Category: randomness

Getting monkeys off your back

I’m mortally scared every time I make pulav. Now, I’m reputed to be a pretty good pulav maker – at least the wife and the mother-in-law will vouch for this, and it is this reputation that puts pressure on me every time I stand throwing spices into the pressure cooker. “The law of averages will soon catch up with me”, I think, and hope that this is not the time it will catch up.

Normally, if you make pulav seven times, and each time make it better than the previous time, you begin to think you’re becoming an expert in that and you can do no wrong thenceforth. I don’t feel that way. Knowing myself fairly well, I know it’s nigh impossible for me to hit 100% accuracy in pretty much anything that I do – at best I can hit a 90%. That I got a “hit” seven times in a row means that the coin fell on the 90% side seven times, and even assuming a Markovian process (success or failure of this batch of pulav is unrelated to previous performances), it gives me a 10% chance of failure each time I make it!

The thing with making pulav in a pressure cooker is that when it comes out well, it comes out great, but it can go spectacularly wrong. I don’t use formal measures for the amount of rice and water I use – it is all based on rules of thumb (literally – sometimes I stick my thumb into the mixture in the pressure cooker to feel if the amount of water is right). And I know that if I put too much water, it can end up being a soggy mess. At the other end, it can end up not cooking at all, or worse, burning.

So when a couple of months back my pulav went marginally wrong (slightly watery, but not inedible) – it made me feel happy. It made me feel happy that the law of averages had caught up with me, and that it didn’t result in a spectacular failure! Sometimes when you know that you are due for a failure, it can be self-reinforcing and result in spectacular failures. So it helps to take a mild fall once in a while that gives you the assurance that you’re human after all, and doesn’t put undue pressure on you the next time.

So what do you think about your continued successes, in the kitchen, at the workplace, and elsewhere? Does that make you feel better or worse? Does it lead to a sense of hubris, or greater self-doubt? Do leave a comment here and let me know.

Why being on time is a wonderful thing

This post is NOT about Indigo airlines, though I do fly them fairly frequently (approximately once a month). It is about the general culture of timeliness, and how it can help all of us save time and money.

If you and I decide to meet at say, 1 pm tomorrow, what time are you likely to turn up? There are two factors to consider here – you don’t want to be too late since that will create a bad impression in my mind, and you wouldn’t want that. You don’t want to turn up too early, either, for you don’t want to end up waiting for me. So when you plan your travel to the place we are meeting, you will first estimate what time I’m likely to show up and then plan to turn up such that you’ll maximize the probability of turning up between the time I’m expected to show up and five minutes earlier.

Notice how this can change depending upon the culture of timeliness. If you and I know each other, and I know that you are a punctual person and vice versa, we will both make an attempt to time our travel so that we maximize our probability of being there before 1 pm (the appointed time). What if I think that you are perennially late? The problem here is that I need to not only shift the “mean” of when I want to get to the place, but the variance also changes!

Notice that in case I know you are habitually late, I’m unlikely to know precisely when you’re going to arrive. Say I estimate based on our past record that you might turn up any time between ten and twenty minutes after the appointed time. How will I now plan to arrive so that I arrive between five and zero minutes of the time when I expect you to arrive? My travel time to get to the place already creates one level of uncertainty and to that I need to add another level of uncertainty in terms of when you are expected to arrive! Thus, these two sources of variation end up adding up and I will either be late (in case I’m okay wtih that) or end up spending more time just waiting for you!

Essentially, because I know that I cannot precisely determine when you are likely to get there, I assume a variance of when you are likely to get there, and that variance will add to the variance of my travel time and thus I’ll have to give myself a larger buffer so that I need to be on time while not waiting for too long!

This is similar to what people in quantitative finance call “market price of risk”. Let me illustrate that again using travel time as an example. In case 1, travel time from my office to yours has a mean of 40 minutes and a variance of 10 minutes (let us assume it is normally distributed). In case 2, travel time from my office to yours has a mean of 40 minutes (same as above) but a variance of only 5 minutes. Let us assume I want to be on time for the meeting at least 97.5% of the time. What time should I leave in each case?

