Author: SK

Flight food and choice

The topic of outrage for the day on Twitter seems to be Air India’s decision to serve only vegetarian food on flights that last less than 90 minutes. Predictably, given the current government’s policies and track record so far, people are decrying this as some sort of a “brahminical conspiracy”. This was even quoted as  a reason to privatise Air India (while I don’t agree with this reason, I fully agree that there are several other reasons to privatise Air India).

While outragers will outrage (and they might have a pathological need to outrage), this decision of Air India actually has sound basis. I had touched upon this in an earlier  blog post about why I get irritated with Indigo’s in-flight service.

The problem is that the more the choice you give customers, the slower the overall service will be. While this may not affect people seated in rows where service begins, it can be an immense cause of frustration for passengers who are seated in rows that will be served last.

In longer duration flights, this matters less since people who are served last will have sufficient amount of time to finish their meals before the trays have to be cleared in time for the flight to land. On shorter flights, however, the time available for meal service is so short that it is possible that trays might have to be cleared barely after a section of the passengers have started eating.

Eliminating choice significantly speeds up the meal delivery process (refer to my post on Indigo’s food for more on this), and ensures that people who have been served last have sufficient time to finish their meals before trays have to be cleared. While it may not take much time for the steward to ask the customer her choice, considering the total cycle time (along with passengers asking details of the menu, etc.) and the number of passengers to be served, cutting choice is a sound decision indeed.

As for the vegetarian option, when there is no choice offered, it is natural to go with the option that satisfies the maximum number of people. Considering that Indians don’t eat much meat (while only a small proportion of Indians are vegetarian, overall meat consumption is very low), it is a rather obvious choice that only vegetarian food will be served.

This is a commendable decision by Air India and I hope they stick to it. I hope other airlines will also learn from this and cut choice in their inflight menus (Indigo, I’m looking at you) so that passengers can be served with the minimum uncertainty and minimum fuss.

Tailpiece The above linked NDTV Profit piece has a bizarre comment from an expert. Quoting:

However, according to travel industry expert Rajji Rai, the state-owned airline should have first carried out a passenger survey, which is an industry practice, before affecting any change in the menu.

“Airlines world over carry out customer surveys before taking such decisions. Unfortunately, Air India is very poor in such practices. This decision to discontinue non-vegetarian food on these non-metro flights is just one-sided,” he said.

Surveys are overrated in my opinion, and there is no reason Air India should have conducted a survey before making this decision – for they have access to significant amounts of actual customer preferences over a large number of schedules. The value of a survey in this case is at best marginal

Bayes and serial correlation in disagreements

People who have been in a long-term relationship are likely to recognise that fights between a couple are not Markovian – in that the likelihood of fighting today is not independent of the likelihood of having fought yesterday.

In fact, if you had fought in a particular time period, it increases the likelihood that you’ll fight in the next time period. As a consequence, what you are likely to find is that there are times when you go days, or weeks, or even months, together in a perennial state of disagreement, while you’ll also have long periods of peace and bliss.

While this serial correlation can be disconcerting at times, and make you wonder whether you are in a relationship with the right person, it is not hard to understand why this happens. Once again, our old friend Reverend Thomas Bayes comes to the rescue here.

This is an extremely simplified model, but will serve the purpose of this post. Each half of a couple beliefs that the other (better?) half can exist in one of two states – “nice” and “jerk”. In fact, it’s unlikely anyone will completely exist in one of these states – they’re likely to exist in a superposition of these states.

So let’s say that the probability of your partner being a jerk is P(J), which makes the probability of him/her being “nice” at P(N) = 1- P(J). Now when he/she does or says something (let’s call this event E), you implicitly do a Bayesian updation of these probabilities.

For every word/action of your partner, you can estimate the probabilities in the two cases of your partner being jerk, and nice. After every action E by the partner, you update your priors about them with the new information.

So the new probability of him being a jerk (given event E) will be given by
P(J|E) = \frac{P(J).P(E|J)}{P(J).P(E|J) + P(N).P(E|N)} (the standard Bayesian  formula).

