Decision making and explainability

This is NOT a post about AI. It is, instead, about real intelligence.

My hypothesis is – the more you need to explain your decisions to people, the worse your decision-making gets.

Basically, instinct gets thrown out of the window.

Most of you who have worked in a company would have seen a few attempts at least of the company trying to be “more data driven”. Instead of making decisions on executives’ whims and will, they decide to set up a process with objective criteria. The decision is evaluated on each of these criteria and weights drawn up (if the weights are not known and you have a large number of known past decisions, this is just logistic regression). And then a sumproduct is computed, based on which the decision is made.

Now, I might be biased by the samples of this I’ve seen in real life (both in companies I’ve worked for and where I’ve been a consultant), but this kind of decision making usually results in the most atrocious decisions. And it is not even a problem with the criteria that are chosen or the weights each is assigned (so optimising this will get you nowhere). The problem is with the process.

As much as we would like to believe that the world is objective (and we are objective), we as humans are inherently instinctive and intuitive individuals (noticed that anupraas alankaar?). If we weren’t we wouldn’t have evolved as much as we have, since a very large part of the decisions we need to make need to be made quickly (running from a lion when you see one, for example, or braking when the car in front of you also brakes suddenly).

Quick decisions can never be made based on first principles – to be good at that, you need to have internalised the domain and the heuristics sufficiently, so that you know what to do.

I have this theory on why I didn’t do well in traditional strategy consulting (it was the first career I explored, and I left my job in three months) – it demanded way too much structure, and I had faked my way in. For all the interview cases, I would intuitively come up with a solution and then retrofit a “framework”. N-1 of the companies I applied to had possibly seen through this. One didn’t and took me in, and I left very soon.

What I’m trying to say is – when you try to explain your decisions, you are trying to be analytical about something you have instinctively come to the conclusion about, and with the analysis being “a way to convince the other person that I didn’t use my intuition”.

So when a bunch of people come up with their own retrofits on how they make the decision, the “process” that you come up with is basically a bunch of junk. And when you try to follow the process the next time, you end up with a random result.

The other issue with explaining decisions is that you try to come up with explanations that sound plausible and inoffensive. For example, you might interview someone (in person) and decide you don’t want to work with them because they have bad breath (perfectly valid, in my opinion, if you need to work closely with them – no pun intended). However, if you have to document your reason for rejection, this sounds too rude. So you say something rubbish like “he is overqualified for the role”.

At other times, you clearly don’t like the person you have spoken to but are unable to put your rejection reason in a polite manner, so you just reverse your decision and fail to reject the person. If everyone else also thinks the same as you (didn’t like but couldn’t find a polite enough reason to give, so failed to reject), through the “Monte Carlo process”, this person you clearly didn’t like ends up getting hired.

Yet another time, you might decide to write an algorithm for your decision (ok I promised to not talk about AI here, but anyways). You look at all the past decisions everyone has made in this context (and the reasons for those), and based on that, you build an algorithm. But then, if all these decisions have been made intuitively and the people’s documented decisions only retrofits, you are basing your algorithm on rubbish data. And you will end up with a rubbish algorithm (or a “data driven process”).

Actually – this even applies to artificial intelligence, but that is for another day.

 

Darwin Nunez and missed chances

There is one “fact” I’m rather proud of – it is highly likely (there is absolutely no way to verify) that in CAT 2003-4 (scheduled for 2003; then paper got leaked and it was held in Feb 2004), among all those who actually joined IIMs that year, I had the most number of wrong answers.

By my calculations after the exam (yeah I remember these things) I had got 20 answers wrong (in a 150 question paper). Most of my friends had their wrong counts in the single digits. That I did rather well in the exam despite getting so many answers wrong was down to one thing – I got a very large number of answers right.

Most readers of this blog will know that I can be a bit narcissist. So when I see or read something, I immediately correlate it to my own life. Recently I was watching this video on striker Darwin Nunez, and his struggles to settle into the English Premier League.

“Nobody has missed more clear chances this season than Darwin Nunez”, begins JJ Bull in this otherwise nice analysis. Somewhere in the middle of this video, he slips in that Nunez has missed so many chances because he has created so many more of them in the first place – by being in the right place at the right time.

