A brief elaboration on rating systems discussed before. Since mass voting creates a skewed distribution and uses only a fraction of the scale, web services typically should avoid simple aggregation of votes.
For example, here’s IMDb’s famous Top 250 distributed by release decade:
The top is biased in favor of recent movies. Not least because voters themselves represent a young audience.
Compare it with film distribution from less known aggregator TSPDT:
Best films here have a normal distribution around the 1970s.
Which rating is better? Ideally, a rating system must minimize the difference between the ratings you see before and set after watching a movie. It works better when ratings can be conditioned on your preferences. (The recommender systems have the same goal, and that’s what the Netflix competition was about.)
TSPDT is based on opinions by critics, and the IMDb accepts anyone’s vote. Clearly, critics spend more time on comparing and evaluating cinema. But their tastes deviate from those of the public, and their ratings may be only second-best predictors of your own rating.
In politics, the role of authoritative recommenders typically belongs to journalists. And as Gentzkow et al. notice in “Competition and Ideological Diversity,” people prefer like-minded newspapers. So, both journalists and critics have access only to like-minded subsets of population. And being informed helps a little in guiding others in choosing politicians and movies, unless you recommend something your reader already agree with.
But information remains an important dimension in any voting. Banerjee and Duflo, “Under the Thumb of History?”, survey papers showing that voters choose other candidates when they have more information. In that sense, critics may improve everyone else’s choices.
The problem may be not in information and preferences themselves, but in poor matching between experts and the public with similar preferences. Web services then may focus on improving this matching as far as they have access to both groups. Their recommender systems may advise products, but it makes sense to advise specific experts. Ratings in that case shouldn’t be taken literally. They’re only the mean of matching experts and subgroups of the public on issues they agree with each other.