Now notice that the new probability of the partner being a jerk is dependent upon the prior probability. So when P(J) is already high, it is highly likely that whatever action the partner does will not move the needle significantly. And the longer P(J) stays high, the higher the probability that you’ll lapse into a fight again. Hence the prolonged periods of fighting, and serial correlation.

This equation also explains why attempts to resolve a fight quickly can backfire. When you are fighting, the normal reaction to resolve it is by committing actions that indicate that you are actually nice. The problem is that the equation above has both P(E|N) and P(E|J) in it.

So, in order to resolve a fight, you should not only commit actions that you would do when you are perceived nice, but also actions that you would NOT do if you are a jerk. In other words, the easiest way to pull P(J) down in the above equation is to commit E with high P(E|N) and low P(E|J).

What complicates things is that if you use one such weapon too many times, the partner will will begin to see through you, and up her P(E|J) for this event. So you need to keep coming up with new tricks to defuse fights.

In short, that serial correlation exists in relationship fights is a given, and there is little you can do to prevent it. So if you go through a long period of continuous disagreement with your partner, keep in mind that such things are par for the course, and don’t do something drastic like breaking up.

Uber’s anchoring problem

The Karnataka transport department has come out with a proposal to regulate cab aggregators such as Uber and Ola. The proposal is hare-brained on most  counts, such as limiting drivers’ working hours, limiting the number of aggregators a driver can attach himself to and having a “digital meter”. The most bizarre regulation, however, states that the regulator will decide the fares and that dynamic pricing will not be permitted.

While these regulations have been proposed “in the interest of the customer” it is unlikely to fly as it will not bring much joy to the customers – apart from increasing the number of auto rickshaws and taxis in the city through the back door. I’m confident the aggregators will find a way to flout these regulations until a time they become more sensible.

Dynamic pricing is an integral aspect of the value that cab aggregators such as Uber or Ola add. By adjusting prices in a dynamic fashion, these aggregators push information to drivers and passengers regarding demand and supply. Passengers can use the surge price, for example, to know what the demand-supply pattern is (I’ve used Uber surge as a proxy to determine what is a fair price to pay for an auto rickshaw, for example).

Drivers get information on the surge pricing pattern, and are encouraged to move to areas of high demand, which will help clear markets more efficiently. Thus, surge pricing is not only a method to match demand and supply, but is also an important measure of information to a cab aggregator’s operations. Doing away with dynamic pricing will thus stem this flow of information, thus reducing the value that these aggregators can add. Hopefully the transport department will see greater sense and permit dynamic pricing (Disclosure: One of my lines of business is in helping companies implement dynamic pricing, so I have a vested interest here. I haven’t advised any cab aggregators though).

That said, Uber has a massive anchoring problem, because dynamic pricing works only in one way. Anchoring is a concept from behavioural economics where people’s expectations of something are defined by something similar they have seen (there is an excellent NED Talk on this topic (by Prithwiraj Mukherjee of IIMB) which I hope to upload in its entirety soon). There are certain associations that are wired in our heads thanks to past information, and these associations bias our view of the world.

A paper by economists at NorthEastern University on Uber’s surge pricing showed that demand for rides is highly elastic to price (a small increase in price leads to a large drop in demand), while the supply of rides (on behalf of drivers) is less elastic, which makes determination of the surge price hard. Based on anecdotal information (friends, family and self), elasticity of demand for Uber in India is likely to be much higher.

Uber’s anchoring problem stems from the fact that the “base prices” (prices when there is no surge) is anchored in people’s minds. Uber’s big break in India happened in late 2014 when they increased their discounts to a level where travelling by Uber became comparable in terms of cost to travelling by auto rickshaw (the then prevalent anchor for local for-hire public transport).

Over the last year, Uber’s base price (which is cheaper than an auto rickshaw fare for rides of a certain length) have become the new anchor in the minds of people, especially Uber regulars. Thus, whenever there is a demand-supply mismatch and there is a surge, comparison to the anchor price means that demand is likely to drop even if the new price is by itself fairly competitive (compared to other options at that point in time).

