Simulating Covid-19 Scenarios

I must warn that this is a super long post. Also I wonder if I should put this on medium in order to get more footage.

Most models of disease spread use what is known as a “SIR” framework. This Numberphile video gives a good primer into this framework.

The problem with the framework is that it’s too simplistic. It depends primarily on one parameter “R0”, which is the average number of people that each infected patient infects. When R0 is high, each patient infects a number of other people, and the disease spreads fast. With a low R0, the disease spreads slow. It was the SIR model that was used to produce all those “flatten the curve” pictures that we were bombarded with a week or two back.

There is a second parameter as well – the recovery or removal rate. Some diseases are so lethal that they have a high removal rate (eg. Ebola), and this puts a natural limit on how much the disease can spread, since infected people die before they can infect too many people.

In any case, such modelling is great for academic studies, and post-facto analyses where R0 can be estimated. As we are currently in the middle of an epidemic, this kind of simplistic modelling can’t take us far. Nobody has a clue yet on what the R0 for covid-19 is. Nobody knows what proportion of total cases are asymptomatic. Nobody knows the mortality rate.

And things are changing well-at-a-faster-rate. Governments are imposing distancing of various forms. First offices were shut down. Then shops were shut down. Now everything is shut down, and many of us have been asked to step out “only to get necessities”. And in such dynamic and fast-changing environments, a simplistic model such as the SIR can only take us so far, and uncertainty in estimating R0 means it can be pretty much useless as well.

In this context, I thought I’ll simulate a few real-life situations, and try to model the spread of the disease in these situations. This can give us an insight into what kind of services are more dangerous than others, and how we could potentially “get back to life” after going through an initial period of lockdown.

The basic assumption I’ve made is that the longer you spend with an infected person, the greater the chance of getting infected yourself. This is not an unreasonable assumption because the spread happens through activities such as sneezing, touching, inadvertently dropping droplets of your saliva on to the other person, and so on, each of which is more likely the longer the time you spend with someone.

Some basic modelling revealed that this can be modelled as a sort of negative exponential curve that looks like this.

p = 1 - e^{-\lambda T}

T is the number of hours you spend with the other person. \lambda is a parameter of transmission – the higher it is, the more likely the disease with transmit (holding the amount of time spent together constant).

The function looks like this: 

We have no clue what \lambda is, but I’ll make an educated guess based on some limited data I’ve seen. I’ll take a conservative estimate and say that if an uninfected person spends 24 hours with an infected person, the former has a 50% chance of getting the disease from the latter.

This gives the value of \lambda to be 0.02888 per hour. We will now use this to model various scenarios.

  1. Delivery

This is the simplest model I built. There is one shop, and N customers.  Customers come one at a time and spend a fixed amount of time (1 or 2 or 5 minutes) at the shop, which has one shopkeeper. Initially, a proportion p of the population is infected, and we assume that the shopkeeper is uninfected.

And then we model the transmission – based on our \lambda = 0.02888, for a two minute interaction, the probability of transmission is 1 - e^{-\lambda T} = 1 - e^{-\frac{0.02888 * 2}{60}} ~= 0.1%.

In hindsight, I realised that this kind of a set up better describes “delivery” than a shop. With a 0.1% probability the delivery person gets infected from an infected customer during a delivery. With the same probability an infected delivery person infects a customer. The only way the disease can spread through this “shop” is for the shopkeeper / delivery person to be uninfected.

How does it play out? I simulated 10000 paths where one guy delivers to 1000 homes (maybe over the course of a week? that doesn’t matter as long as the overall infected rate in the population otherwise is constant), and spends exactly two minutes at each delivery, which is made to a single person. Let’s take a few cases, with different base cases of incidence of the disease – 0.1%, 0.2%, 0.5%, 1%, 2%, 5%, 10%, 20% and 50%.

The number of NEW people infected in each case is graphed here (we don’t care how many got the disease otherwise. We’re modelling how many got it from our “shop”). The  right side graph excludes the case of zero new infections, just to show you the scale of the problem.