Long ago when I used to be a regular quizzer (nowadays I’m rather irregular), in finals I wouldn’t get stressed if our team missed a lot of questions (either with other teams answering before us, or getting something narrowly wrong). That we came so close to getting the points, I would reason, meant that we had our processes right in the first place, and sooner or later we would start getting those points.

In general I like Nunez. Maybe because he’s rather unpredictable (“Chaos” as JJ Bull calls him in the above video), I identify with him more than some of the more predictable characters in the team (it’s another matter that this whole season has been a disaster for Liverpool -I knew it on the opening day when Virgil Van Dijk gave away a clumsy penalty to Fulham). He is clumsy, misses seemingly easy chances, but creates some impossible stuff out of nothing (in that sense, he is very similar to Mo Salah, so I don’t know how they together work out as a portfolio for Liverpool. That said, I love watching them play together).

In the world of finance, losing money is seen as a positive bullet point. If you have lost more money, it is a bigger status symbol. In most cases, that you lost so much money means that your bank had trusted you with that much money in the first place, and so there must be something right about you.

You see this in the startup world. Someone’s startup folds. Some get acquihired. And then a few months later, you find that they are back in the market and investors are showering them with funds. One thing is that investors trust that other investors had trusted these founders with much more money in the past. The other, of course, is the hope that this time they would have learnt from the mistakes.

Fundamentally, though, the connecting thread running across all this is about how to evaluate risk, and luck. Conditional on your bank trusting you with a large trading account, one bad trading loss is more likely to be bad luck than your incompetence. And so other banks quickly hire you and trust you with their money.

That you have missed 15 big chances in half a season means that you have managed to create so many more chances (as part of a struggling team). And that actually makes you a good footballer (though vanilla pundits don’t see it that way).

So trust the process. And keep at it. As long as you are in the right place at the right time a lot of times, you will cash on average.

Blackjack and ADHD

My mornings feel like I’m playing blackjack. A few months back, I had a bit of a health scare (elevated blood sugar levels), and since finding good low-carb food in/around office is a challenge, after that I’ve been taking my own lunch box.

It’s a fairly elaborate lunch, which one colleague calls as “looking rather European”. It started with grilled paneer and grilled vegetables, but has now grown to a massive glass Ikea box with grilled paneer, boiled eggs, grilled vegetables (some pre-blanched / steamed before grilling), roasted and crushed nuts and (of late) kimchi.

And despite my cook occasionally helping me out with some mise-en-place, there are a lot of things to do every morning. Some of the processes involved are:

  • keeping water for boiling, for eggs
  • putting eggs carefully into the boiling water (without breaking), and setting a timer for 7 minutes. If I’m not wearing my Apple Watch, I need to also run around to find my phone
  • Putting water in the steamer for steaming vegetables
  • Putting the hard veggies (carrot, beans, broccoli) into the steamer and closing the pot.
  • Taking out the veggies from the steamer before they are too soggy
  • Slicing paneer
  • Grilling paneer on the frying pan with salt and pepper and olive oil
  • Grilling veggies on the frying pan with salt and pepper and olive oil (including the steamed veggies)
  • Pre-heating the air fryer
  • Adding almonds into the air fryer; shaking the fryer once in the middle, transferring almonds to the pestle and mortar
  • Putting cashews into the air fryer
  • Taking out cashews when they have just browned and putting into the pestle and mortar
  • Putting eggs in cold water after seven minutes are up
  • Peeling and slicing eggs, and seasoning with salt and pepper
  • Crushing cashews and almonds and adding them to grilled vegetables

I don’t think I’ve ever timed myself. However, pretty much every morning I get into a frenzy trying to finish all of this, and then take my daughter to school on time. Maybe some days I take twenty minutes. Maybe I take thirty. I don’t even know. Life is such a blur.

As you can imagine, the above process can be heavily parallelised. And while my menu is standardised, the process is not. Which means I’m trying to both experiment and measure at the same time. While cooking four different processes at exactly the same time.

Sometimes, life feels like playing blackjack. You would have flipped the paneer over in the frying pan maybe for one last time. And then you think “I can peel this egg before the paneer is done”. Before you know it the paneer is black. You are not wearing your watch, so you go in search of the phone – to put the timer for the egg. In that time the veggies are burnt.

I don’t even know why I sometimes put myself through this. Maybe this is yet another tradeoff between physical and mental health. For now, physical seems to be winning.