The way Uber has implemented its dynamic pricing is that it has set the “base price” at one end of the distribution, and moves price in only one direction (upwards). While there are several good reasons for doing this, the problem is that the resultant anchoring can lead to much higher elasticity than desired. Also, Uber’s pricing model (more on this in a book on Liquidity that I’m writing) relies upon a certain minimum proportion of rides taking place at a surge (the “base price” is to ensure minimum utilisation during off-peak hours), and anchoring-driven elasticity can’t do this model too much good.

A possible solution to this would be to keep the base fare marginally higher, and adjust prices both ways – this will mean that during off-peak hours a discount might be offered to maintain liquidity. The problem with this might be that the new higher base fare might be anchored in people’s minds, leading to diminished demand in off-peak hours (when a discount is offered). Another problem might be that drivers might be highly elastic to drop in fares killing the discounted market. Still, it is an idea worth exploring – in my opinion there’s a sweet spot in terms of the maximum possible discount (maybe as low as 10%, but I think it’s strictly greater than zero)  where the elasticities of drivers and passengers are balanced out, maximising overall revenues for the firm.

We are in for interesting days, as long as stupid regulation doesn’t get in the way, that is.

Cash on delivery

One of the big problems in the Indian e-commerce industry is that a lot of business happens through the “Cash on delivery” model, where the customer pays for the goods upon receiving it rather than at the time of ordering. According to sources, nearly three fourth of e-commerce in India is paid for using this model.

The problem with Cash On Delivery (COD) is that it leads to higher product returns and non-deliveries, since the recipient is not pre-committed to accepting it. While e-commerce vendors might try methods such as blacklisting customers in order to cut their losses, there is no clear solution in sight. COD is also a massive source of fraud, especially given that currently e-commerce platforms are more likely to subsidise rather than take a cut of transactions on their platforms.

One of the reasons given for the development of the COD model is the low credit card penetration in India (compared to other markets), and Indians’ unease with transmitting money online. Research (which I can’t be bothered to find and link to right now) shows that the Indian e-commerce industry actually took off once CoD was introduced.

Given that India is developing some new and innovative payment systems (the Immediate Payment System (IMPS) is one. There is a Unified Payments Interface (UPI) which is even better which is coming up), it will be interesting to see how the e-commerce industry in India shapes up from a payment standpoint.

There are two factors that drive CoD – one is the ease of payment transaction – you just hand over the cash to the courier when the goods are delivered. This is not seamless, of course, since it could involve problems involving change, and handling of large amounts of hard currency which makes it unsafe.

The other factor is trust – Indians don’t seem to trust vendors enough to pay for their goods before they receive them. While not prepaying gives the option to change mind at a later date, this can lead to significant friction in the system resulting in costs that are likely to get added to the customer (this doesn’t happen right now since platforms are still in heavy subsidy mode).

By paying for goods on delivery, the customer hedges against fraud by the vendor, and the transaction is smoother from the customer’s perspective.

While the industry claims that CoD is primarily due to lack of credit card penetration, my hypothesis is that it is more due to the trust factor. So far there has been no method (apart from possibly surveys which are internal to e-commerce platforms and which will never get disclosed) to understand which of the two it is.

With the development of new and innovative payment platforms, and the ability for a large number of people in India to transact online (willingness is another matter), this hypothesis can be tested. Once people have access to mobile apps that let them make instant and secure inter-bank payments (we are already on our way there), the low credit card penetration is unlikely to be a constraint against pre-payment for goods. If my hypothesis is true, the proportion of CoD will not fall despite the growth of these new payment methods.

There are flies in the ointment of course – platforms, driven by losses, are investing in moving customers away from CoD, so the data might not be very clear. Also, over time, people may develop more trust in e-commerce companies and start pre-paying, which will not tell us anything about their confidence levels right now.

We are in for interesting times!