In the first case, the one sided 97.5% confidence interval for my travel time is 40 + 2 * 10 = 60 minutes, or I expect to take no more than 60 minutes 97.5% of the time. In the second case, however, it is only 50 (40  + 2 * 5) minutes! In the first case, if I want to ensure a 97.5% chance of being on time for our 1 pm meeting, I’ll need to leave my office at 12 noon, while in the second case I can leave a full ten minutes later!

You need to notice here is that in both cases, the average travel time is the same. The only thing that has changed is the variance. In the first case, because the variance of the travel time is larger, I need to leave earlier! Leaving ten minutes earlier is essentially the price I have to pay because of the larger variance!

Similarly, when there is a variance in my estimate of when you will arrive for the meeting, it adds to the variance of my travel time, and the total variance I need to consider for when I need to leave goes up! In other words, simply because there is a variance in when you will arrive for the meeting,  i will have to leave earlier to compensate for your variance!

What if we had a culture of being on time? Then, I would know that with a very high probability you would be there on time for the meeting, and that would reduce my overall variance, and make it easier for me to also be on time for the meeting!

Essentially, a culture of being on time can save time for both of us – simply because it eliminates the variability of when we will end up arriving for the meeting, and this saved time is reason enough to build a culture of punctuality.

Yet you have people who schedule back-to-back meetings that invariably cascade and ruin their reputations of being on time, and thus inconvenience themselves and their counterparties!

Serving Bangalore’s best Butter Masale Dose

If you were to do a ranking of Masale Dose in different restaurants in Bangalore, I would say that the clear winner would be the one served at The Restaurant Formerly Known as Central Tiffin Room (TRFKACTR, now known as Shree Sagar). Soaked in ghee (melted butter), extremely crisp on the outside and soft on the inside, and served with two awesome chutneys, it is an experience every visitor to Bangalore must experience (Warning: Not good for your lipid profile, though). Except if you go on a Sunday morning.

The first time I visited TRFKACTR was on a Sunday morning in early 2010. While I was quite impressed by the product itself, I wasn’t so impressed by the ambiance and the operations. It was a Sunday morning and the restaurant was crowded. People were waiting around all over the place waiting to get a seat. Waiters would do nothing to assist you to get a table. And once seated, service was inefficient and slow – the waiters didn’t show any urgency given the size of the crowd at hand. It would remain my last visit to TRFKACTR in close to two years.

And then I shifted my residence, and moved to a house within two kilometres of TRFKACTR . I’ve since visited the restaurant several times (I’ve lost count), and have come away impressed each time. On none of my subsequent visits have I had any complaints about the service and operations, either. I’ve got a table immediately (though usually shared with strangers, as is the practice in such restaurants), been relieved that the waiters are actually not in a hurry and leisurely enjoyed my Butter Masale Dose without being bothered by crowds waiting to grab my seat. In the process I’ve also understood why the waiters didn’t show any urgency on that crowded Sunday morning when I first visited.

I had breakfast at TRFKACTR this morning, and the restaurant looked like this:

 

While this is an extreme case – I went early on a drizzly morning, and the restaurant had just opened – the thing with TRFKACTR is that most of the time it runs at or just below capacity. On any given day, as long as it is not a Sunday morning, you can expect to find a seat as soon as you visit the restaurant. You get served at a leisurely pace (though not too leisurely – this restaurant relies on high table turnover), and can eat in peace.

We need to recognize that “business as usual” in TRFKACTR involves the restaurant running at or close to capacity, and the operations at the restaurant have been optimized for this. That operations are stretched on a Sunday morning is not bad planning by the restaurant – it is a conscious decision by the restaurant that the crowds are a once-in-a-week occurrence and they will not optimize for that. While it might make sense to learn and plan for a different set of procedures on Sunday morning, we need to keep in mind that kitchen and table capacity are limited (slow service at the table on my first visit was perhaps due to a constraint on kitchen capacity) and differential pricing for Sundays is unlikely to go down well with customers.

Instead, what has happened is that customers (the regulars, at least) have learnt that the restaurant is really crowded on Sunday mornings and have shifted their gratification via Butter Masale Dose to other days. It is very likely that a majority the crowd that still comes to the restaurant on a Sunday morning consists of “tourists” – non-regulars who want to see what the restaurant is like.

