Idea averaging is a wonderful thing, and you should probably do it way more than you already do, and it should probably be your default mode when looking to choose between ideas.
A lot of this is spelled out in the literature about why ensemble methods tend to be superior to individual methods in statistical inference, which has a lot of ties to why averages of non-expert opinions tend to be as accurate or even more accurate than small pools of expert opinions.
In the limit, when you reallocate credibility to a set of ideas in a manner that is fully derived from your prior beliefs and the evidence you have, it becomes exactly Bayesian reasoning. A posterior distribution is exactly an "idea average" where each idea (each potential outcome) gets as much (and no more) credibility as it deserves, according to the prior and the probability model at hand.
Here's somewhat of a popular account of it:
< https://www.washingtonpost.com/business/is-it-better-to-trus... >
There is also the recent stuff under the marketing buzzword "Wisdom of Crowds" e.g. < http://onlinelibrary.wiley.com/doi/10.1111/j.1551-6709.2011.... > or the Wikipedia article on it too (and many sources that point out potential problems and corner cases that are also important).
In general though, I think I have to disagree with the article's premise. Unless two distinct ideas truly are mutually exclusive (most things aren't), then it's better to have a diversified portfolio over the space of all the ideas than to put all your eggs in one basket, and averaging ideas is sort of a humble heuristic that seems to work in a lot of areas.
A lot of this is spelled out in the literature about why ensemble methods tend to be superior to individual methods in statistical inference, which has a lot of ties to why averages of non-expert opinions tend to be as accurate or even more accurate than small pools of expert opinions.
In the limit, when you reallocate credibility to a set of ideas in a manner that is fully derived from your prior beliefs and the evidence you have, it becomes exactly Bayesian reasoning. A posterior distribution is exactly an "idea average" where each idea (each potential outcome) gets as much (and no more) credibility as it deserves, according to the prior and the probability model at hand.
Here's somewhat of a popular account of it:
< https://www.washingtonpost.com/business/is-it-better-to-trus... >
There is also the recent stuff under the marketing buzzword "Wisdom of Crowds" e.g. < http://onlinelibrary.wiley.com/doi/10.1111/j.1551-6709.2011.... > or the Wikipedia article on it too (and many sources that point out potential problems and corner cases that are also important).
In general though, I think I have to disagree with the article's premise. Unless two distinct ideas truly are mutually exclusive (most things aren't), then it's better to have a diversified portfolio over the space of all the ideas than to put all your eggs in one basket, and averaging ideas is sort of a humble heuristic that seems to work in a lot of areas.