  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.