PS: I’ve visited the restaurant once again on a Sunday morning after that initial visit. I had gone alone, but found a seat immediately. It is a possibility that my perception that the restaurant is really crowded on all Sunday mornings suffers from small sample bias.

Correlation and the 1987 Stock Market Crash

Recently on this blog I had talked about the phenomenon of correlations, and how that can send financial models topsy-turvy. I had taken the example of additional cars on the road on a rainy day and had explained how in 2008 CDOs went bust as a fall in house prices led to mortgages defaulting together. Today I read this interesting post by JP Koning which attributes the stock market crash of 1987 (Black Monday) also to correlation, but of a different kind.

It basically have to do with how bubbles behave. When you know that the stock market is overheated, there are two things you can do. You can either choose to ride whatever is left of the bubble, and thus go long, or short the market and hope that the bubble has come towards its end. There are problems with both approaches – if you are long and the bubble bursts, you stand to lose significant money. On the other hand if you are short and the bubble continues, you can end up getting wiped out before the bubble bursts and offers you an opportunity to profit (as Keynes supposedly said – the market can remain irrational for longer than you can remain solvent).

Trading is difficult business during the times of a bubble. Every good trader knows that a bubble is on. Yet, they are faced with the above dilemma. They want to participate in the party as long as it lasts but leave before the house comes crashing down. But nobody knows when the house will crash. Some smart traders such as Taleb (no doubt backed by their banks’ deep pockets) simply buy put options and wait it out for the bubble to burst and make their money. Some get out of the market. But most remain, taking directional bets (in either direction) and not sure of whether they are going to get wiped out.

Suppose you are a trader in one such bubble, and you decide to use a mixed strategy of whether you go long or short. Let us assume that on four out of five days (randomly chosen) you are long the market, and you short the fifth day. Let us assume every trader follows a similar strategy, but strategies of no two traders is correlated. So on a given day, for every trader going short, there are four traders going long and thus the bubble continues (let us assume that each trader plays with the same amount of money). You can see where this is going. What if there is a day when for some reason more than the usual 20% of  the traders decide to go short?

Let us briefly revisit the house party analogy. There is a party on and you want to enjoy it for as long as possible. However, the house in which the party is going on is unstable, and as soon as the number of people in the house falls below a certain number, the house will collapse, crushing anyone still in there (yes, this is a weird house, but never mind). You go near the house and you see a large number of people having a gala time. You see that the number of people in the house far exceeds the threshold, and so you join the party. And thus the party swells.

Suppose you are now in the party, and you see a large number of people leaving. Suddenly, you realize that following their exit, the number of people left in the house will be not too much more than the threshold. If you stay on, you might end up holding up the house, you might reason, and you will want to leave with the large group. The only problem is that you are not alone in thinking such. Most other guests have also seen this large group leave, and want to accompany them on their way out.

Traders were aware that the crash of 1929 had also occurred in late October, and on a Monday. On the 19th of October 1987, Koning mentions in his blog, the Wall Street Journal published a graph of the stock market in the 1980s and superimposed it with a graph of the stock market in the 1920s, leading up to the stock market crash in 1929 (which led to the Great Depression). The two graphs looked similar, as you can see below.

This was all the trigger that the market needed. Suddenly, you have a day when every trader reads about the bursting of the 1929 bubble in the newspaper, and how the current market is similarly poised. Suddenly every trader is doubly conscious of the stock market bubble, and wants to get away. Instead of every trader playing a random strategy, where only 20% will want to short, on this particular day a much larger number of traders want to short. As they collectively short, the market falls significantly enough to tell everyone that the bubble is busting. Everyone else tries to join them as they try to rush out of the party house. The house duly crashes.

Once again, notice that this was a random system being held up by low correlation. Traders knew there was a bubble, but didn’t know when it would burst and thus played uncorrelated mixed strategies, which kept the market afloat. All it took was one newspaper article, which every trader happened to read. The correlation suddenly jumped, and the market moved decisively.

As an exercise at the end of this blog post, think of other systems which are similarly “held up” because of low correlation in people’s behaviour. It need not only be financial – remember the road on rainy day example I gave in my previous post. Then think of what might result in correlations that hold up these systems to collapse to 1, and how those systems will then respond. Please don’t, however, blame me for scaring you.