Notice this – even when 50% of the population is infected, as long as the shopkeeper or delivery person is not initially infected, the chances of additional infections through 2-minute delivery are MINUSCULE. A strong case for policy-makers to enable delivery of all kinds, essential or inessential.

2. SHOP

Now, let’s complicate matters a little bit. Instead of a delivery person going to each home, let’s assume a shop. Multiple people can be in the shop at the same time, and there can be more than one shopkeeper.

Let’s use the assumptions of standard queueing theory, and assume that the inter-arrival time for customers is guided by an Exponential distribution, and the time they spend in the shop is also guided by an Exponential distribution.

At the time when customers are in the shop, any infected customer (or shopkeeper) inside can infect any other customer or shopkeeper. So if you spend 2 minutes in a shop where there is 1 infected person, our calculation above tells us that you have a 0.1% chance of being infected yourself. If there are 10 infected people in the shop and you spend 2 minutes there, this is akin to spending 20 minutes with one infected person, and you have a 1% chance of getting infected.

Let’s consider two or three scenarios here. First is the “normal” case where one customer arrives every 5 minutes, and each customer spends 10 minutes in the shop (note that the shop can “serve” multiple customers simultaneously, so the queue doesn’t blow up here). Again let’s take a total of 1000 customers (assume a 24/7 open shop), and one shopkeeper.

 

Notice that there is significant transmission of infection here, even though we started with 5% of the population being infected. On average, another 3% of the population gets infected! Open supermarkets with usual crowd can result in significant transmission.

Does keeping the shop open with some sort of social distancing (let’s see only one-fourth as many people arrive) work? So people arrive with an average gap of 20 minutes, and still spend 10 minutes in the shop. There are still 10 shopkeepers. What does it look like when we start with 5% of the people being infected?

The graph is pretty much identical so I’m not bothering to put that here!

3. Office

This scenario simulates for N people who are working together for a certain number of hours. We assume that exactly one person is infected at the beginning of the meeting. We also assume that once a person is infected, she can start infecting others in the very next minute (with our transmission probability).

How does the infection grow in this case? This is an easier simulation than the earlier one so we can run 10000 Monte Carlo paths. Let’s say we have a “meeting” with 40 people (could just be 40 people working in a small room) which lasts 4 hours. If we start with one infected person, this is how the number of infected grows over the 4 hours.

 

 

 

The spread is massive! When you have a large bunch of people in a small closed space over a significant period of time, the infection spreads rapidly among them. Even if you take a 10 person meeting over an hour, one infected person at the start can result in an average of 0.3 other people being infected by the end of the meeting.

10 persons meeting over 8 hours (a small office) with one initially infected means 3.5 others (on average) being infected by the end of the day.

Offices are dangerous places for the infection to spread. Even after the lockdown is lifted, some sort of work from home regulations need to be in place until the infection has been fully brought under control.

4. Conferences

This is another form of “meeting”, except that at each point in time, people don’t engage with the whole room, but only a handful of others. These groups form at random, changing every minute, and infection can spread only within a particular group.

Let’s take a 100 person conference with 1 initially infected person. Let’s assume it lasts 8 hours. Depending upon how many people come together at a time, the spread of the infection rapidly changes, as can be seen in the graph below.

If people talk two at a time, there’s a 63% probability that the infection doesn’t spread at all. If they talk 5 at a time, this probability is cut by half. And if people congregate 10 at a time, there’s only a 11% chance that by the end of the day the infection HASN’T propagated!

One takeaway from this is that even once offices start functioning, they need to impose social distancing measures (until the virus has been completely wiped out). All large-ish meetings by video conference. A certain proportion of workers working from home by rotation.

And I wonder what will happen to the conferences.

I’ve put my (unedited) code here. Feel free to use and play around.

Finally, you might wonder why I’ve made so many Monte Carlo Simulations. Well, as the great Matt Levine had himself said, that’s my secret sauce!