Maybe a sustainable long term strategy is to forego lunch as well (nowadays I don’t eat breakfast unless I’ve gone to the gym in the morning), and transition to an “OMAD” (one meal a day) lifestyle.  Or maybe I should find myself some nice lunch I can take to office which doesn’t involve so many parallel steps.

Until I figure something out, I’ll continue running in the mornings.

Why I never became a pundit

It’s been nearly a decade since i started writing in the mainstream media. Ahead of the Karnataka elections in 2013, I had published on this blog a series of quantitative analyses of the election, when R Sukumar (then editor-in-chief of Mint) picked it up and asked me if I could write for his paper on the topic – quantitative analysis of elections.

And so Election Metrics (what my pieces in Mint – they were analysis and not editorials, which meant it wasn’t a strict “column” per se, but I got paid well) was born. I wrote for Mint until the end of 2018, when my then contract ran out and Sukumar’s successor chose not to renew.

Having thus “cracked print”, I decided that the next frontier had to be video. I wanted to be on TV, as a pundit. That didn’t come easily. The 2014 national elections (when Modi first became PM) came and went, and I spent the counting day in the Mint newsroom, far from any television camera. I tried to get my way in to IPL auction analysis, but to no avail.

Finally, in 2018, on the day of the Karnataka elections, I got one guy I knew from way back to arrange for a TV appearance, and went on “News9” (a Bangalore-focussed English news channel) to talk about exit polls.

“I saw the video you had put on Facebook”, my friend Ranga said when he met me a few days later, “and you were waxing all eloquent about sample sizes and standard errors”. On that day I had been given space to make my arguments clear, and I had unleashed the sort of stuff you don’t normally see on news TV. Three days later, I got invited on the day of counting, enjoyed myself far less, and that, so far, has been the end of my career in punditry.

Barring a stray invitation from The Republic aside, my career in TV punditry has never gotten close to getting started after that. Of late I haven’t bothered, but in the past it has frequently rankled, that I’ve never been able to “crack TV”. And today I figured out why.

On my way to work this morning I was listening to this podcast featuring noted quant / factor investors Jim O’Shaughnessy and Cliff Asness. It was this nice episode where they spoke about pretty much everything – from FTX and AMC to psychedelics. But as you might expect with two quant investors in a room, they spent a lot of time talking about quantitative investing.

And then somewhere they started  talking about their respective TV appearances. O’Shaughnessy started talking about how in the early days of his fund, he used to make a lot of appearances on Bloomberg and CNBC, but of late he has pretty much stopped going.

He said something to the effect of: “I am a quant. I cannot give soundbites. I talk in terms of stories and theories. In the 80s, the channels used to give me a minute or two to speak – that was the agreement under which I appeared on them. But on my last appearance, I barely got 10 seconds to speak. They wanted soundbites, but as a quant I cannot give soundbites”.

And then Asness agreed, saying pretty much the same thing. That it was okay to go on television in the time when you got a reasonable amount of time to speak, and build a theory, and explain stuff, but now that television has come down to soundbites and oneliners, he is especially unsuited to it. And so he has stopped going.

There it was – if you are the sort who is driven by theories, and you need space to explain, doing so over voice is not efficient. You would rather write, where there is room for constructing an argument and making your point. If you were to speak, unless you had a lot of time (remember that speaking involves a fair amount of redundancy, unlike writing), it would be impossible to talk theories and arguments.

And I realise I have internalised this in life as well – at work for example, I write long emails (in a previous job, colleagues used to call them “blogposts”) and documents. I try to avoid complicated voice discussions – for with my laborious style I can never win them. Better to just write a note after it is over.

Stable Diffusion and Chat GPT and Logistic Regression

For a long time I have had this shibboleth on whether someone is a “statistics person or a machine learning person”. It is based on what they call regressions where the dependent variable is binary. Statisticians simply call it “logit” (there is also a “probit“).

Now, in terms of implementation as well, there is one big difference between the way “logit” is modelled versus “logistic regression”. For a logit model (if you are using python, you need to use the “statsmodels” package for this, not scikit learn), the number of observations needs to far exceed the number of independent variables.

Else, a matrix that needs to be inverted as part of the solution will turn out to be singular, and there will be no solution. I guess I betrayed my greater background in statistics than in Machine Learning when, in 2018, I wrote this blogpost on machine learning being a “process to tie down coefficients in maths models“.