Correlations: In Traffic, Mortgages and Everything Else

Getting caught in rather heavy early morning traffic while on my way to a meeting today made me think of the concept of correlation. This was driven by the fact that I noticed a higher proportion of cars than usual this morning. It had rained early this morning, and more people were taking out their cars as a precautionary measure, I reasoned.

Assume you are the facilities manager at a company which is going to move to a new campus. You need to decide how many parking slots to purchase at the new location. You know that all your employees possess both a two wheeler and a car, and use either to travel to work. Car parking space is much more expensive than two wheeler parking space, so you want to optimize on costs. How will you decide how many parking spaces to purchase?

You will correctly reason that not everyone brings their car every day. For a variety of reasons, people might choose to travel to work by scooter. You decide to use data to make your decision on parking space. For three months, you go down to the basement (of the old campus) and count the number of cars, and you diligently tabulate them. At the end of the three months, you calculate that on an average (median), thirty people bring their cars to work every day. You calculate that on ninety five percent of the days there were forty or fewer cars in the basement, and on no occasion did the total number of cars in the basement cross forty five.

So you decide to purchase forty car parking spaces in the new facility. It is not the same set of people who bring their cars to work every day. In fact, each employee has brought his/her car to the workplace at least once in the last three months. What you are betting on here, however, is correlation, You assume that the reason Alice brings her car to office is not related to the reason Bob brings his car to office. To put it statistically, you assume that Alice bringing her car and Bob bringing his car are independent events. Whether Alice brings her car or not has no bearing on Bob’s decision to bring his car, and vice versa. And you know that even on the odd day when more than forty people bring their cars, there are not more than forty five cars, and you can somehow “adjust” with your neighbours to borrow the additional slots for that day. You get a certificate from the CEO for optimizing on the cost of parking space.

And then one rainy morning things go horribly wrong. Your phone doesn’t stop ringing. Angry staffers are calling you complaining that they have no place to park. Given the heavy rains that morning, none of the staffers have wanted to risk getting wet in the rain, and have all decided to bring their cars. Never before have they faced a problem parking so they are all confident that there will be no problem parking once they get to work, only to realize there is not enough parking space. Over a hundred employees have driven to work, and there are only forty slots to park.

The problem here, as you might discover, is that of correlation. You had assumed that Alice’s reason to get her car was uncorrelated to Bob’s decision. What you had not accounted for was the possibility that there could be an exogenous event that could suddenly drive the correlation from zero to one, thus upsetting all your calculations!

This is analogous to what happened during the Financial Crisis of 2008. Normally, Alice defaulting on her home loan is not correlated with Bob defaulting on his. So you take a thousand such loans, all seemingly uncorrelated with each other and put them in a bundle, assuming that 99% of the time not more than five loans will default. You then slice this bundle into tranches, get some of them rated AAA, and sell them on to investors (and keep some for yourself). All this while, you have assumed that the loans are uncorrelated. In fact, the independence was a key assumption in your expectation of the number of loans that will default and in your highest tranche getting a AAA rating.

Now, for reasons beyond your control and understanding, house prices drop. Soon it becomes possible for home owners to willfully default on their loans – the value of the debt now exceeds the value of their home. With one such exogenous event, correlations suddenly rise. Fifty loans in your pool of thousand default (a 1 in gazillion event according to your calculations that assumed zero correlation). Your AAA tranche is forced to pay out less than full value. The lower tranches get wiped out. This and a thousand similar bundles of loans set off what ultimately became the Financial Crisis of 2008.

The point of this post is that you need to be careful about assuming correlations. It is to illustrate that sometimes an exogenous event can upset your calculations of correlations. And when you go wrong with your correlations – especially those among a large number of variables, you can get hurt real bad.

I’ll leave you with a thought: assuming you live in a primarily two wheeler city (like Bangalore, where I live), what will happen to the traffic on a day when 10% more people than usual get out their cars?