 

Why Border Control Is Necessary

India is shutting down its domestic flights today in order to stop the spread of Covid-19. This comes a day after shutting down the national railways and most inter-city buses. States and districts have imposed border controls to control the movement of people across borders.

The immediate reaction to this would be that this is a regressive step. After a few decades of higher integration (national and international) this drawing of borders at minute levels might seem retrogade. Moreover, the right of a citizen to move anywhere in India is a fundamental right, and so this closing of borders might seem like a violation of fundamental rights as well.

However, the nature of the Covid-19 bug is that such measures are not only permissible but also necessary. The evidence so far is that it has a high rate of transmission between people who meet each other – far higher than for any other flu. The mortality rate due to the illness the bug causes is also low enough that each sick person has the opportunity to infect a large number of others before recovery or death (compared to this, diseases such as Ebola had a much higher death rate, which limited its transmission).

So far no cure for Covid-19 has been found. Instead, the most optimal strategy has been found to prevent infected people from meeting uninfected people. And since it is hard to know who is infected yet (since it takes time for symptoms to develop), the strategy is to prevent people from meeting each other. In fact, places like Wuhan, where the disease originated, managed to stem the disease by completely shutting down the city (it’s about the size of Bangalore).

In this context, open borders (at whatever level) can present a huge threat to Covid-19 containment. You might manage to completely stem the spread of the disease in a particular region, only to see it reappear with a vengeance thanks to a handful of people who came in (Singapore and HongKong have witnessed exactly this).

For this reason, the first step for a region to try and get free of the virus is to “stop importing” it. The second step is to shut down the region itself so that the already infected don’t meet the uninfected and transmit the disease to them.

Also, a complete shutdown can be harmful to the economy, which has already taken a massive battering from the disease. So for this reason, the shutdown is best done at as small a level as possible, so that the overall disruption is minimised. Also different regions might need different levels of shutdown in order to contain the disease. For all these reasons, the handling of the virus is best done at as local a level as possible. City/town better than district better than state better than country.

And once the spread of the disease has been stopped in a region, we should be careful that we don’t import it after that, else all the good work gets undone. For this reason, the border controls need to remain for a while longer until transmission has stopped in neighbouring (and other) regions.

It’s a rather complex process, but the main points to be noted are that the containment has to happen at a local level, and once it has been contained, we need to be careful to not import it. And for both these to happen, it is necessary that borders be shut down.

The future of work, and cities

Ok this is the sort of speculative predictive post that I don’t usually indulge in. However, I think my blog is at the right level of obscurity that makes it conducive for making speculative predictions. It is not popular enough that enough people will remember this prediction in case this doesn’t come through. And it’s not that obscure as well – in case it does come through, I can claim credit.

So my claim is that companies whose work doesn’t involve physically making stuff haven’t explored the possibilities of remote work enough before the current (covid-19) crisis hit. With the gatherings of large people, especially in air-conditioned spaces being strongly discouraged, companies that hadn’t given remote working enough thought are being forced to consider the opportunity now.

My prediction is that once the crisis over and things go back to “normal”, there will be converts. Organisations and teams and individuals who had never before thought that working from home would have taken enough of a liking to the concept to give it a better try. Companies will become more open to remote working, having seen the benefits (or lack of costs) of it in the period of the crisis. People will commute less. They will travel less (at least for work purposes). This is going to have a major impact on the economy, and on cities.

I’m still not done with cities.

For most of history, there has always been a sort of natural upper limit to urbanisation, in the form of disease. Before germ theory became a thing, and vaccinations and cures came about for a lot of common illnesses, it was routine for epidemics to rage through cities from time to time, thus decimating their population. As a consequence, people didn’t live in cities if they could help it.

Over the last hundred years or so (after the “Spanish” flu of 1918), medicine has made sufficient progress that we haven’t seen such disease or epidemics (maybe until now). And so the network effect of cities has far outweighed the problem of living in close proximity to lots of other people.

Especially in the last 30 years or so, as “knowledge work” has formed a larger part of the economies, a disproportionate part of the economic growth (and population growth) has been in large cities. Across the world – Mumbai, Bangalore, London, the Bay Area – a large part of the growth has come in large urban agglomerations.