For “logistic regression” (as opposed to “logit”) puts no such constraint – on the regression matrix being invertible. Instead of actually inverting the matrix, machine learning approaches simply focus on learning the terms of the inverted matrix using gradient descent (basically the opposite of hill climbing), so mathematical inconveniences such as matrices that cannot be inverted are moot there.

And so you have logistic regression models with thousands of variables, often calibrated with a fewer number of data points. To be honest, I can’t understand this fully – without sufficient information (data points) to calibrate the coefficients, there will always be a sense of randomness in the output. The model has too many degrees of freedom, and so there is additional information the model is supplying (apart from what was supplied in the training data!).

Of late I have been playing a fair bit with generative AI (primarily ChatGPT and Stable Diffusion). The other day, my daughter and I were alone in my in-laws’ house, and I told her “look I’ve brought my personal laptop along, if you want we can play with it”. And she had demanded that she “play with stable diffusion”. This is the image she got for “tiger chasing deer”.

I have written earlier here about how the likes of ChatGPT and Stable Diffusion in a way redefine “information content“.

 

And if you think about it, almost by definition, “generative AI” creates information (and hallucinates, like in the above pic). Traditionally speaking, a “picture is worth a thousand words”, but if you can generate a picture with just a few words of prompt, the information content in it is far less than a thousand words.

In some sense, this reminds me of “logistic regression” once again. By definition (because it is generative), there is insufficient “tying down of coefficients”, because of which the AI inevitably ends up “adding value of its own”, which by definition is random.

So, you will end up getting arbitrary results. ChatGPT often gives you wrong answers to questions. Dall-E and Midjourney and Stable Diffusion will return nonsense images such as the above. Because a “generative AI” needs to create information, by definition, all the coefficients of the model cannot be well calibrated. 

And the consequence of this is that however good these AIs get, however much data is used to train them, there will always be an element of randomness to them. There will always be test cases where they give funny results.

No, AGI is not here yet.

Computer science and psychology

This morning, when I got back from the gym, my wife and daughter were playing 20 questions, with my wife having just taught my daughter the game.

Given that this was the first time they were playing, they started with guessing “2 digit numbers”. And when I came in, they were asking questions such as “is this number divisible by 6” etc.

To me this was obviously inefficient. “Binary search is O(log n)“, I realised in my head, and decided this is a good time to teach my daughter binary search.

So for the next game, I volunteered to guess, and started with “is the number \ge 55“? And went on to “is the number \ge 77“, and got to the number in my wife’s mind (74) in exactly  7 guesses (and you might guess that \lceil log_2 90 \rceil (90 is the number of 2 digit numbers) is 7).

And so we moved on. Next, I “kept” 41, and my wife went through a rather random series of guesses (including “is it divisible by 4” fairly early on) to get in 8 tries. By this time I had been feeling massively proud, of putting to good use my computer science knowledge in real life.

“See, you keep saying that I’m not a good engineer. See how I’m using skills that I learnt in my engineering to do well in this game”, I exclaimed. My wife didn’t react.

It was finally my daughter’s turn to keep a number in mind, and my turn to guess.

“Is the number \ge 55?”
“Yes”

“Is the number \ge 77?”
“Yes”

“Is the number \ge 88?”
“Yes”

My wife started grinning. I ignored it and continued with my “process”, and I got to the right answer (99) in 6 tries. “You are stupid and know nothing”, said my wife. “As soon as she said it’s greater than 88, I knew it is 99. You might be good at computer science but I’m good at psychology”.

She had a point. And then I started thinking – basically the binary search method works under the assumption that the numbers are all uniformly distributed. Clearly, my wife had some superior information to me, which made 99 far more probable than any number between 89 and 98. And s0 when the answer to “Is the number \ge 88?”turned out to by “yes”, she made an educated guess that it’s 99.

And since I’m used to writing algorithms, and  teaching dumb computers to solve problems, I used a process that didn’t make use of any educated guesses! And thus took far many more steps to get to the answer.

When the numbers don’t follow a uniform distribution, binary search works differently. You don’t start with the middle number – instead, you start with the weighted median of all the numbers! And then go on to the weighted median of whichever half you end up in. And so on and so forth until you find the number in the counterparty’s mind. That is the most optimal algo.

Then again, how do you figure out what the prior distribution of numbers is? For that, I guess knowing some psychology helps.

 

The Twelfth Camel

In a way, this post should write itself. For those of you with context, the title should be self explanatory. And you need not read further.

