Coasters are evil

I asked my cook to make me a cup of tea. He did so and placed the steel tumbler full of tea on a coaster on my dining table. I went to pick it up. As I picked up, the bottom of the tumbler seemed to get stuck to the coaster, because of which I couldn’t pick up the tumbler cleanly. And the tumbler came up along with the coaster, and toppled, spilling tea all over the dining table.

And so I had the task of cleaning up the dining table of all the tea spilt on it. Thankfully there was no tea on the floor, and I didn’t lose too much tea. Yet, none of this happens on most days when the cook places my teacup directly on the dining table without an intermediating coaster.

So it is all the coaster’s fault, I tell you. Coasters are evil.

Speaking of coasters, Monkee had once commented that coasters are the ultimate sign of domestication – curtains being an intermediate sign of domestication. And thinking about it, these coasters (one of which was responsible for the spilled tea today) were bought during my honeymoon!

What is the feminine of Amit?

“Amit” is a word that is commonly used, often pejoratively, to refer to men from the North of India. The reason for the usage of “Amit” in this context is that while it is an extremely common name for men from North India, it is not as common in other parts of India, and thus it characterises men from North India.

A question that has been floating around in social media circles for a long time in this connection is what the feminine form of “Amit” is. If Amit characterises the median North Indian male, what name characterises the median North Indian female? Popular candidates for this are Neha, Isha and Pooja. Pooja suffers from the fact that is is also a fairly common name in other parts of India. Isha, while it might be strongly North Indian, is too obscure. And for some reason, people are loathe to accept Neha as the feminine Amit. So how do we resolve this?

I, being a stud, am a big follower of the Hanuman principle. If you have to solve a problem, and it takes no more effort to solve a generic problem, then solve the generic problem and apply it to this problem as a special instance rather than spending time to solve each instance. Hence, we will rephrase this problem as “What first name uniquely identifies a particular ethnicity?”. I, being a quant, am going to use the quantitative hammer to hammer down this nail. So we can rephrase as “how can we quantitatively characterise ethnicities by first names?”

The first thing to notice is that we need a frame of reference. Amit is a good name to characterise a North Indian man among the universe of Indian men. However, if we define the universe differently, as “Asian” for example, or “men living in Delhi”, Amit may not be as characteristic at all. Hence, any formula that we develop needs to take into account the frame of reference.

Secondly, what makes a name ethnically characteristic? I argue that there are two factors, and these two will be used in deriving the final formula. Firstly, the name should be common among the particular ethnicity – for example, Murugaselvan is extremely characteristic of Tamil men, but its occurrence is so low that using Murugaselvan as the median Tamil man among all Indian men is futile. Secondly, the name should be distinctive for that particular community. For example, a possible competitor to Amit is Rahul, a name that is possibly as common among North Indians as Amit is (I haven’t seen the statistics). The problem with Rahul, however, is that it is a fairly common name in South India also! So it does a bad job in terms of discrimination. So basically what we are looking for is a name that is both popular in the ethnicity we want to characterise, and also characteristic to that particular ethnicity in comparison to the universe.

These two requirements lead to the following rather simple formula (I’m not claiming that this is the best formula – if there is a way to objectively evaluate such formulas, that is – but it is sufficiently good and simple to understand and evaluate). Let our universe by U and the community we are trying to characterise by C. C’ is {U – C} (I’m assuming all of you know set theoretic notation). The first name N that characterises the community C is the one that maximises P(N|C) – P(N|C’). That’s it. Simple.

To explain in English, for each first name, we calculate the incidence of that particular name in the community C. That is, for example, what proportion of North Indian girls are named Neha, Pooja, Isha, Nidhi, etc. Next, we calculate the incidence of the name in the “complement of C”, that is how likely is it that someone in the rest of the “universe” we have defined has the same name. In our above example, we calculate what proportion of Indian but NOT North Indian girls (taking Indian women as the universe) are named Neha, Pooja, Isha, Nidhi, etc. Then, for each name, we subtract the latter quantity from the former quantity and then select the name for which this difference is maximum! Rather simple, I would think!

Now, we need data. Unfortunately I can’t seem to find any publicly available data sets that contain long lists of names along with markers of ethnicity (address or city or state or language preference or some such). If you can help me with some data sets, we can actually run the above formula for different ethnicities and characterise them. It is going to be a fun exercise, I promise! So pour in the data. And I request you to share publicly available data and not proprietary data.

And then we can for once and for all finish this debate of what the feminine form of Amit is, along with many other fun ethnic classifications.

