Sorting Big Data by Revealed Preference with Application to College Ranking
This provides a method for consumers and governments to make better choices and policies in areas such as college rankings, but it is incremental as it builds on existing sorting approaches with endogenous weighting.
The paper tackles the problem of ranking big data observations like colleges by addressing heterogeneous consumer preferences, resulting in a consistent steady-state solution that reduces required data volume through efficient revealed preferences.
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.