*Q: How do you carve an elephant?
A: Take a large stone and remove from it all that doesn’t look like an elephant*

– Ancient Indian proverb, as told to us by Prof C Pandu Rangan during the Design of Algorithms course

As I had explained in a post a long time ago, this whole business of louvvu and marriage and all such follows a “Monte Carlo approach“. When you ask yourself the question “Do I want a long-term gene-propagating relationship with her?” , the answer is one of “No” or “Maybe”. Irrespective of how decisive you are, or how perceptive you are, it is impossible for you to answer that question with a “Yes” with 100% confidence.

Now, in Computer Science, the way this is tackled is by running the algorithm a large number of times. If you run the algo several times, and the answer is “Maybe” in each iteration, then you can put an upper bound on the probability that the answer is “No”. And with high confidence (though not 100%) you can say “Probably yes”. This is reflected in louvvu also – you meet several times, implicitly evaluate each other on several counts, and keep asking yourselves this question. And when both of you have asked yourselves this question enough times, and both have gotten consistent maybes, you go ahead and marry (of course, there is the measurement aspect also that is involved).

Now, the deal with the arranged marriage market is that you aren’t allowed to have too many meetings. In fact, in the traditional model, the “darshan” lasts only for some 10-15 mins. In extreme cases it’s just a photo but let’s leave that out of the analysis. In modern times, people have been pushing to get more time, and to get more opportunities to run iterations of the algo. Even then, the number of iterations you are allowed is bounded, which puts an upper bound on the confidence with which you can say yes, and also gives fewer opportunity for “noes”.

*Management is about finding a creative solution to a system of contradictory constraints*

– Prof Ramnath Narayanswamy, IIMB

So one way to deal with this situation I’ve described is by what can be approximately called “pruning”. In each meeting, you will need to maximize the opportunity of detecting a “no”. Suppose that in a normal “louvvu date”, the probability of a “no” is 50% (random number pulled out of thin air). What you will need to do in order to maximize information out of an “arranged date” (yes, that concept exists now) is to raise this probability of a “no” to a higher number, say 60% (again pulled out of thing air).

If you can design your interaction so as to increase the probability of detecting a no, then you will be able to extract more information out of a limited number of meetings. When the a priori rejection rate per date is 50%, you will need at least 5 meetings with consistent “maybes” in order to say “yes” with a confidence of over 50% (I’m too lazy to explain the math here), and this is assuming that the information you gather in one particular iteration is independent of all information gathered in previous iterations.

(In fact, considering that the amount of incremental information gathered in each subsequent iteration is a decreasing function, the actual number of meetings required is much more)

Now, if you raise the a priori probability of rejection in one particular iteration to 60%, then you will need only 4 independent iterations in order to say “yes” with a confidence of over 95% (and this again is by assuming independence).

Ignore all the numbers I’ve put, none of them make sense. I’ve only given them to illustrate my point. The basic idea is that in an “arranged date”, you will need to design the interaction in order to “prune” as much as possible in one particular iteration. Yes, this same thing can be argued for normal louvvu also, but there I suppose the pleasure in the process compensates for larger number of iterations, and there is no external party putting constraints.