34 pages. Unpublished. In version 2 an incorrect claim about the assignment game has been deleted and reference to prior work by Chambers and Yenmez given. This work was developed independently
Many-to-many matching with contracts is studied in the framework of revealed preferences. All preferences are described by choice functions that satisfy natural conditions. Under a no-externality assumption individual preferences can be aggregated into a single choice function expressing a collective preference. In this framework, a two-sided matching problem may be described as an agreement problem between two parties: the two parties must find a stable agreement, i.e., a set of contracts from which no party will want to take away any contract and to which the two parties cannot agree to add any contract. On such stable agreements each party's preference relation is a partial order and the two parties have inverse preferences. An algorithm is presented that generalizes algorithms previously proposed in less general situations. This algorithm provides a stable agreement that is preferred to all stable agreements by one of the parties and therefore less preferred than all stable agreements by the other party. The number of steps of the algorithm is linear in the size of the set of contracts, i.e., polynomial in the size of the problem. The algorithm provides a proof that stable agreements form a lattice under the two inverse preference relations. Under additional assumptions on the role of money in preferences, agreement problems can describe general two-sided markets in which goods are exchanged for money. Stable agreements provide a solution concept, including prices, that is more general than competitive equilibria. They satisfy an almost one price law for identical items.
How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators
Imperial College Business School, Imperial College London（帝国理工学院商学院，帝国理工学院）；University of Sydney Business School, University of Sydney（悉尼大学商学院，悉尼大学）；Meta
Rationalizing collective revealed preferences with an application in fair resource allocation
Booth School of Business, University of Chicago（芝加哥大学布斯商学院）；Antai College of Economics and Management, Shanghai Jiao Tong University（上海交通大学安泰经济与管理学院）；School of Data Science, School of Management and Economics, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院、管理学院与经济学院）；Department of Management Science and Engineering, Stanford University（斯坦福大学管理科学与工程系）
Learning Economic Parameters from Revealed Preferences
Carnegie Mellon University（卡内基梅隆大学）；The Hebrew University（希伯来大学）；Georgia Institute of Technology（佐治亚理工学院）
从Beigman and Vohra（2006）和Zadimoghoughs and Roth（2012）开始，最近的一系列工作解决了{ \em学习}从显示的偏好数据中获得效用函数的问题。这里的目标是利用过去的数据描述购买的效用最大化代理时，面对一定的价格和预算约束，以产生一个假设函数，可以准确地预测{ \em未来}的代理行为。在这项工作中，我们通过为一些重要的类提供样本复杂性保证和有效的算法来推进这一工作。通过与多类学习的最新进展相联系，我们提供了一种计算效率高的算法，该算法具有严格的样本复杂度保证（$Θ(d/ε)$用于$d$商品的情况），用于在线性价格模型下学习线性效用函数。这就解决了Zadimoghloves and Roth（2012）中的一个悬而未决的问题。我们的技术产生了许多概括，包括学习其他研究良好的效用函数类的能力，以处理一个错误指定的模型，并与非线性价格。
