<i>Example</i> Paint Artists<p><i>Given</i> A group of peers. In this case a self selected group of persons who paint artistically, say 4 hours or more a week. Let these be the peers.<p><i>Hypothesis</i> A "consensus" of opinion within this group should establish a qualitative analysis of the whole pool of submitted works; a curve. If each individual within this group of (approximate) peers is given the ability to actively criticize a significant percentage of the other artist's work, I believe the result would be simple but accurate stratification (indicating, roughly, the top dog).<p><i>Algorithm Hypothesis</i><p>-- 500 artists who all work in a similar category (realism, impressionism, anime?) submit 1 work of art each.<p>-- Each artist is required to view and rank 3 unique sets of 5 works. (5 works per set for ease of ranking, 3 sets to increase the amount of redundant input, "unique" meaning each set of 5 works is a permutation).<p>-- 1500 ranks (ideally) will be submitted. Each piece of art should get ranked 30 times against a completely different selection each time.<p><i>Why is this supposed to be good?</i><p>-- Those voting are work-proven peers<p>-- The ranks are not simple "hot or not" choices, but rudimentary curves.<p>-- Voting is limited, evenly distributed, and public<p>-- Recursive post analysis is possible based on discounting the rankings of lower ranked voters and, similarly boosting the ranking weight of higher ranked voters.<p><i>Disclaimer</i> I am working on a project that will use this algorithm, and your comments, links and criticism. I am not a statistician, obviously.