Unfortunately, I don't expect that there's any clear-cut mathematical solution to this problem. The modelling issues are rather subtle, and different people will make very different assumptions in the framework they are using. I think there's a lot of value to exploring these possibilities, but it's likely to lead to a lot of competing, somewhat useful approaches, rather than a definitive or widely accepted solution.
Social choice theory deals with aggregating opinions from a lot of voters, and this is a huge field. (Donald Saari has written some great books on this topic.) The drawback is that it doesn't deal with the "ideal choice" aspect of your question, but rather just with which choice is most popular or representative in some way, which becomes subtle when there are more than two choices. Is it better for everyone to be lukewarm, or for some people to love a choice and others hate it?
There's been a lot of work in machine learning on learning from expert advice, where the experts may have different levels of expertise, which you don't know in advance and which can even change over time. Under certain assumptions, multiplicative weight algorithms and boosting are excellent ways to solve this problem, and they are quite simple and practical. (See http://www.cs.princeton.edu/~arora/pubs/MWsurvey.pdf and http://cseweb.ucsd.edu/~yfreund/papers/adaboost.pdf, for example.) This can be viewed as a reputation-based system, which rewards experts for good performance.
However, the assumptions are really critical. For example, these methods apply to cases where you get periodic objective feedback that can be used to judge the experts. In some cases, like journal ranking, you just don't: you learn something from measures like citation counts, but nobody thinks they are the best measure of quality, and you don't want the whole system to degenerate into a contest over who can best predict citation counts. On the other hand, a contest over who can predict repair rates for cars is not so crazy.
This is where the modelling gets tricky. People may not even agree on what they are trying to measure, let alone how to measure it. For example, which is more impressive: a relatively shallow paper that excites and inspires many people to do better work on the topic, or a deeper paper that plays a critical role in a smaller and arguably less important area? If we can't even settle that informally, it's hard to build a model.
There are also nontrivial issues of incentives. For example, the U.S. News & World Report college rankings are partly based on reputation surveys. Some university administrators have deliberately rated their institution highly and all others as inferior, to boost their rankings. (Clemson admitted this publicly; see http://www.insidehighered.com/news/2009/06/03/rankings.) The subfield of mechanism design within game theory addresses this issue of figuring out how to elicit information without giving anyone an incentive to lie. It can be done in some cases, but it's a hard problem, especially in cases like product reviews where, say, the manufacturer or a competitor may be hiring people behind the scenes to provide biased reviews. (I don't know of good statistics for this, but it is widely believed to be a serious problem.)
Overall, there are a lot of important ideas out there that are relevant to this topic, and I expect further progress. However, the modelling issue is a huge obstacle.