For the rest I’ll write a rather small essay.

The story is of the old Arab who died leaving his eldest son half his wealth, the second a fourth of his wealth and the youngest one sixth. The wealth in question turned out to be 11 camels.

With 11 being a prime number, how could this will be executed without any of the camels being executed? An ingenious neighbour came in and lent his camel. Now there were twelve. The three sons respectively received 6 (\frac{12}{2}), , 3 (\frac{12}{4}) and 2 (\frac{12}{6}) camels respectively. One camel was left over – the neighbour’s, who took it back.

This is mathematically inaccurate, since the sons received fractions of their father’s wealth slightly different from what he had intended. However, in general in life, this parable of the twelfth camel offers a useful metaphor.

In engineering, this is rather common – you have systems such as a choke, for example, to enable systems to get started from a “cold start process”. The choke comes in only at the time of startup – once the thing has started, it plays no role.

However, it has its role in normal life and business as well. For example, after a bad breakup, you might rebound to a “stop gap partner”. You know that this is not going to be a long term relationship, but this partner helps you tide over the shock of the bad breakup, and by the time this relationship (inevitably) breaks up, it has achieved its purpose of getting you back on track. And you get on with life, finding more long term partners.

Then, when the company is in deep trouble, you have specialists who come in to take over with the explicit goal of cleaning things up and getting the company ready for new ownership. For exanple, John Ray III has recently taken over as CEO of FTX. His previous notable appointment was as CEO of Enron, soon after that scandal had broken. He will not stay for a long term – he will just clean things up and move on.

And sometimes the role of the twelfth camel is rather more specific. Apart from “generic cleaning”, the temporary presence of the twelfth camel can be used to get rid of people who had earlier been hard to get rid of.

In sum, the key thing about the twelfth camel theory is that the neighbour knew all along that he was going to get back his camel. In other words, it is a deliberate temporary measure intended to achieve a certain set of specific outcomes. And the camel itself may not know that it is being “lent”!

Size, diversity and social capital

Starting off with a “global” statement, life is full of tradeoffs.

When we listen to stories as kids, we think of “battles between good and evil”. When we watch sport, there is “our side and their side”. From stories, we are usually conditioned to “battles” where one side is superior to the other, and there is a clear “favourite” to root for.

Real life is not so simple. A lot of times, you have battles between two sides that are both bad (a lot of elections, for example). And frequently you come across situations where there are two “good” things of which you can only have one (and so the tradeoffs). One of the more famous of these is the “impossible trinity” of international economics.

In a completely different context (which I’m writing about today), two desirable things that are a tradeoff are diversity and social capital. The general theory is that the more diverse a society gets, the lower is the social capital (google is impossible in providing good links on this, so maybe ask ChatGPT).

The theory here goes that you fundamentally trust people like you and mistrust people who are not like you. The more homogeneous a society is, the more there are “people like you”, and so the more you trust. And if everyone trusts one another much more, there is more social capital.

A recent conversation and observations makes me wonder if social capital is also related to size. More specifically I’m thinking of size as in number of people in an institution.

One observation we had when we went for our reunion last week is that the campus is a lot quieter than in our time, and that a lot more rules are followed in letter rather than just in spirit (no, not that kind, but I’m talking about rules about that also).

For example, the basketball hoop in L^2 has been removed. While we pitched up a net and played tsepak in BEFG Square (where we always played it), current students informed us that they can’t play there because playing there is now banned. Students mostly hung out in their wings rather than in the common areas. After our first evening there we assumed all the students were out on holiday, while it turned out only the first years had a term break then.

I still remember my first night in IIMB (in 2004). I stayed in G Block, on one side of the aforementioned BEFG square. A bunch of people were playing Tsepak until late in the night, which meant I didn’t get good sleep (and for the rest of the week we had our “orientation” which meant I couldn’t sleep well then either). A few days later I just joined these people in playing Tsepak and making noise. “If you can’t stop them, join them”, was a perfect way to go about things back in the day.

What I remember is that, with a batch size of ~200, our social capital was pretty good. While there were some students who occasionally displayed elevated levels of conscience, we largely stuck by one another and tolerated one another (and, of course, massively trolled one another). Disagreements and fights always happened, but were largely resolved among us by dialogue, rather than inviting external parties.