Rare observations and observed distributions

Over the last four years, one of my most frequent commutes in Bangalore has been between Jayanagar and Rajajinagar – I travel between these two places once a week on an average. There are several routes one can take to get to Rajajinagar from Jayanagar, and one of them happens to be from the inside of Chamrajpet. However, I can count the number of times I’ve taken that route in the last four years on the fingers of one hand. This is because the first time I took that route I got stuck in a massive traffic jam.

Welcome to the world of real distributions and observed distributions. The basic concept is that if you observe a particular event rarely, the distribution you observe can be very different from the actual distribution. Take for example, the above example of driving through inner Chamrajpet. Let us say that the average time to drive through that particular road on a Saturday evening is 10 minutes. Let us say that 99% of the time on a Saturday evening, you take less than 15 minutes to drive through that road. In the remaining 1% of the time, you can take as much as an hour to drive through the road.

Now, if you are a regular commuter who drives through this road every Saturday evening, you will be aware of the distribution. You will be aware that 99% of the time you will take at most 15 minutes to get past, and base your routing decision based on that. When it takes an hour to drive past, you know that it is a rare event and discount it from your future calculations. If, however, you are an irregular commuter like me and happened to drive through that road on that one day when it took an hour you get past, you will assume that that is the average time it takes to get past! You are likely to mistake the rare event as the usual, and that can lead to suboptimal decisions in the future.

In his book The Black Swan, Nassim Nicholas Taleb talks about the inability of people to model for rare events. He says that the problem is that people underestimate the probability of rare events and fail to account for it in their models, leading to blow ups when they do occur. While I agree that is a problem, I contend that the opposite problem can also be not ignored. Sometimes you fail to recognize that what has happened to you is a rare event and thus end up with a wrong model.

Let me illustrate both problems with the same example. Think of a game where 99 times out of 100 you win a rupee. The rest of the time (i.e. 1%) you lose fifty rupees. Regular players of the game, who have “sampled” this enough will know the full distribution, and will take that into account when deciding on whether to play the game. Non-regular players, however, don’t have complete information.

Let us say there are a hundred cards. 99 of them have a +1 written on it, and the 100th has a -50. Let us suppose you pick ten cards. Ninety percent of the time, all ten cards you pick will be a “+1”, and you will conclude that all cards are “+1”. You will model for the game to give you a rupee each time you play. The other 10% of the time, however, you will draw nine +1s and one -50. You will then assume that the expected value of playing the game is Rs. -4 .1( (9 * 1  + 1 * (-50))/10 ). Notice that both times you are wrong in your inference!

So while it is important that you recognize black swans, it is also important that you don’t overestimate their probability. Always remember that if you are a rare observer, the distribution you observe may not reflect the real distribution.

Indigo’s Food Policy

My last few flights on Indigo Airlines have not been pleasant, at least from a food perspective. It is said about the airline that they put a great amount of thought into each of their processes, but while it might have been working earlier (I used to positively prefer Indigo’s food experience a while back) of late it doesn’t seem to be doing too well.

Firstly, I don’t have a problem with the food itself. I most definitely prefer Indigo’s cold sandwiches and Real Activ fruit juice to the reheated omelette/pulao that Jet Airways serves. It is much lighter on the stomach and feels healthier, and doesn’t give you that usual bad aftertaste of “airline food”. I also understand that it makes sense from the company’s perspective, since the lack of hot food reduces their cost of serving it and also makes the plane easier to clean.

The problem, however, is with the process. Firstly, Indigo has these “corporate program customers” (I’ve never understood how to get into one of these), whose meal is pre-paid. So you have stewardesses walking around with printouts to know who is eligible for a free meal. I’ve also noticed some kind of priority in terms of service – that the corporate program customers are served before others (which is logical, since they’ve already paid), which disrupts the flow.

Then there is the problem of cash management. For whatever reasons the price points are not in multiples of 50 (sandwiches cost Rs. 170, fruit juice Rs. 70), so change management (!!) is a huge problem. While they have credit card machines they don’t work uniformly, and end up causing further delays.

The biggest issue, however, is the choice! For probably good reason Indigo serves a variety of meals, enough variety that the menu runs up to a full page in their in flight “retail therapy magazine”. There are two problems that result from this – firstly, there is a problem of inventory. When you offer so much choice, how much of each type do you carry? I know there must be some science going into how many packets of ready-to-make Uppit they carry and how many chicken sandwiches. However, on days when I’m (unfortunately from a food perspective) seated in the vicinity of Row 14 or Row 30, it is reasonably unlikely that I don’t end up getting my preferred choice.

