We study two-sided decentralized matching markets in which participants have uncertain preferences. We present a statistical model to learn the preferences. The model incorporates uncertain state and the participants' competition on one side of the market. We derive an optimal strategy that maximizes the agent's expected payoff and calibrate the uncertain state by taking the opportunity costs into account. We discuss the sense in which the matching derived from the proposed strategy has a stability property. We also prove a fairness property that asserts that there exists no justified envy according to the proposed strategy. We provide numerical results to demonstrate the improved payoff, stability and fairness, compared to alternative methods.

中文翻译：

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不确定偏好下去中心化匹配市场的学习策略

我们研究了参与者具有不确定偏好的双边去中心化匹配市场。我们提出了一个统计模型来学习偏好。该模型结合了市场一侧的不确定状态和参与者的竞争。我们推导出一个最优策略，最大化代理的预期收益，并通过考虑机会成本来校准不确定状态。我们讨论从所提出的策略中得出的匹配具有稳定性的意义。我们还证明了一个公平属性，该属性断言根据所提出的策略不存在合理的嫉妒。与替代方法相比，我们提供了数值结果来证明改进的收益、稳定性和公平性。