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AUCTION-LOTTERY HYBRID MECHANISMS: STRUCTURAL MODEL AND EMPIRICAL ANALYSIS

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Abstract

This paper studies an auction‐lottery hybrid mechanism that is widely adopted in allocating new vehicle licenses in China. We characterize individuals' entry and bidding strategies in a symmetric Bayesian Nash equilibrium, structurally estimate individuals' value distribution from a dataset of Guangzhou program, and evaluate the performance of the mechanism. Based on the estimated distribution and counterfactual analysis, our study suggests that a hybrid mechanism preserves 83% efficiency and 52% revenue, while improves equity by 25 times comparing to a pure auction. We show that allowing auction losers to participate in the lottery can further enhance the performance. This article is protected by copyright. All rights reserved

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... Historically, only neural networks with training without a teacher have been used to solve CSP tasks. They are best suited for this purpose, since they do not require generating training examples [6][7][8][9]. This can easily be explained by a simple example: if we assume that the input of a neural network under control is a solution to the CSP problem, then a "good" training example for the network would have to be a valid CSP solution, hence we would already have denouements, which is obviously absurd. ...
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