论文标题
在不确定的偏好下，分散匹配市场的学习策略
Learning Strategies in Decentralized Matching Markets under Uncertain Preferences
论文作者
论文摘要
当代理人的偏好未知时，我们必须从数据中学习，从而研究了共享资源的稀缺性问题，因此我们研究了决策问题。以双面匹配市场为典型的例子，我们专注于分散的环境，在该设置中，代理商不与中央权威分享他们的偏好。我们的方法是基于代表复制品希尔伯特领域中的偏好的代表，以及一种学习算法的偏好算法，这些偏好是由于市场上代理商之间的竞争而导致不确定性的。在规律性条件下，我们表明，我们的偏好估计器以最小值的最佳速率收敛。鉴于这个结果，我们得出了最大化代理商预期收益的最佳策略，并通过考虑机会成本来校准不确定的状态。我们还获得了激励兼容的属性，并表明从学习策略中的结果具有稳定性。最后，我们证明了一个公平的财产，该财产断言没有根据学识渊博的策略没有合理的嫉妒。
We study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori and must be learned from data. Taking the two-sided matching market as a running example, we focus on the decentralized setting, where agents do not share their learned preferences with a central authority. Our approach is based on the representation of preferences in a reproducing kernel Hilbert space, and a learning algorithm for preferences that accounts for uncertainty due to the competition among the agents in the market. Under regularity conditions, we show that our estimator of preferences converges at a minimax optimal rate. Given this result, we derive optimal strategies that maximize agents' expected payoffs and we calibrate the uncertain state by taking opportunity costs into account. We also derive an incentive-compatibility property and show that the outcome from the learned strategies has a stability property. Finally, we prove a fairness property that asserts that there exists no justified envy according to the learned strategies.
