# Mata Amrita Index

There are many kinds of people in the world. There are those that hug everyone they meet. Then there are those who start fidgeting when their bodies come somewhat close to that of other people (I assume these people don’t travel by Mumbai local trains). There are the sexist ones – who only hug people of their own sex, and there are those who reserve their hugs exclusively for people of the opposite sex. Some people don’t actively hug, but gracefully comply when someone goes out to hug them. And yet others say “tchee tchee” and run when someone wants to hug them.

Given such diversity, what we need is a simple measure to classify people, so that such classification might probably be of use to marketers. The measure that I propose in this essay is by no means simple, yet it is a start. The measure that I propose is a real number, no less than zero, no greater than one. The definition of this number isn’t clean. It involves a little bit of math, and by math I mean math, not just arithmetic. I also complicate it a little bit by proposing a couple of related measures, which, though not as simple as the main index number, are capable of offering much better insight.

The measure I am proposing here is named after the great Mellu saint Mata Amritanandamayi (Mata Amrita for short), also known as “the hugging saint”. Mata Amrita has the policy of hugging her followers in order to deliver her blessings, which is a marked improvement over most other Indian godmen and godwomen who ask their followers to touch their feet. In fact, there is probably no other public figure who is as famous for hugging people.  Another factor that goes into the choice of this name is that it is generally nice to have your concept linked to a holy person. If not anything else, the concept will be blessed with some good Karma.

Coming to the measure itself, Baada says that the word “index” might be misleading, and it should be called the “Mata Amrita Number” instead. However, “Index” sounds so much better in this context, and MAI is a much better acronym than MAN. I need to mention right up front that the MAI is an absolute index, and is not a relative index. Each and every man and woman and transsexual has his or her own MAI. And there is also a “bilateral MAI” which is defined for pairs of people, but we will come to that later.

The Mata Amrita Index for a person is defined as the likelihood of him or her hugging the next random person he/she meets.

The Bilateral Mata Amrita Index for a pair of persons is defined as the likelihood of this pair hugging each other the next time they meet each other.

Yes, as simple as that. Or maybe not, since likelihood is not such a simple concept. But then, I’m sure you are getting the drift. What complicates the first definition is the word “Random” (towards the end). The reason this un-natural random word has been inserted in there is to add stability to a person’s MAI. So that a person’s MAI is not influenced by the knowledge of who he/she is going to meet next. I hope you are getting the drift. If you aren’t, leave a comment and I’ll explain with examples. However, there is no such complication in case of the bilateral index since it is defined for a pair.

Now, I suppose that it is intuitive that a person’s MAI is a weighted average of the person’s BMAI with all the people he knows, weighted by the frequency of meeting each such person. So for example, there is a bunch of people whom I hug every time I meet them (high BMAI), but I don’t meet these people too often, so my overall MAI remains low. And so forth.

Oh, and by definition Mata Amrita herself has a MAI of 1, since she deterministically hugs every random person she meets.

Long ago, people used to say “honey, give me a hug”. That is so passe now. That is so 20th century. Now, you are supposed to say “let us enhance our Mata Amrita Index”.

Update

I hereby thank my FGB*, the Flower Of Tam Brahm Womanhood (FOTBW) Nityag and my stalker Priyanka for their cantributions to this theory. I also thank Kodhi and Aadisht for gratefully listening to the theory when I first proposed it to them. And I thank Baada also, for his critical analysis and constant encouragement. Last but not the least, I thank my school classmate Kavya who is the chief inspiration behind this concept. In fact, I dedicate this concept to Kavya.

*FGB = foremost girl buddy

## 11 thoughts on “Mata Amrita Index”

1. This is quite stud! Normally I don’t comment on blogs unless I have something to say but this post is indeed remarkable.

Kudos Skimpy.

1. skimpy says:

thank you

2. KT says:

Interesting..
some variants
– slap index : p(slap next guy u weet) – is uni-directional unlike hugging
– kill index : p(kill next guy u meet) – only 1 time use once executed

high values will indicate aggressiveness of a person

1. skimpy says:

your indices have one fundamental problem – they lack interesting names. so they’ll never catch on

the thing with MAI is that it has an interesting name, and that should help a lot in propagating the concept

3. xyz says:

You have a concept here. How would you implement it? How would it be measured? By whom? Might it be “gamed’?

MAI – sounds like the correlation of a single credit (i.e. the underlying assets) with “the single factor” in a single-factor model.

BMAI – soulds like a bivariate correlation of the single credit above with, and when conditioned on, a given – as opposed to arbitrary – second name.

1. skimpy says:

yeah this is just the concept. yet to flesh it out.

hmmm interesting definitions you’ve put here

4. Skobit says:

Skimpy, I think there’s a small problem with saying that:

MAI = Weighted Average of BMAIs (with people a person knows)

Does this exclude random people whom he meets? Of course, you can say that for any individual, both the frequency of meeting them and BMAIs are likely to be vanishingly small (except for, of course, MA herself). Still, for completeness sake, I would say that MAI should include that.

1. Skobit says:

Hmm, on re-reading the definition of calculation, I guess you do include BMAIs of random people too. I guess I’m concentrating too much on the words people he knows .