The finiteness of the global advertising market

In this excellent post on social media companies, Aswath Damodaran articulates something I’ve long wondered – about the finiteness of the global advertising market. Given the number of companies that come up with new mechanisms to match advertisers with consumers, one can be forgiven for believing that the market for advertising is infinite. That the more avenues you create for serving advertisements to people, the more the advertising that will flow, and there won’t be a let up anywhere.

This picture here is from Damodaran’s blog (which I recommend you subscribe to, since every single post is worth reading). Based on the numbers that Damodaran presents here, the overall growth of the worldwide advertising market seems rather low.

Source: Aswath Damodaran (http://aswathdamodaran.blogspot.in/2014/11/twitters-bar-mitzvah-is-social-media.html). All numbers in billions of dollars

The number to take away for me from this calculation is the shrinking pie of non-digital advertising. Based on these numbers, the total non-digital advertising market in 2008 was $468 billion. In 2014, going by the same numbers this is down to $400 billion. This de-growth is significant and holds important lessons for other sectors that are dependent on advertising.

So far, the flow of advertising capital has been taken for granted and the number of business plans made (in both old and new economies) with an assumption on advertising growth is endless. If you want your local bus utility to make more money, you rent out advertising space on buses. If a low-cost airline wants to make more money, they put advertisements on the back of seats (a very good idea since it gets undivided attention for the duration of the flight). It is a surprise that insides of toilet stall doors (which again get undivided attention) haven’t fallen prey to advertisements yet.

The point here is that while it is all well and good to plan businesses based on advertising income, what we need to keep in mind is that the advertising pie in the long term grows at the same rate as the global economy. Sooner or later the waters will recede to the natural level, and then we will know who is swimming naked!

 

Perverse regulations

So Uber has tied up with PayTM to process its payments without a second factor of authentication in order to comply with RBI regulations. This is a major win-win for both companies. Uber can now gain access to the part of the relatively affluent Indian population that does not own a credit card (this is a significant segment). PayTM now has a compelling reason to sign up users for its Wallet solution, since all Uber customers now form a sort of a captive audience for this solution.

While discussing this on twitter, someone suggested that once the new Payment Bank regulation is brought in by RBI, wallet solution providers such as PayTM can then set themselves up as Payment Banks.

The problem with that is that if PayTM becomes a payment bank then it will have to comply with RBI regulations of second factor authentication and thus Uber users will not be able to use their PayTM wallets (now accounts) for seamless payment!

#Thatzwhy we need strong regulations.

Switching languages

I used to marvel about how whenever I was in the company of other people from IIT Madras, I would instinctively switch to speaking “IITese“. Words such as “slisha”, “peace”, “rod”, and all others that I would not normally use in normal English when speaking to normal people would suddenly appear in my vocabulary while talking to others from IITM.

I used to consider myself special that I could discriminate thus, and make best use of the languages I know while not discriminating against people who didn’t understand one of the languages, such as IITese. I used to consider this great, but this bubble got broken when my nephew started talking.

This guy is half-Kannadiga, half-Marathi, with a Gult nanny and his parents speak to each other in Hindi. He is now three years old and for over a year now he’s been very comfortable speaking Kannada and Marathi, and to an extent Telugu, Hindi and English (which he’s learning in school) !  The most remarkable thing with him, though, (as with all other multilingual kids, I would imagine) is that he has mapped people to languages. For example, he knows that I speak Kannada and he speaks to me only in Kannada. And while talking to me if his father (who is Marathi) is present, he immediately switches to Marathi to talk to him. Across languages that are very different, he is able to switch easily and seamlessly and moreover know who speaks which language!

There is a downside, though. Once when his mother, who is “supposed to speak to him in Kannada”, tried talking to him in Marathi, he got really angry and wild and asked her to speak in Kannada! Our initial thought was he was being finicky, but I now think it is to do with parsing. When his mother speaks, he has his “Kannada parser” switched on, and if she speaks Marathi, there is a parsing error and it causes great stress on his processor to switch languages. And being a small kid, that makes him cranky and wild!

In other words, this can be considered as another case of Bayesian recognition! It seems like the human mind’s parsing of speech is influenced by the prior distribution of what language the speaker is speaking in. As the first few words come out, we firm up which parser to use, and then it is smooth sailing. For a kid, though, it seems like the prior distribution of parsers is “binary” (one 1, and the rest 0s), which is what makes the wrong speaker wrong language combo annoying for them!

Us human beings are smarter than we think!

R, Windows, Mac, and Bangalore and Chennai Auto Rickshaws

R on Windows is like a Bangalore auto rickshaw, R on Mac is a Chennai auto rickshaw. Let me explain.

