论文标题
通过揭示偏爱大学排名来分类大数据
Sorting Big Data by Revealed Preference with Application to College Ranking
论文作者
论文摘要
在对美国大学等大数据观察进行排名时，不同的消费者会发现异质的偏好。本文的目的是为这些观察结果进行线性排序，并建议提高其在排名中的相对位置的策略。正确分类的解决方案可以帮助消费者做出正确的选择，政府做出明智的政策决策。以前的研究人员已经应用了外源加权或多元回归方法来对大数据对象进行分类，而忽略了它们的多样性和可变性。通过认识到观察结果和消费者之间的多样性和异质性，我们相反，对这些矛盾的偏好进行了内源性权重。结果是在这些矛盾中的平衡平衡的一致稳态解决方案。该解决方案考虑了观测值之间多步相互作用的溢出效应。当从数据中有效地揭示来自数据的信息时，显示的偏好大大减少了分类过程中所需数据的体积。就业方法可以应用于许多其他领域，例如运动队排名，学术期刊排名，投票和实际有效汇率。
When ranking big data observations such as colleges in the United States, diverse consumers reveal heterogeneous preferences. The objective of this paper is to sort out a linear ordering for these observations and to recommend strategies to improve their relative positions in the ranking. A properly sorted solution could help consumers make the right choices, and governments make wise policy decisions. Previous researchers have applied exogenous weighting or multivariate regression approaches to sort big data objects, ignoring their variety and variability. By recognizing the diversity and heterogeneity among both the observations and the consumers, we instead apply endogenous weighting to these contradictory revealed preferences. The outcome is a consistent steady-state solution to the counterbalance equilibrium within these contradictions. The solution takes into consideration the spillover effects of multiple-step interactions among the observations. When information from data is efficiently revealed in preferences, the revealed preferences greatly reduce the volume of the required data in the sorting process. The employed approach can be applied in many other areas, such as sports team ranking, academic journal ranking, voting, and real effective exchange rates.