One impact of this has been a rapid rise in property prices in such cities – it is in the same period that these cities have become virtually unaffordable for the young to buy houses in. The existing large size and rapid growth contribute to this.

Now that we have a scary epidemic around us, which is likely to spread far more in dense urban agglomerations, I imagine people at the margin to reconsider their decisions to live in large cities. If they can help it, they might try to move to smaller towns or suburbs. And the rise of remote work will aid this – if you hardly go to office and it doesn’t really matter where you live, do you want to live in a crowded city with a high chance of being hit by a stray virus?

This won’t be a drastic movement, but I see a marginal redistribution of population in the next decade away from the largest cities, and in favour of smaller towns and cities.It won’t be large, but significant enough to have an impact on property prices. The bull run we’ve seen in property prices, especially in large and fast-growing cities, is likely to see some corrections. Property holders in smaller cities that aren’t too unpleasant to live in can expect some appreciation.

Oh, and speaking of remote work, I have an article in today’s Times Of India about the joys of working from home. It’s not yet available online, so I’ve attached a clipping.

Schools and Officers’ Wives

I’m reading this fascinating interview in the Financial Times (possibly paywalled) with my former super^n-boss Lloyd Blankfein. It’s full of interesting nuggets, as well as fodder for people who want to criticise him.

I must admit right up front – I’m a big fan of Lloyd’s. This has nothing to do with the fact that I briefly worked for Goldman when he was CEO (I had even asked him a “planted question” when he had given a talk to the office sometime during my tenure there). In general, I think he says things as they are, and his twitter account is rather entertaining as well.

Anyway, the first statement in the interview that caught my attention was this statement about why the quality of schooling has gone down over the years. “He explains that the schools were only good because the women who staffed them were blocked from jobs in business and industry.” This is complementary to a view that I’ve strongly endorsed for a while.

Let me explain this using examples from India. Long long ago, maybe until the 1940s and 1950s, most school teachers in India were men. Way too few women had the kind of education that would qualify them to teach in schools. Moreover, back then, teaching paid sufficiently to run a (at least lower middle class) family on a single income.

In the 1950s and 1960s, women in India started going to college, and started entering the workforce. Mind that it was still a massively patriarchal society here back then, and women were expected to do their “household duties” in addition to bringing home a secondary income. And this meant that many of them were in the market for jobs that offered good work-life balance.

Teaching in schools offered that sweet spot – it required credentials, and the woman’s degrees would help in that. The hours weren’t too long. There would be ample vacations through the year. Schools were found everywhere, so the job was location-independent to a large extent. This last bit was important since the women’s husbands would frequently be employed in government jobs that were transferable, and the women’s “secondary careers” meant that they would be forced to move along.

And so we saw the rise of a class of teachers that I’ve come to call (not very politically correctly) as “officers’ wives”. These were well educated women, married to well educated men who held good jobs. They were passionate about their jobs, and went about it with a sense of purpose that went well beyond making money. This meant that the standard of teaching overall was raised.

And most importantly, this increased standard of teaching came without a corresponding increase in cost. The marginal utility to the family of this secondary source of income wasn’t particularly high, so this class of teachers didn’t demand very highly in terms of wages. In any case, they were doing their job out of passion rather than for the money, and would be willing to accept below-market wages to go about their jobs.

Then, two things happened. Firstly, the presence of employees who weren’t in it solely for the money pushed down average wages, and teachers for whom teaching was the sole source of family income started getting crowded out of the market. Secondly, with liberalisation in the 1990s, the nature of the job market itself suddenly changed.

One reason why the “officers’ wives” took to teaching was that it was hard to find other employment that was commensurate to their education that gave them the flexibility they desired (if you’re a secondary income earner you need that flexibility). With the market opening up, there was suddenly a number of options available to these people that matched their skill and flexibility needs. For example, my 11th standard physics teacher quit the school midway through the year to take up a job as a software tester at Wipro (this was in 1998-99).

