学习正在发生的事情：从不透明的交易中重建偏好和优先级
Learning What's going on: reconstructing preferences and priorities from opaque transactions
摘要：
我们考虑一个设置，其中$n$买家，组合偏好超过$m$项目，和卖方，运行基于优先级的分配机制，反复互动。我们的目标是，通过观察关于这些互动结果的有限信息，重建买家的偏好和卖家的机制。更具体地说，我们考虑一个在线设置，在每个阶段，一个子集的买家到达并分配物品，根据一些未知的优先级，卖方在买家。我们的学习算法只观察哪些买家到达和产生的分配（或分配的某些功能，例如哪些买家获得了正效用，哪些没有），其目标是预测未来买家子集的结果。对于这个任务，学习算法需要重建买家之间的优先级和每个买家的偏好。我们推导出错误边界算法添加剂，单位需求和单一的头脑买家。我们还考虑的情况下，买方的公用事业的固定捆绑可以改变阶段之间由于不同的（观察到的）价格。我们的算法在计算时间和最大错误数（买家和物品的数量都是多项式）方面都是有效的。
英文摘要：
We consider a setting where $n$ buyers, with combinatorial preferences over $m$ items, and a seller, running a priority-based allocation mechanism, repeatedly interact. Our goal, from observing limited information about the results of these interactions, is to reconstruct both the preferences of the buyers and the mechanism of the seller. More specifically, we consider an online setting where at each stage, a subset of the buyers arrive and are allocated items, according to some unknown priority that the seller has among the buyers. Our learning algorithm observes only which buyers arrive and the allocation produced (or some function of the allocation, such as just which buyers received positive utility and which did not), and its goal is to predict the outcome for future subsets of buyers. For this task, the learning algorithm needs to reconstruct both the priority among the buyers and the preferences of each buyer. We derive mistake bound algorithms for additive, unit-demand and single minded buyers. We also consider the case where buyers' utilities for a fixed bundle can change between stages due to different (observed) prices. Our algorithms are efficient both in computation time and in the maximum number of mistakes (both polynomial in the number of buyers and items).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