With a much bigger batch size now, though, from what we were told, it appears it is not so simple to resolve things using dialogue and mediation. And people frequently take to inviting external parties. And the expected result (crane-mongoose effect) happens.

That some people want to study when others want to play now means that the former complain against the latter, with playing within the hostel being banned. Some people want to enforce the rules on spirits, and they bring in external parties, and the law is invoked in letter rather than spirit.

When social capital dwindles, in some ways, the minority rule comes into play – when there is a small but vocal minority that wants things a certain way, that becomes the way for everyone else as well. (With high social capital, the majority might be able to convince the minority that they need to be more tolerant)

Yet again, this is not a one way street. You can also argue that when social capital is too high, the minorities can tend to get oppressed (since their views don’t count any more), and so a high social capital society cannot be inclusive.

And so yeah, the Baazigar principle is there everywhere. To get something, you need to give up something. To get a more inclusive class, you need to be less majoritarian, and that means less fun on the average. When you have lots of intolerant minorities (a consequence of diversity), those “intolerant rules” get applied on everyone, and the overall payoff reduces.

A few random thoughts to end:

  1. It’s not just the class size, it’s also the fees. We paid ~ ?300,000 over 2 years as tuition fees. Many of us (who sat for campus placements) made almost twice that (post taxes) in our first year of graduation.

    Students nowadays pay ~ ?2,500,000 , so they are a lot more conscious about getting their money’s worth. And being able to study.

  2. In general, cultures change over time, so coming back after 15 years and complaining that “things aren’t the way they used to be” isn’t very nice. So yeah, this blogpost can get classified in the “not so nice” category I guess (not like I’ve ever been known for niceness)
  3. I wonder how much changed during the pandemic, when students were off campus for nearly a year, and had severely curtailed interactions even once they were back. With a 2 year course, it only takes 1 batch to “break culture”, making the culture far more malleable. So again I’m wrong to complain.
  4. All that said, it’s my duty to pontificate and so I’ll continue to write like this

Girard and reunions

Thanks to my subscription to Jim O’Shaughnessy’s Infinite Loops podcast, I have been exposed to some of the philosophy of Rene Girard. A few times, he has got philosopher Johnathan Bi on the show, to talk about Girard’s philosophy.

 

Bi has also done a series of YouTube lectures on Girard’s philosophy, though I haven’t watched any of them.

In any case, Girard’s basic thesis (based on my basic understanding so far) is that we are all driven by “mimetic desire”, or a desire to mime. This means we want to do things that others want to do.

So you see an instagram post by a friend who has gone to Sri Lanka, and you want to go to Sri Lanka as well. Your cousin has invested in Crypto, so you want to invest in crypto as well. Everyone in your class wants to do investment banking, and so you want to do that as well.

(actually now that I think of it, I was first exposed to mimetic desire by a podcast episode sent by my school friend Hareesh. In a way, Bi’s appearance on Infinite Loops only enhanced my liking for this philosophy).

 

This is yet another of those theories that “once you see you cannot unsee”. You see mimetic desire everywhere. Sometimes you copy the actions of people who you want to impress (well, that’s how I discovered Heavy Metal, and that has now turned out to be my most-listened-to genre of music, because it turned out I like it so much).

The theory of the “mirror neuron” is unclear (at least I’m yet to be convinced by it), but either by gene or by meme, we are conditioned to mime. We mime people’s actions. We mime their desires. We do things because others do them.

As the more perceptive of you might know from my previous post, we had our 16th year IIMB reunion this year. Not many turned up – about 30 from my class (2006) and 45 from the class of 2005 (thanks to covid both our 15th year reunions had been postponed, so we ended up having our 16th and 17th year reunions respectively).

It was an amazing experience. I don’t know what it was, but I liked it far more than the 10th year reunion.  One major thing was the schedule – the 10th year reunion lacked a focal point on the main day (I’ve written about it) because of which we were rather scattered around campus. The 10th year reunion also had a much more formal structure, with “sessions” which meant we had less time to chat.

This time round, the Saturday schedule was very good – an interaction with the current director RTK from 10 to 11, and then NOTHING. That interaction was enough of a focal point to get us in one place, so everyone was accessible.

Then, fewer people having turned up meant we ended up having deeper conversations. We spoke about life, philosophies, kids, spouses, divorces, other people’s divorces, random gossip and all such. Absolutely no small talk, and infinitesimal work talk, and that made it more satisfying.

