understand this regulation, we study a counterfactual negotiation model where the firm can temporarily increase compensation to its employees during wage negotiations. Comparing this to the case where the firm does not have this option, we show that the firm may strategically increase the union's temporary wage to upset the union's incentive to strike, decreasing the union's bargaining power, and shrinking the set of permanent wage contracts that may arise in a perfect equilibrium. As the union becomes more patient, the best possible equilibrium contract to the union gets worse. In the limit, the uniqueness and hence the full efficiency of the perfect equilibrium are restored. We also demonstrate that allowing the union to refuse the firm's temporary compensation does not affect the set of perfect equilibrium outcomesBargaining, Negotiation, Good Faith Bargaining

A Revelation Principle for Dominant Strategy Implementation

We introduce a perfect price discriminating (PPD) mechanism for allocation problems with private information. A PPD mechanism treats a seller, for example, as a perfect price discriminating monopolist who faces a price schedule that does not depend on her report. In any PPD mechanism, every player has a dominant strategy to truthfully report her private information. We establish a revelation principle for dominant strategy implementation: any outcome that can be dominant strategy implemented can also be dominant strategy implemented using a PPD mechanism. We apply this principle to derive the optimal, budget-balanced, dominant strategy mechanisms for public good provision and bilateral bargaining

Comparing Emergency Department Resident and Patient Perspectives on Costs in Emergency Care

Objectives: Costs of care are increasingly important in healthcare policy and, more recently, clinical care in the Emergency Department (ED). We compare ED resident and patient perceptions surrounding the costs of emergency care, compliance, communication, and education. Methods: We conducted a mixed methods study using surveys and qualitative interviews in a single, urban academic ED. The first study population was a convenience sample of adult patients (\u3e17 years of age), and the second was ED residents training at the same institution. Participants answered open- and closed-ended questions on costs, cost-related compliance, and communication. Residents answered additional questions on residency education on costs of care. Closed-ended data were tabulated and described using standard statistics while open-ended responses were analyzed using grounded theory. Results: Thirty ED patients and 24 ED residents participated in the study. Both ED patients and residents felt neutral regarding the importance of cost discussions and generally did not have knowledge of medical costs. Patients were comfortable discussing costs while residents were less comfortable. Additionally, some patients had cost concerns restricting compliance with treatment. Limitations to discussing costs included lack of time and perceived irrelevance. Generally, ED residents took costs into consideration during clinical decision-making, most commonly because of a feeling of personal responsibility to control healthcare costs. Nearly all ED residents agreed they had too little education regarding costs, and the most common suggestion for enhancing education was inclusion of price lists. Conclusions: There were several notable differences in patient and resident perspectives on cost discussions in the ED in this sample. While patients do not see cost discussions to be important, they are generally comfortable discussing costs yet do not report having sufficient knowledge on what care costs. ED residents think costs are important, but are less comfortable discussing them, primarily because they lack education on medical costs

A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization

AbstractWe present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes

ProxyFL: Decentralized Federated Learning through Proxy Model Sharing

Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator's data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant's privacy. Proxy models allow efficient information exchange among participants using the PushSum method without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a pan-cancer diagnostic problem using over 30,000 high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy

Robust Trading Mechanisms with Budget Surplus and Partial Trade

Abstract In a bilateral bargaining problem with private values

Leaving Goals on the Pitch: Evaluating Decision Making in Soccer

Analysis of the popular expected goals (xG) metric in soccer has determined that a (slightly) smaller number of high-quality attempts will likely yield more goals than a slew of low-quality ones. This observation has driven a change in shooting behavior. Teams are passing up on shots from outside the penalty box, in the hopes of generating a better shot closer to goal later on. This paper evaluates whether this decrease in long-distance shots is warranted. Therefore, we propose a novel generic framework to reason about decision-making in soccer by combining techniques from machine learning and artificial intelligence (AI). First, we model how a team has behaved offensively over the course of two seasons by learning a Markov Decision Process (MDP) from event stream data. Second, we use reasoning techniques arising from the AI literature on verification to each team's MDP. This allows us to reason about the efficacy of certain potential decisions by posing counterfactual questions to the MDP. Our key conclusion is that teams would score more goals if they shot more often from outside the penalty box in a small number of team-specific locations. The proposed framework can easily be extended and applied to analyze other aspects of the game