Sorting big data by revealed preference with application to college ranking

Xingwei Hu

doi:10.1186/s40537-020-00300-1

Abstract

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.

Highlights

The problem we address here relates to the following typical situation: millions of consumers face hundreds of alternatives when making decisions
One concrete context is to rank the colleges in the United States, where each student selects only one college to attend when admitted by multiple institutions
Results from a miniature ranking It is not our intention to generate a new college ranking to compete with the commercial ones

Summary

Introduction

The problem we address here relates to the following typical situation: millions of consumers face hundreds of alternatives when making decisions. Each alternative is of significant complexity and variety. From an affordable and pre-selected shortlist, a consumer picks just one alternative, revealing some preference over others on the shortlist. Our objective is to sort all of these alternatives according to revealed preferences. One concrete context is to rank the colleges in the United States, where each student selects only one college to attend when admitted by multiple institutions. College ranking is of broad public interest. The consumers are students and their families and governments that aim to rationalize their allocation of funding as well as college administrators who are interested in comparing their ranking to peer

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Conclusion