The second problem with the variety in food is the time lost in decision-making. “Give me a chicken sandwich. Oh, it isn’t there? Then give me biryani! Oh, but that’s a Ramen kind of thing? No I don’t want that. Give me cashew nuts. Not pepper flavour, give me chilli”. The amount of time it takes for a passenger on Indigo to decide on what to eat is significantly more than the corresponding time it takes for a passenger in a so-called full-service carrier (veg/non-veg). Again, it doesn’t help (from this perspective), that an Indigo flight operates with four stewards, as opposed to six in a “full-service” carrier of the same size.

Overall, it makes the entire process of ordering for, paying and getting a meal rather unpleasant for significant proportions of passengers. My solution to this would be two-fold. Firstly, include the cost of the meal in every ticket. The current cost of an Indigo meal is Rs. 240 (170 for sandwich, 70 for juice). With economies of scale (everyone ordering a meal) I’m sure this can be brought down to about Rs. 200. When I’m paying Rs. 5000 for a flight, I wouldn’t mind the extra Rs. 200. I may not eat (note that half the time I fly Jet I don’t eat), but the point here is that given the brand Indigo has built I may not change my decision on flying Indigo because it costs Rs. 200 more.

The second idea is to drastically reduce the choice. Yes,  I know that might end up pissing off some customers who have their own favourites from the Indigo menu (mine is spinach-corn-cheese sandwich) but it makes the logistics much easier to handle. Imagine having just two choices of sandwich and two choices of juice (and no more, maybe less) and you think of how much quicker the service will get then. Going even more drastic is also an option (this is something Jetlite used to do in 2008, and I’ve noticed the same with Turkish Airline’s low-cost brand Anadolujet). Give absolutely no choice and just deposit one sandwich and one can of juice on every single tray-table. They could even.

The point of this post is that uncertainty hurts, and sometimes even those that it is intended to benefit. The choice in the Indigo menu is meant to be a boon for the passengers, but it has significant costs attached – in terms of availability and timeliness.

PS: There are no good food stalls in the airport terminal (Mumbai 1B) also that one can peacefully carry on to flights. Last two times I carried muffins from Cafe Coffee Day and Cafeccino respectively and both were downright horrible. I miss Delhi’s terminal 1D and the double chocolate chip muffin at the Costa there.

Festivals and memes

We don’t normally celebrate festivals. We don’t particularly enjoy them. The only festival we celebrate to some degree is Dasara, when we set up dolls and invite people home to view the dolls. Of course, the last couple of years it’s been similar arrangements and there hasn’t been much innovation in what we do, but we enjoy it as a process and hence take forward the festival. Last year, we even got some fireworks during Deepavali and burst them. Again – it was a fun element. We aren’t too enthused by rituals and since most other festivals are little more than rituals we don’t celebrate them.

The wife, however, sometimes have existential doubts. “There must be a reason that our ancestors celebrated these festivals”, she pops up from time to time, “so it may not be correct on our part to simply stop celebrating. We should take forward the tradition”. This is question that comes up each time we don’t celebrate a festival (which you might guess is fairly often). Before today I hadn’t been able to give a convincing reply either way – whether it makes sense to follow our instinct or if it’s a cultural duty to take forward the tradition.

Towards the end of his classic book The Selfish Gene, Richard Dawkins introduces the concept of the meme.  In fact it was Dawkins who “invented” the concept of the meme. It is meant to be a cultural analogy to the gene, and it’s a “cultural’ concept that propagates like biological concepts are taken forward through the generations via genes. Given the multitude of so-called memes that keep popping up every other day, I’m sure all of you know what meme means. I’m just providing the context here since my argument depends on the original Dawkinsian definition of the meme.