This morning, Bi tweeted again about Girard and mimetic desire.

One of the corollaries of Girard’s theory is that people get into conflict not when they are different but when they are similar. And mimetic desire means that people will try to become more similar to each other, and that increases conflict.

If not anywhere else, that is true in a business school, especially one where the class is rather homogeneous. Mimetic desire means everyone wants the same jobs, the same grades. And so they compete. And get into conflict.

16 years post graduation, we have drifted apart, and not in a bad way. Over this period, a lot of us have figured out what we really want to do and what we really want, and understood that what we want is very different from what others around us want. Not really being around our former peers, we have no desire to mime them any more, and that has freed us up to do what we really want to do, rather than just signalling.

And so, when we meet at a reunion like this, we are all so independent that we just never talk about work. There is no sense of competition, and we just focus on having fun with people we went to school with. The time apart has helped us get out of our desires to mime, and so when we get together, we compete less.

Maybe I should read / understand more philosophy. Or is this desire just mimetic?

 

 

Hanging out with the lads

One of my favourite podcasts this year has been The Rest is History with Tom Holland and Dominic Sandbrook. It is simultaneously insanely informative and insanely funny, and I’ve been listening to it as regularly as I can this year.

A few months back, a prequel to The Lord Of The Rings called “Rings of Power” came out on Amazon. To commemorate that, Rest is History did a few episodes on JRR Tolkien. It’s a fascinating profile, but one line especially stood out.

Holland was talking about how Tolkien found himself a steady girlfriend when he was 13 (and got himself excommunicated from the church in the process – he was Catholic and she was Protestant, I think). And then he said “that part of his life having been settled, he now focussed on other things, such as hanging out with the lads”.

I find this to be a rather profound line. “Hanging out with the lads”. And having found myself a steady girlfriend for the first time relatively late in life (when I was nearly 27), I can look back at my life and think of the value of this phrase.

When you are single, among other things, you become a “life detector” (this phrase comes from one friend, who used it to describe another, saying “she is a life detector. She puts blade on anything that moves”). Especially if, as a youngster, you have watched good but illogical movies such as Dil To Pagal Hai.

You may not realise it until you are no longer single, but being single takes a toll on your mental health. Because you are subconsciously searching for a statistically significant other, you mind has less time and space for other things. And you miss out on more enjoyable things in life.

Such as “hanging out with the lads”.

I have written (forgot where, and too lazy to find the link now) about how being no longer single was fantastic in terms of simply appreciating other women. You could say they were nice, or beautiful, or intelligent, or whatever, and it would be a simply honest comment without any “ulterior motives”. More importantly, you could very simply tell her that, without worrying whether she will like you back, what caste she belongs to (if you were into that kind of stuff) and so on.

I listened to the podcast on Tolkien when it came out a few months ago, but got reminded of it over the weekend. I spent most of my weekend in IIMB, at our 15th year batch reunion (ok, it’s been 16 years since we graduated but our party was postponed by a year due to Covid). As part of the reunion (and unlike our 10th reunion in 2016), we had a real “L^2 party” (check here to see what L^2 parties used to be (for me) back in the day).

So effectively, this Saturday I was at my first ever L^2 party after I had graduated from IIMB. In other words, I was at my first ever L^2 party where I was NOT single (my wife wasn’t there, though. Pretty much no one from our batch brought spice or kids along).

However, despite the near 17-year gap from the last L^2 I had attended, I could feel a different feeling. I found myself far more willing to “hang out with the lads” than I had been in 2004-6. I had a lot of fairly strong conversations during the time. I held random people and danced (thankfully the music got better after a while).

Through the entire party I was at some kind of perfect peace with myself. Yeah, you might find it strange that a 40-year-old guy is writing like this, but whatever. Early on, I sent a video of the party to my wife. She sent back a video of our daughter trying to imitate the way I was “dancing”.

And it was not just the party. I spent a day and a half at IIMB, hanging out with the “lads” (which included a few women from our batch), having random conversations about random things, just laughing a lot and exchanging stories. Nobody spoke about work. There was very little small talk. Some conversations actually went deep. It was a great time.

With the full benefit of hindsight, I had as much fun as I did in this period (ok i might be drawing random connections, but what the hell)  because I was secure in the fact that I am in a steady relationship, and have a family. And it took me a long time to realise this, well after I had stopped being single.