Monday 25 May 2015

How to Accept (Reject) Recommendations/ Suggestions?

Suppose a book or an expert on a chosen field has set of recommendations for the government to solve a social or economic problem of the day. For example, Tony Atkinson in his book Inequality: What Can Be Done? has given 15 recommendations to solve the problem of rising and severe inequality. Or think like this. A government has instituted a commission to suggest some policy measures to solve the issue of inequality. The committee comes out with a bunch of recommendations. Now all these suggestions will be discussed and debated vociferously. It will continue for months. A government concerned about rising inequality wants to do something to reduce it.  My puzzle is this:

1.      Out of top 10 inequality experts, six experts accept recommendations #1 through #10 and reject #11 through #15. And the other four experts reject #1 through #10 and accept #11 through #15. In effect we see all recommendations are rejected and accepted. What should a government wanting to implement some of recommendations do? The question before the government is: Which one it should accept (reject) and why?

2.      In the process of intense discussions top experts in the field will reject some recommendations and will accept some. So, if there are 30 big stars in inequality research, then 15 accepting strongly all recommendations and 15 rejecting strongly how to accept those recommendations?

So, how to accept recommendations when there is a sharp divide among experts ? On what basis?


I have one suggestion. Instead of asking the experts to say yes/no, we can ask each expert to place their confidence or weights behind all suggestions. Then we will aggregate all confidence or weights. The recommendation having highest confidence will be the chosen over others for government action. Will it solve the puzzle mentioned above? It may not because when an expert says yes (no), he may give 100% weight or 90% weightage. But it will be very effective when experts are uncertain about the efficacy of recommendations. If experts give their views unbiasedly then this weighting scheme will work better.