PS: Like telecommunications (where most of India skipped the landline) and retail (where India skipped the “walmart step”), the payments industry in India is also likely to “leapfrog”, with a large part of the country set to bypass credit cards altogether.

Inequality in income and consumption

My hypothesis is that while inequality in terms of income or wealth (measured in rupees/dollars) has been growing, consumption inequality is actually coming down. I hope to do a more detailed analysis using data, but I’ll stick to an anecdote for this this introductory blogpost.

The trigger for this thought came about a year back, at a meeting in one of the organisations I’m associated with. The meeting wasn’t terribly interesting, so I spent time checking out the guy sitting next to me, whose Net Worth I knew is at least a couple of orders of magnitude more than mine.

He was wearing a Louis Philippe shirt, and I have several shirts of that brand. He had a Parker pen, and I use a Parker too. He had a rather fancy watch whose brand I do not recall now, but my Seiko isn’t that bad in comparison. And he had an iPhone, which cost four times as much as the phone I used then (a Moto G), but not out of reach for me.

I can go on but the gist is that while our income and wealth levels were different by an order of magnitude, our consumption wasn’t all that far off. I must admit that I’m also a so-called “1-percenter” in terms of income (recall a study which said that 99th percentile of income in India is Rs. 12 lakh per annum), so I was also part of the power law tail, yet the marginal difference in consumption to income levels was strikingly low.

Since this is an introductory blog post on this topic, I posit that this is a more general trend and applies at many other levels. The thing with inequality is that income (and wealth) is usually distributed according to a Power Law (unless the state is extremely coercive and extractive), so as the economy grows, inequality as measured by measures such as the Gini coefficient is bound to increase (here’s a nice but hard-to-read paper by Nassim Nicholas Taleb on why the Gini coefficient is flawed for fat-tailed distributions such as the power law).

Yet, as the economy grows, more people are pushed beyond a “basic level” of income where they are able to afford “necessities” (and certain kinds of luxuries), so inequality as measured by consumption will actually be lower. The challenge is in measuring such inequality appropriately.

I’ll mention a couple of more anecdotes in support of this. One sector where inequality has fallen is in commute. Some rich old-time Bangaloreans look back in nostalgia at a time when there was no congestion on the streets of Bangalore, and how the city has since deteriorated. Yet, that congestion-free travel was then available only to the extremely wealthy (who could afford private vehicles) or lucky (my father waited for four years to get his first scooter because of limited supply). Public transport infrastructure was abysmal and buses infrequent.

Now, a larger proportion of the population can afford private vehicles and public transport has also improved (though not by much), making life better at the lower end of income/wealth levels. And the rich (who had exclusive access to roads in private cars earlier) are faced with higher congestion.

Another obvious example is the telephone. Very few people had them even twenty years back (we applied for ours in 1989, only to get “allotted” a phone in 1995), and now pretty much everyone has a basic mobile phone now (and with cheaper smart phones, even some relatively poor people own smart phones).

This is a theory worth pursuing. Need to analyse how to collect data and measure inequality, but I think there’s something to this hypothesis. Any thoughts will be welcome!

Letters to my wife

As I turned Thirty Three yesterday, my wife dug up some letters (emails to be precise) I’d written to her over the years and compiled them for me, urging me to create at “Project Thirty Four” (on the lines of my Project Thirty). What is pleasantly surprising is that I’ve actually managed to make a life plan for myself, and execute it (surprising considering I don’t consider myself to be too good a planner in general).

In February 2011, after having returned from a rather strenuous work trip to New York, this is what I had to say (emphasis added later, typos as in original):

For me steady state is when I’ll be doing lots of part-time jobs, consulting gigs, where I’m mostly owrking from home, getting out only to meet people, getting to meet a lot of people (somethign taht doesn’t happen in this job), having fun in the evenings and all that

I wrote this six months before I exited my last job, and it is interesting that it almost perfectly reflects my life nowadays (except for the “have fun in the evenings” bit, but that can be put down to being long distance).