So, rather suddenly, the opportunity cost of teaching shot up since the teachers suddenly many more options. It wasn’t possible for schools to jack up fees at the same time to be able to continue to afford the same teachers. And so, supply of quality teachers dropped. And consequently, the average quality of teachers (holding the schools constant) dropped as well.

Putting it in another way, schools nowadays need to compete with a much larger and much more diverse set of employers for their teachers. Many of them, for the sort of fees they charge, are simply unable to do so. The “passionate bunch” has found other avenues to exhibit their passion.

And the problem continues. And from what Lloyd says, it isn’t only India that is seeing this drop in quality of teaching – the US sees that as well. It was a sort of repressed larger market that had artificially pushed up the quality of school teachers, and the drop in repression has meant that the quality of teaching has dropped.

I will leave you with the concept of Baumol’s Cost Disease.

Linear Separation of Children and Adults

The other day, we took our daughter to her classmate’s birthday party. Since she is still relatively new in the school, and we don’t yet know too many of the other parents, both of us went, with the intention that we could talk to and get to know some of her classmates’ parents.

Not much of that happened, and based on our experience at two other recent children’s parties, it had to do with the physical structuring of children and adults at the party.

As Matt Levine frequently likes to say, everything is in seating charts.

Not easily finding too many other parents to talk to, the wife and I decided to have a mutual intellectual conversation on what makes for a “successful” kids’ birthday party. Based on four recent data points, the answer was clear – linear separation.

At our daughter’s little party at our home four months back, we had set up the balcony and the part of the living room closest to it with all sorts of sundry toys, and all the children occupied that space. The adults all occupied the other (more “inner”) part of the living room, and spoke among each other. Maybe our Graph Theory helped, but to the best of the knowledge, most adults spoke to one another, though I can’t tell if someone was secretly bored.

At the other party where we managed to network a fair bit with daughter’s classmate’s parents, there was simply one bouncy castle at one end of the venue. All the children were safely inside that castle. Parents had the other side of the venue all to themselves, and it was easy to talk to one another (most people were standing, and there were soft drinks on offer, which made it easy to walk around between groups and talk to a wide variety of people).

In the recent party where we concocted this theory, the children were in the middle of the venue, and around that, chairs had been set up. This radial separation was bad for two reasons – firstly, you were restricted to talking  to people in your own quadrant since it was impractical to keep walking all the way around since most of the central space was taken up by the kids. Secondly, chairs meant that a lot of the parents simply put NED and sat down.

It is harder to approach someone who is seated and strike up a conversation, than doing so to someone who is standing. Standing makes you linear, and open (ok I’m spouting some fake gyaan I’d been given during my CAT interview time), and makes you more approachable. Seating also means you get stuck, and you can’t go around and network.

So parents and event managers, when you are planning the next children’s party, ensure that the children and adults are linearly separable. And unless the number of adults is small (like in the party we hosted – which happened before we knew any of the daughter’s friends from school), make them stand. That will make them talk to each other rather than get bored.

 

“Principal Component Analysis” for shoes

OK, this is not a technical post. This is more in the realm of “life hacks“. It has everything to do with an observation I made a couple of months back, and how that has helped significantly combat decision fatigue.

I currently own eight pairs of shoes, which is perhaps a lifetime high. And lifetime high means that I was spending a lot of time each time I went out on which shoe to wear.

I have two pairs of open shoes, which I can’t wear for long periods of time, but are convenient in terms of time spent in wearing and taking off. I have two pairs of “semi-formal” ankle-high shoes – one an old pair that refuse to wear out, and another a rather light new one with sneaker bottoms. There are two pairs of “formal shoes”, one black and one brown. And then there are two sneakers – one pair of running shoes and one more general-purpose “fancy” one (this last one looks great with jeans, but atrocious with chinos, which I wear a lot of).

The running shoes have resided in my gym bag for the last nine months, and I use them exclusively indoors in the gym. So they’re “sorted”.