For a long time now I’ve been using R for all my data management and manipulation and analysis and what not. Till two months back I did so on a Windows laptop and a desktop. The laptop had 8 GB RAM and the desktop had 16GB RAM. I would handle large datasets, and sometimes when I would try to do something complicated that required the use of more memory space than the computer had, the process would fail, saying “fail to allocate X GB of memory”. On Windows R would not creep into the hard disk, into virtual memory territory.

In other words it was like a Bangalore auto rickshaw, which plies mostly on meter but refuses to come to areas that are outside the driver’s “zone”. A binary decision. A yes or a no. No concept of price discrimination.

The Mac, which I’ve been using for the last two months, behaves differently. This one has only 8GB of RAM, but I’m able to handle large datasets without ever running out of memory. How is this achieved? By means of using the system’s Virtual Memory. This means the system doesn’t run out of memory, I haven’t received the “can’t allocate memory” error even once on this Mac.

So the catch here is that the virtual memory (despite having a SSD hard disk) is painfully slow, and it takes a much longer time for the program to read and write from the memory than it does with the main memory. This means that processes that need more than 8 GB of RAM (I frequently end up running such queries) execute, but take a really long time to do so.

This is like Chennai auto rickshaws, who never say “no” but make sure they charge a price that will well compensate them for the distance and time and trouble and effort, and a bit more.

How do you change Mata Amrita Index?

Over five years ago, I had introduced the concept of the Mata Amrita Index on this blog. Just to refresh your memories, it refers to the probability that a person will hug any random person she meets. You can also define bilateral Mata Amrita Index, which is the probability that a given pair of people hug when they meet.

Now, after I wrote that post I realise that the Mata Amrita Index is a rather cultural thing – some cultures are more predisposed to hugging than others. I, for example, for whatever reason, am quite queasy about hugging and won’t do so unless I know the counterparty quite well. More importantly than the queasiness, I want to avoid the awkwardness when I offer a hug which makes the other person queasy because they are not prepared for it (this happened the very first time I met the person who is now my wife,btw). For others, hugging comes much more naturally, and if such people initiate a hug to me, I’m happy to continue with the process. But with some others I’ve noticed that both of us are not sure if it’s okay to hug and it ends up in a weird handshake while it might have been a hug!

Anyway, the point of this post is whether the bilateral Mata Amrita Index between a pair of people can change over time, and if so, what the conditions are under which it changes. We will leave romantic or hopefully-romantic or possibly-romantic relationships out of this discussion – the human touch works in those situations in completely different ways. So the question is under what circumstances can the bilateral Mata Amrita Index between a pair of people change over time? And let’s be nice on this blog, and discuss only about increase in MAI, not decrease.

So what are the circumstances under which the bilateral MAI between a pair of people increase over time? One is the frequency of meeting. If you meet someone very regularly, you get into a particular routine on how you greet each other – be it a handshake or a hug or a namaste or a feet-touch or a cheek-peck. Since you are meeting each other regularly, both of you remember the established protocol. And both instinctively go for it. Even if you want to change protocol, the other person is so used to it that they continue. And considering that you can’t command someone to hug you (unless you are an “aunty”) you end up sticking to protocol!

If you meet each other infrequently, on the other hand, you are likely to have forgotten whatever protocol existed, and so there is a higher probability of changing protocol, and so there is a chance that you can enhance your Mata Amrita Index. It helps if it’s been so long since you last met that either of you has undergone a culture-changing experience (like moving to a new country, or a new job, or a new school, for example), which can change the way you greet and can use as an excuse if the counterparty objects to your way of greeting.

Then, if you are meeting after a long time and for some reason you have got closer in the interval (in terms of things you’ve spoken about with each other since the last time you met, for example), it is again okay to explore an enhancement of the Mata Amrita Index the next time you meet.

There is also the company you keep. Let’s say A, B and C are meeting. Whether it’s due to their past bilateral MAI, or individual MAI, A and B hug, and then B and C hug. Now it becomes socially awkward for A and C not to hug, so they end up hugging each other, and enhancing their MAI. The next time they meet, this enhancement will be in their mind, and can lead to further enhancement in MAI. This can also work the other way – if you are in a large group and only two of you in that group have a high bilateral MAI, then it becomes awkward for you to hug when everyone else is being all prude and shaking hands. That can decrease MAI.

There is another way the company you keep can end up decreasing MAI. Again, if A,B and C meet, and A-B have a high bilateral MAI. Let’s say that A is meeting C for the first time. Before A hugs B, she evaluates is she is also okay hugging C (since not doing so might be awkward), and that might lead A to not hug B!

This is complicated business! Do you know any way in which the Mata Amrita Index can be enhanced? Or diminished? Do write in!