I’ve just started a part time job. I have a couple of consulting gigs going. I write for a newspaper (and get paid for it). I mostly work from home. I’ve had one “general catch up” a day on an average (this data is from this Quantified Life sheet my wife set up for me).

A week later I had already started planning what I wanted to do next. Some excerpts from a letter I wrote in March 2011:

Ok so I plan to start a business. I don’t know when I’ll start, but I’m targeting sometime mid 2012.

I want to offer data consultancy services.

Basically companies will have shitloads of data that they can’t make sense of. They need someone who is well-versed in working with and looking at data, who can help them make sense of all that they’ve got. And I’m going to be that person.

Too many people think of data analysis as a science and just through at data all the analytical and statistical weapons that they’ve got. I believe that is the wrong approach and leads to spurious results that can be harmful for the client’s business.

However, I think it is an art. Making sense of data is like taming a pet dog. There is a way you communicate with it. There is a way you make it do tricks (give you the required information). And one needs to proceed slowly and cautiously in order to get the desired results.

I think of myself as a “semi-quant”. While I am well-versed in all the quantitative techniques in data analysis and financial modeling, I’m also deeply aware that using quantitative tools indiscriminately can lead to mismanagement of risks, which can be harmful to the client. I believe in limited and “sustainable” use of quantitative tools, so that it can lead without misleading.

 

My past experience with working with data is that data analysis can be disruptive. I don’t promise results that will be of particular liking for the client – but I promise that what I diagnose is good for the client’s business. When you dig through mountains of data, you are bound to get some bitter pills. I expect my clients to handle the bad news professionally and not shoot the messenger.

I don’t promise to find a “signal” in every data set that I’m given. There are chances that what I’m working with is pure noise, and in case I find that, I’ll make efforts to prove that to the client (I think that is also valuable information).

And these paragraphs, written a full year before I started out doing what I’m doing now, pretty much encapsulate what I’m doing now. Very little has changed over nearly five years! I feel rather proud of myself!

And a thousand thanks to my wife for picking out these emails I had sent her and showing me that I can work to a plan.

Now on to making Project Thirty Four, which I hope to publish by the end of today, and hope to execute by the end of next year.

Kadlekai Parshe

This afternoon I visited the Kadlekai Parshe (groundnut fair) with the in-laws. It was the second time I was visiting the fair, and the first time during daylight (the last time was in 2011, with the wife, and I remember getting incredibly pained with the vuvuzelas all kids seemed to be blowing then). Some pertinent observations:

  1. Considering that it was a groundnut fair, an activity that all visitors could be expected to indulge in would be to buy groundnuts and eat them as they walked through the fair. Eating groundnuts has the externality of skins, and there weren’t enough dustbins to effectively dispose of the skins.
  2. As you might expect in a fair where you have a large number of shops selling pretty much the same goods, prices were largely the same. A litre of raw groundnuts (yes, that’s how whole groundnuts are measured and sold in Bangalore) cost Rs. 25 in most roadside shops, while a litre of roasted groundnuts cost Rs. 30.
  3. We ended up buying groundnuts from several shops, and the quality varied widely, though the price didn’t. Some had too many “buDDes” (groundnuts with underdeveloped nuts), some were not roasted well enough, some were roasted too much and so on. Yet, price didn’t vary by much. This is puzzling since it was possible to sample a couple of groundnuts before making the decision to buy.
  4. There were a lot of people and most of the road space was taken up by pedestrians. Yet, there were vehicles plying (well at a slower rate), leading to traffic jams all around. A better solution might have been to turn the stretch of Bull Temple Road between haLLi mane and Kamat Bugle Rock pedestrian only. Would’ve ensured greater safety and possibly faster traffic flows on alternate routes.
  5. In terms of food, there was a large number of chaat carts, carts selling slices of a kind of thick (15 inches diameter) edible root, carts selling potato chips (which looked quite good and reminded me of Prague where I remember buying similar chips at St. Wenceslas’s Square), etc. Being noon, none of them seemed to be doing much business. Hopefully they’d’ve had better luck in the evening
  6. Stalls were licensed, as I happened to see a license number on a “stall” (basically groundnuts heaped on the ground) selling groundnuts. This is a good move. We need full time licensing of city food carts.
  7. The entire stretch of BP Wadia Road bewteen Bugle Rock Park and BMS College for Women was occupied by Lambani tribespeople selling plaster of paris figurines. Again, around noon, not much business, but enough attention to block traffic on that road.
  8. There was a massive crowd going into the Big Ganesha and Big Bull temples. We steered clear and stuck to the peanuts.
  9. There were a few people with DSLRs clicking away (I was one of those on my last visit). No groups though.