The problem I was facing was that among my seven other pairs of shoes I would frequently get confused on which one to wear. I would have to evaluate the fit with the occasion, how much I would have to stand (I need really soft-bottom shoes if I’ve to stand for a significant period of time), what trousers I was wearing and all such. It became nerve-wracking. Also, our shoe box, which was initially designed for two people and now serves three, placed its own constraints.

So as I somehow cut through the decision fatigue and managed to wear some shoes while stepping out of home, I noticed that a large proportion of the time (maybe 90%) I was wearing only three pairs of shoes. The other shoes were/are still good and I wouldn’t want to give them away, but I found that three shoes would serve the purpose on most occasions.

This is like in principal component analysis, where a small number of “components” (linear combination of variables) predict most of the variance in all the variables put together. In some analysis, you simply use these components rather than all the variables – that rather simplifies the analysis and makes it more tractable.

Since three pairs of shoes would serve me on 90% of the occasions, I decided it was time to take drastic action. I ordered a set of shoe bags from Amazon, and packed up four pairs of shoes and put them in my wardrobe inside. If I really need one of those four, it means I can put the effort at that point in time to go get that from inside. If not, it is rather easy to decide among the three outside on which one to wear (they’re rather dissimilar from each other).

I no longer face much of a decision when I’m stepping out on what shoes to wear. The shoe box has also become comfortable (thankfully the wife and daughter haven’t encroached on my space there even though I use far less space than before). Maybe sometime if I get really bored of these shoes outside, I might swap some of them with the shoes inside. But shoe life is much more peaceful now.

However, I remain crazy in some ways. I still continue to shop for shoes despite owning a lifetime high number of pairs of them. That stems from the belief that it’s best to shop for something when you don’t really need it. I’ll elaborate more on that another day.

Meanwhile I’m planning to extend this “PCA” method for other objects in the house. I’m thinking I’ll start with the daughter’s toys.

Wish me luck.

Schelling segregation on High Streets

We’ve spoken about Thomas Schelling’s segregation model here before. The basic idea is this – people move houses if not enough people like them live around them. A simple rule is – if at least 3 of your 8 neighbours around you aren’t like you, you move.

And Schelling’s insight was that even such a simple rule – that you only need more than a third of neighbours like yourself  to stay in your place, when applied system wide, can quickly result in near-complete segregation.

I had done a quick simulation of Schelling’s model a few years back, and here is a picture from that

Of late I’ve started noticing this in retail as well. The operative phrase in the previous sentence is “I’ve started noticing”, for I think there is nothing new about this phenomenon.

Essentially retail outlets want to be located close to other stores that belong to the same category, or at least the same segment. One piece of rationale here is spillovers – someone who comes to a Louis Philippe store, upon not finding what they want, might want to hop over to the Arrow store next door. And then to the Woodland store across the road to buy shoes. And so on.

When a store is located with stores selling stuff targeted at a disjoint market, this spillover is lost.

And then there is the branding issue. A store that is located along with more downmarket stores risks losing its own brand value. This is one reason you see, across time, malls becoming segmented by the kind of stores they have.

A year and half back, I’d written about how the Jayanagar Shopping Complex “died”, thanks to non-increase of rents which resulted in cheap shops taking over, resulting in all the nicer shops moving out. In that I’d written:

On the other hand, the area immediately around the now-dying shopping complex has emerged as a brilliant retail destination.

And now I see this Schelling-ian game playing out in the area around the Jayanagar Shopping Complex. This is especially visible on two roads that attract a lot of shoppers – 11th main and 30th cross (which intersect at the Cool Joint junction).

These are two roads that have historically had a lot of good branded stores, but the way they’ve developed in the last year or so is interesting.

I don’t know if it has to do with drainage works that have been taking forever, but 32nd Cross seems to be moving more and more downmarket. A Woodland’s shoe store moved out. As did a Peter England store. Shree Sagar, which once served excellent chaats, now looks desolate.