Let us say that there is a genetic attribute I inherited from my father, let’s say it’s my height (my father was 5 feet 10 inches, and I’m an inch taller than that). Now, it is not necessary that this particular gene is passed on to my progeny. It is not even necessary that the corresponding gene from my wife gets passed on – there might be a mutation there and despite the wife and I being fairly tall (by Indian standards) we cannot rule out producing a short child. The point I’m trying to make is that while genes propagate, not every trait needs to pass on from you to your offspring. Only a few traits (chosen more or less at random when your and your gene-propagating partner’s genes undergo meiosis) get passed on. Yet, through the network of you and your siblings and cousins and extended family, the family’s genetic code gets passed on.

Now, festivals and other cultural practices can be described as memes. We in the Indian society have a set of memes, which are called “Ganesh Chaturthi”, “Deepavali”, etc. That these memes have survived through the generations shows their strength – who knows about festivals that had been invented but didn’t survive. Now, the fact that we have inherited this meme doesn’t necessarily mean that we need to propagate it. Unlike genetics, the choice here is not random combination – it is our personal choice (we can’t decide what genes our offspring inherits from either of us or through a mutation).

So, just like every genetic trait doesn’t need to be propagated from a parent to an offspring, not every cultural trait needs to be passed on. If I were to pass on every cultural trait I inherit irrespective of whether it is desirable, even when circumstances change, undesirable cultural traits continue to exist. This is not efficient. As a society, we have bandwidth only for a certain number of cultural traits, and if traits are passed on without much thought, the bad ones won’t die. And will crowd out the good ones.

So if you were to look at it in terms of responsibility to society, you need to propagate only those cultural traits that you deem to be relevant and important. “So what if everyone stops celebrating Ganesh Chaturthi?” you may ask. If that would happen that would simply mean a vote of no confidence for the festival and an indication that the festival needs to be phased out. If everyone were to propagate only those cultural traits they find useful, traits that a significant proportion of society finds significant will continue to survive and thrive. For Ganesh Chaturthi to exist 30 years hence, it isn’t necessary for ALL families that have inherited it to celebrate it now. As long as a critical mass of families celebrate it, the festival will survive. If not, it probably doesn’t need to exist.

(the choice of Ganesh Chaturthi for illustration is purely driven by the fact that the festival is today).

The Crow’s Designs

As I had mentioned in my blog post yesterday, I just finished reading Sanjeev Sanyal’s Land of seven rivers yesterday afternoon. And later in the evening I started reading Nassim Nicholas Taleb’s Anti-fragile. And before you wonder, let me tell you that yesterday was a working day for me. Just that I had a long process running which gave me the flexibility to catch up on my reading.

So one topic that was mentioned both towards the end of Sanyal’s book and in the prologue of Taleb’s book was the issue of urban planning. And interestingly, the two agreed. In the prologue of Anti-fragile, Taleb has listed out a series of “fragile”, “robust” and “anti-fragile” systems. He has classified it by subject, and in each subject he gives us examples of the three systems. Being halfway through the first chapter, I understand that he is going to elaborate on each member of the list later on in his book, but I’m yet to reach the chapter (I’m still in chapter one, I told you) where he talks about urban planning. Yet, what he has written in that table in the preface on this chapter caught my eye. More so, given that it agreed with what Sanyal had written in his book. In the row on “urbanism”, Taleb has simply written “Le Corbusier” in the Fragile column and “Jane Jacobs” in the Anti-fragile column (the preface of the book is available on Taleb’s website. The relevant section of the table is on page 27).

In the last chapter of Land of seven rivers Sanyal talks about post-independence events that has affected the geography of India. One topic that he delves into is urban planning, where he contrasts the sterility of Le Corbusier’s Chandigarh with the dynamism of unplanned Gurgaon. He mentions that despite careful planning, little economic value has been created in the city of Chandigarh itself, and one reason why it is supposedly clean is because there exist no space for the poor within the city! The city’s rigid master plan is actually a hindrance to economic activity as it allows for little space for entrepreneurial activity to take place. So whatever growth and innovation Chandigarh has seen, says Sanyal, has actually happened in its suburb of Mohali, which is in the state of Punjab.