It seems like a fairly fun event. With better management (traffic, dustbins) it can be even better.

Charging for parking

In a potentially interesting move, the Delhi government has declared that starting from 2016, only half the stock of Delhi’s cars will be allowed on the road each day, based on the parity of the number plate.

While in theory it might work, the dependence of Delhi people on cars, ownership of multiple cars and possible number-trading might render it moot. Also, given that not everyone uses their car every single day, a simple car swap arrangement (like Zipcar; but we need to figure out liability properly) might defeat this regulation.

The more sure-fire way to reduce the number of cars on the road is to impose a congestion surcharge but it it is not an easy regulation to implement – given that you’ll need electronic modes to collect tolls, devices in cars, etc (not that it hasn’t been done, but given India’s scale it’s considerable effort).

A better way to implement congestion surcharge is to charge economic rates for parking. In most cities in India nowadays, parking is highly subsidised (in terms of money) which results in more people taking their cars out, not being able to park them, and creating further congestion by driving around looking for a place to park (Brigade Road in Bangalore is a good example of parking-led congestion thanks to slow-moving cars looking for a place to park).

The question is what an economic rate for parking must be, and that can be determined by looking at the prevailing real estate rates in that area. In the area I live in Bangalore, for example, the “guidance value” (rate used by the municipal corporation to determine the “fair value” of a property in order to tax sales) is about Rs. 8000 per square foot.

Assuming a price to earnings ratio of 20, this translates to Rs. 400 per square foot per year, or little more than a rupee per square foot per day. A parking lot is about 9 feet wide and 18 feet long (based on US standards, assuming India is the same). Let us assume a 50% overhead for space needed to move the car in and out of the lot. Based on this, the “fair rent” for one car parking space in my area is 18 * 9 * 3/2 * 400 / 365 = ~Rs. 270 per day, or translates to around Rs. 11 per hour.

Notice that all the calculations above were either multiplications or divisions, and hence the per hour parking price is directly proportional to the guidance value of property in the area. Based on the numbers above, a good rule of thumb for “economic cost” of an hour parking space is 11 / 8000 or about 14 basis points (a basis point is one hundredth of a percentage point) of the per square foot guidance value.

Of course, there are transaction costs (of putting the car in and out) and demand varies by time of day (so we might have an element of “surge pricing”). Yet, what we have is a good rule of thumb to decide the per hour parking rates.

Politician salaries and corruption

Recently, Vijay Nair, CEO of OML Entertainment, which organises the popular NH7 Weekender music festivals, tweeted that the Weekender in Delhi last month was the first where he didn’t have to pay a single paisa of bribe.

Just before the event, Delhi Chief Minister Arvind Kejriwal gave a speech in which he stated that he had bought passes for the event by paying for them, and urged his ministers and other government officials to do the same rather than asking for freebies.

Yesterday the Delhi government increased salaries of its legislators and ministers by a factor of four (which the Economic Times incorrectly reported as a “400% increase” – it’s a 300% increase).

The basic monthly salary of Delhi MLAs and ministers is all set to go up by a whopping 400 per cent besides significant hike in a slew of allowances with the assembly today approving a bill to effect the increase considering rising cost of living.