The road has instead been taken over by stores selling “export reject garments” and knock down brands. And as I’ve observed over the last few months, these kind of shops continue take over more and more of the retail space on that road. In that sense, it is surprising that a new Jockey store took over three floors of a building on that road – seems completely out of character there. I expect it to move in short order.

I must mention here that over the last few years, the supply of retail space in Jayanagar has exploded, and that has automatically meant that all kinds of brands have space to operate there. It was only natural that a process takes place where certain roads become more upmarket than others.

Nevertheless, the way 30th cross (between 10th and 11th mains) and 10th main have visibly evolved over the last year or so is rather interesting.

Evolution of sports broadcasting

I had a pleasant surprise yesterday morning when I was watching the highlights of Liverpool’s 4-0 victory at Leicester. The picture quality suddenly looked better. The production aesthetics in the first few seconds (before coverage of the actual match began) looked “American”. I doubted myself for a minute if this was actually English football I was watching.

And then I remembered that the pictures for this  game came from Amazon Prime. The streaming service had got rights to broadcast two full rounds of Premier League games this season, making a small chink in the duopoly of Sky Sports and BT Sport.

Traditional media wasn’t too impressed by it. Streaming necessarily meant a small delay in broadcast, and that made it less exciting for some viewers. The Guardian predictably made a noise about the “corporate takeover” with Amazon’s entry. From all the reports I read (mostly across the Guardian and the Athletic), commentators seemed intent on picking holes in Amazon’s performance.

That said, the new broadcaster also brought a fresh production aesthetic. While there were the inevitable teething problems (I must confess I didn’t watch these games live – being midweek evening games, they were very late night in India), Amazon for sure brought some new ideas into the broadcast.

Just like Fox Sports had done when it had done a big launch into NFL broadcasting in the early 90s. Read this oral history of that episode. It’s rather fantastic. Among the “innovations” that Fox Sports brought into American broadcasting (based on its sports broadcast in Australia, primarily) was this box at the corner showing the time and the live score. The thing wasn’t initially well received, but is now a fixture.

For evolution to happen, you need sex. And that means mixing things up, in ways they weren’t mixed before. If we were all the children of a super-god and a super-goddess, we would all be pretty much the same since the amount of “innovation” that could happen would be limited. And things would be boring, and static. Complex forms such as human beings could have never happened.

It is similar in business, and sports broadcasting, as well. When you have the same channels covering the same sports, they get into well-set local optima, and nothing new is tried. There is no necessity for improvement in that sense.

When new players comes in, preferably from another market, however, they see the need to differentiate themselves, and bring in ideas from their former market. And this leads to a crossover of ideas. In their efforts to stand out and make an impact, they might also bring in some ideas never seen anywhere – “mutations” in the evolutionary sense.

A lot of them don’t make sense and they die out. Others score unexpected hits and catch on. And that way, this memetic evolution leads to better business.

The great thing about memetic evolution is that while bad ideas come along much more often than good ideas, they get discarded fairly quickly, while the good ideas live on. And that leads to overall better products.

Right now in India we have a duopoly in sports broadcasting, controlled by the Star family and the Sony family. I’ve ranted several times about how the latter is absolutely atrocious and does nothing to improve the game. Hopefully a new player getting rights of some sport here will shake things up and bring in fresh ideas. Even if some of the ideas turn out to be bad, there will be plenty of good ideas.

Check out the highlights of the Leicester-Liverpool game, and you’ll get an idea.

Fancy stuff leads to more usage

A couple of months back, I decided to splurge a bit and treat myself to a pair of AirPods. Not the Pro version, which hadn’t yet been released, but this was the last generation. For someone who had hardly ever bought earphones in life (mostly using the ones that came bundled with phones), and for someone who would incessantly research before buying electronics, this counted as an impulse purchase.

A few months back a friend had told me that he had researched all the earphones in the market, and concluded that the best one for making calls is the AirPods. As it happens, he has an Android phone, and so decided it’s not worth it in the absence of an iPhone. And when he told me this, I figured that with an all-Apple lineup of devices, this is something I should seriously consider.

