摘要翻译：
对于目标再分配问题，如果偏好是严格的但不受限制的，则最大交易周期规则(TTC)是最优规则：它是唯一满足效率、禀赋下界和策略证明的规则；而且，TTC与核心重合。然而，在单峰偏好的子域上，Bade(2019a)定义了一个新的规则，即“爬虫”，它也满足了前三个性质。我们的第一个定理指出爬虫和一个自然定义的“对偶”规则实际上是相同的。其次，对于目标分配问题，我们根据均匀分布随机选择一个资源配置文件，并应用原定义，定义了一个概率版本的爬虫。我们的第二个定理表明，该规则与Knuth(1996)和Abdulkadiroglu和S\onmez(1998)所证明的“随机优先规则”相同，后者等价于“来自随机禀赋的核心”。
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英文标题：
《The Crawler: Two Equivalence Results for Object (Re)allocation Problems
  when Preferences Are Single-peaked》
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作者：
Yuki Tamura and Hadi Hosseini
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要：
  For object reallocation problems, if preferences are strict but otherwise unrestricted, the Top Trading Cycle rule (TTC) is the leading rule: It is the only rule satisfying efficiency, the endowment lower bound, and strategy-proofness; moreover, TTC coincides with the core. However, on the subdomain of single-peaked preferences, Bade (2019a) defines a new rule, the "crawler", which also satisfies the first three properties. Our first theorem states that the crawler and a naturally defined "dual" rule are actually the same. Next, for object allocation problems, we define a probabilistic version of the crawler by choosing an endowment profile at random according to a uniform distribution, and applying the original definition. Our second theorem states that this rule is the same as the "random priority rule" which, as proved by Knuth (1996) and Abdulkadiroglu and S\"onmez (1998), is equivalent to the "core from random endowments".
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PDF链接：
https://arxiv.org/pdf/1912.06909