The Delhi government will now send the bill to the Centre and if it is approved, then basic salaries of legislators will rise from current Rs 12,000 to Rs 50,000 …

The move to increase Delhi legislator salaries has been expectedly panned by opposition parties, but it is an important step in reducing corruption, the main plank on which the Delhi government came to power last year.

In order to better understand this, go back to Kejriwal’s statement before the Weekender urging officials to purchase their tickets and not ask for freebies. The on-ground price of a ticket for the Delhi Weekender was Rs. 3500.

As per existing salary structures, a Delhi MLA would have had to spend a quarter of his monthly basic salary for the event. If he were to buy tickets for his family of four, he would have to spend his entire monthly basic salary (I understand there are other components of compensation, too)! And this would have been good enough justification for him to ask for free tickets.

While a higher salary might still not prevent an MLA from demanding free tickets, his earlier moral justification for the demand doesn’t exist any more – since the new salary structure now makes the tickets affordable. This removal of moral justification is certain to have an impact on corruption at the margin.

More importantly, official salary levels have a massive impact on the kind of people the profession attracts. When you get paid a pittance as a politician, it repels people who are loathe to be corrupt – for it is next to impossible to make a decent living on such salaries. People will be loathe to leave well-paying jobs for politics, and the only people politics will attract are those that hope to make money on the side.

I hope the Centre approves the Delhi government’s proposal to increase salaries, and other states match this. Given the small number of legislators and ministers, fiscal impact will be marginal. But the impact it can have on corruption, in terms of removing the moral justification, and on the kind of people it will attract to politics will more than pay for this.

Hooke’s Curve, hooking up and dressing sense

So Priyanka and I were talking about a mutual acquaintance, and the odds of her (the acquaintance) being in a relationship, or trying to get into one. I offered “evidence” that this acquaintance (who I meet much more often than Priyanka does) has been dressing progressively better over the last year, and from that evidence, it’s likely that she’s getting into a relationship.

“It can be the other way, too”, Priyanka countered. “Haven’t you seen countless examples of people who have started dressing really badly once they’re in a relationship?”. Given that I had several data points in this direction, too, there was no way I could refute it. Yet, I continued to argue that given what I know of this acquaintance, it’s more likely that she’s still getting into a relationship now.

“I can explain this using Hooke’s Law”, said Priyanka. Robert Hooke, as you know was a polymath British scientist of the seventeenth century. He has made seminal contributions to various branches of science, though to the best of my knowledge he didn’t say anything on relationships (he was himself a lifelong bachelor). In Neal Stephenson’s The Baroque Cycle, for example, Hooke conducts a kidney stone removal operation on one of the protagonists, and given the range of his expertise, that’s not too far-fetched.

“So do you mean Hooke’s Law as in stress is proportional to strain?”, I asked. Priyanka asked if I remembered the Hooke’s Curve. I said I didn’t. “What happens when you keep increasing stress?”, she asked. “Strain grows proportional until it snaps”, I said. “And how does the curve go then”, she asked. I made a royal mess of drawing this curve (didn’t help that in my mind I had plotted stress on X-axis and strain on Y, while the convention is the other way round).

After making a few snide remarks about my IIT-JEE performance, Priyanka asked me to look up the curve and proceeded to explain how the Hooke’s curve (produced here) explains relationships and dressing sense.

“As you get into a relationship, you want to impress the counterparty, and so you start dressing better”, she went on. “These two feed on each other and grow together, until the point when you start getting comfortable in the relationship. Once that happens, the need to impress the other person decreases, and you start wearing more comfortable, and less fashionable, clothes. And then you find your new equilibrium.

“Different people find their equilibria at different points, but for most it’s close to their peak. Some people, though, regress all the way to where they started.

“So yes, when people are getting into a relationship they start dressing better, but you need to watch out for when their dressing sense starts regressing. That’s the point when you know they’ve hooked up”, she said.

By this point in time I was asking to touch her feet (which was not possible since she’s currently at the other end of the world). Connecting two absolutely unrelated concepts – Hooke’s Law and hooking up, and building a theory on that. This was further (strong) confirmation that I’d married right!