In the past I’d never been that much of a earphone user, mostly using them to listen to music when seated with my laptop outdoors. I hardly ever used them with my phone (a cable jutting out of the pocket was cumbersome). Based on that rationale, when I was in the market for a pair last year, I ended up buying a random cheap pair.

What my AirPods have shown me is that having a good device makes you use it so much more.

The UX on the AirPods is excellent and intuitive. Right now, for example, they’re connected to my laptop as I listen to music while writing this. If I were to get a call right now, I can very quickly switch them to pair with my phone, and talk on. And then after the call it’s two clicks to get them back to pair with the laptop.

This kind of experience is something that cannot be quantified, and because you cannot quantify and compare this across competing devices, in deep research you can miss out on this. This is one of those points that Rory Sutherland makes in Alchemy, which I read last month. And you fail to appreciate things like experience until you have really experienced it.

The amazing UX on the AirPods, not to talk about the great sound, means that I’ve, in a month, used them far more than I’d use other earphones in a year. Even when alone at home, I don’t blast music on my computer now – it’s always through the AirPods. I sometimes wear them while going on walks (though long walks are reserved for introspection with nothing streaming through my ears).

I was in Mumbai on Tuesday, and on the flight on both ways, I listened to podcasts using the AirPods. I’m surprised I had never thought of the idea before – it’s incredibly neat since you can close your eyes and listen, and sleep at your leisure. On commutes between meetings in Mumbai, I listened to podcasts in taxis. And so on.

So this is a learning for the next time – when I’m researching for a product that I think I may not use frequently, I need to keep in mind that if I like it I will use it far more than whatever it replaces. And if that is going to make my life better, the premium I would have paid for it will be really really worth it.

Oh, and coming back to AirPods, one question I keep getting is if they’re easy to lose. Based on the evidence so far, the biggest risk on that count is the daughter running off with one or both of them and misplacing them somewhere!

Festive lunches

There was a point in time (maybe early childhood) when I used to look forward to going to weddings just for the food. Maybe my parents’ network was such that most weddings we went to served good food, or I was too young to be discerning, but I would love the food at most functions and absolutely belt it.

Of late things haven’t been so kind. Maybe the general standard of wedding lunches has fallen (the last “function” where I remember the food being spectacularly good was my sister-in-law’s wedding, and that was in early 2017), or I’ve become more discerning in terms of the kind of food I like, but it’s not the case any more.

Recently I had written about how several functions serve lunch and dinner really late, and that we should make it a habit to eat at home before we go for such functions. The other problem is that even when food is served promptly, it frequently leaves me rather underwhelmed.

It doesn’t have to always do the quality of cooking, though. For example, most of the food at the wedding I attended today was cooked really well, and was tasty, but it was perhaps the choice of menu that has left me rather underwhelmed and hungry even after eating a lunch with 3 different sweets!

The problem with Indian wedding food is that they are massive carb fests. The main dish, if one were to call it, is rice (people like my daughter don’t mind at all – she belted a whole load of plain rice today). And then there are accompaniments, most of which seem watered down (and really, what is it about functions just not serving huLi (sambar) nowadays? At least that’s usually reasonably think and has lentils in it).  And then there are sweets.

There are some fried items but they are served in such small quantities that you can’t really get “fat nutrition” from it. There is a token amount of ghee served at the beginning of the meal, but that’s about it! There’s not much protein and vegetables in the meal either.

So you “belt” the meal and fill yourself, only to find yourself hungry an hour later. And this has happened on the last four or five occasions when I’ve eaten “function food”.

Maybe it has to do with my regular diet which has of late become more “high density“, that I find these low density meals rather underwhelming. Maybe all the wedding meals I enjoyed came at a time when my regular diet was low density as well. Maybe people were more liberal with ghee and vegetables back then (this is unlikely since people in India are, on average, far more prosperous now than they were in my childhood).

Oh, and did I mention that my daughter belted copious amounts of plain rice at today’s lunch? An hour later she too was complaining of hunger. I guess I’ll let her figure out about density of food her own way!