Urban planning is a topic that I’ve been thinking about quite a bit in recent times, as I’m trying to figure out where to buy a house and “settle down”. Having examined several of Bangalore’s neighbourhoods, I’ve found a strong contrast between planned and unplanned neighbourhoods. The former (eg. Jayanagar) usually have wide roads, pavements, access to markets at frequent intervals (one thing where planning has failed, and for the good I think, is zoning. I wouldn’t want to walk to the main market for every one of my needs) and auto rickshaws. More importantly, they have people walking around on the streets all the time, which makes the neighbourhood safe. Unplanned neighbourhoods (eg. Sarjapur Road) usually have large condominiums, few shopping options and no auto rickshaws. You have either highways or small village roads and not too many people walk around. This makes the streets unsafe and makes you reliant on private transport, which in my opinion is not a good thing. Nevertheless, one must admit that given the massive influx into Bangalore in the last 10-12 years (on account of the IT boom), it is the unplanned neighbourhoods that have taken the lion’s share of housing the incoming population.

So the question is how much planning a city needs. Too much planning (as in Chandigarh and Delhi) can make the cities static, and not provide enough for potential immigration – which is necessary for increased economic activity. On the other hand, unplanned areas are inherently unsafe and don’t provide for a great urban quality of life (as far as I’m concerned one of the primary indicators of urbanism is public transport). Is there a middle ground of “light touch regulation” which derives the best of both worlds? How should urban planners approach this issue? How can we make our cities both dynamic and safe? As of now, I don’t have the answers.

PS: The title of this post is in reference to the name “Le Corbusier” which is French for “The Crow”.

Time for bragging

So the Karnataka polls are done and dusted. The Congress will form the next government here and hopefully they won’t mess up. This post, however, is not about that. This is to stake claim on some personal bragging rights.

1. Back in March, after the results of the Urban Local Body polls came out, I had predicted a victory for the Congress in the assembly elections.

2. Then, a couple of weeks back, I used the logic that people like to vote for the winner, and this winner-chasing will result in a self-fulfilling prophecy that will lead to a comfortable Congress victory.

These two predictions were on the “Resident Quant” blog that I run for the Takshashila Institution. It was a classic prediction strategy – put out your predictions in a slightly obscure place, so that you can quickly bury it in case it doesn’t turn out to be right, but showcase it in case you are indeed correct! After that, however, things went slightly wrong (or right?). Looking at my election coverage Mint asked me to start writing for them.

As it happened I didn’t venture to make further predictions till the elections, apart from building a DIY model where people could input swings in favour of or against parties, and get a seat projection. Watching the exit polls on Sunday, however, compelled me to plug in the exit poll numbers into my DIY model, and come up with my own prediction. I quickly wrote up a short piece.

3. As it happened, Mint decided to publish my predictions on its front page, and now I had nowhere to hide. I had taken a more extreme position compared to most other pollsters. While they had taken care to include some numbers that didn’t mean an absolute majority in the range the predicted for the Congress (so as to shield themselves in that eventuality), I found my model compelling enough to predict an outright victory for the Congress. “A comfortable majority of at least 125 seats”, I wrote.

I had a fairly stressful day today, as the counting took place. Initial times were good, as the early leads went according to my predictions. Even when the BJP had more leads than the Congress, I knew those were in seats that I had anyway tipped them to win, so I felt smug. Things started going bad, however, when the wins of the independents started coming out. The model I had used was unable to take care of them, so I had completely left them out of my analysis. And now I was staring at the possibility that the Congress may not even hit the magic figure of 113 (for an absolute majority), let alone reach my prediction of 125. I prepared myself to eat the humble pie.

Things started turning then, however. It turned out that counting had begun late in the hyderabad karnataka seats – a region that the Congress virtually swept. As I left my seat to get myself some lunch, the Congress number tipped past 113. And soon it was at 119. And then five minutes again back at 113. And so it continued to see-saw for a while, as I sat at the edge of my office chair which I had transplanted to in front of my television.

And then it ticked up again, and stayed at 119 for a while. And soon it was ticking past 120. All results have now been declared, with the Congress clocking up 121 seats. It falls short of the majority I had predicted, but it is a comfortable majority nevertheless. I know I got the BJP number horribly wrong, but so did most other pollsters, for nobody expected them to get only 20% of the popular vote. I also admit to have missed the surge in Independents and “Others”.

Nevertheless, I think I’ve consistently got the results of the elections broadly right, and so I can stake claim to some bragging rights. Do you think I’m being unreasonable?