Hosur cuisine

Some 6-7 months back my office shifted from a relatively quiet semi-residential lane in Indiranagar to the slam-bang commercial area of Residency Road. This meant that Udupi Vaibhava, situated next to our old office and had served many of us rather well, suddenly lost a bunch of business. We, however, needed something to find something.

On the first day in the new office I visited good ol’ Konark next door for “tiffin” and coffee. Food was good but transaction cost (of sitting down and waiting) was rather high. And then people in office started raving about this “IDC Kitchen” across the road, and a week later I went there for breakfast.

I asked for idli-vaDe, and the first look of the vaDe gave me the jitters – instead of one large vaDe, there were two tiny vaDes, the sort we make at death ceremonies here in Bangalore. The idli looked dense as well. “Oh gosh, this is Tamil-style food”, I thought. And then I found that the sambar was red and sweet, of the kind you normally find in Bangalore. It was a bit of a relief.

Yet, the food was confusing. Some of it was evidently Tamil style (the “pODi iDli” and stuff), but it wasn’t quite entirely Tamil style. The dosé was thin. Chutney was neither thick nor thin. Very very very confusing.

And then a few days later a friend insisted we have breakfast at “Cafe Amudham” in Siddapura, insisting the dosé there was excellent. I didn’t want to have a dosé that day, so I asked for iDli-vaDe, and once again it was insanely dense iDlis, but normal sized vaDes. The sambar was more Bangalore style as well – again massively confusing.

Based on these two data points (and that yet-to-be-sampled data point that is Rameshwaram Cafe), I hereby declare that there exists a new cuisine that I call “Hosur cuisine”. It is basically a mix of Bangalore and classic Tamil cuisines. It is like the chromosomes of the two cuisines having undergone a random crossover (and some mutations), and so different restaurants serving this cuisine have adopted different aspects of the cuisines of the two  states – the style of sambar, density of idli, thickness of dosé, size of vaDe, number of chutneys served, etc.

And recently, having got quite bored of IDC (I’ve pretty much stopped eating there now), I tried the Virinchi Cafe next door to that. They make thick dosés but have drumstick in their otherwise red sambar. Incredibly confusing, and I can say that this is yet another “strand” of the Hosur cuisine crossover.

In any case, I’ve been brewing over this blogpost for a few days now, and then I saw Sandesh’s excellent dissection of Rameshwaram Cafe, and decided it’s time to put this down.

I’m yet to visit a Rameshwaram Cafe – the only one within my orbit is in JP Nagar 2nd phase, but it’s way too close to SN Refreshments to give it a try (and I have breakfast at SN some 2-3 times a week at least!). I suppose that is yet another random crossover of the Bangalore and Tamil food styles .

PS: This blogpost has absolutely NOTHING to do with my grandmother-in-law who is from Hosur

Reading Kannada aloud

I’ve never learnt much Kannada formally. Of course, it is the first language, and the language I’ve always spoken at home. However, I’ve not learnt it much formally. While we had it in school as a “third / fourth language”, the focus there was largely functional – that we learnt the language sufficiently to get by in South Bangalore.

The little I remember from the Kannada lessons in school is that we made fun of some words. Basically, the way they were written is very different from the way we spoke them. “adarinda” became “aaddarinda” or even “aadudarinda”. “nintOgatte” became “nintu hOgatte”. Basically, Kannada as a language in which it was written was very different from the way we spoke it.

That said, during those days (early 90s), the only newspaper we got at home was in Kannada, and I learnt to read it fairly well. I still made fun of the “aadudarindas” (and my parents agreed it was weird), but I had figured out how to parse the “written Kannada” as “normal Kannada” and got the information I needed to.

In adulthood, my Kannada reading skills have atrophied, primarily because there isn’t much need to read / write Kannada (apart from the occasional addresses or sign boards). In terms of speaking, Kannada is still my first language, but when it comes to the written text (either reading or writing), English has taken its place.

Recently, my wife has gotten our daughter a few Kannada and Hindi story books, so that she can practice reading the two languages. And last night, before she went to bed, my daughter asked me to read out one of the Kannada books to her.

What I found is that Kannada is a language that is very tough to read aloud, primarily due to the large (in my mind) differences between the way it is written and spoken. I read the sentences out alright, but struggled to make meaning out of it since the words were all formally written.

Soon I gave up and resorted to what I used to do with “Kannada Prabha” or “Vijaya Karnataka” back in the 90s – I would see the words in the formal way but call them out “informally”. So I would see “aadudarinda” in the text, and just read it as “adarinda”. I would read “hOguttade” and say “hOgatte”. Wasn’t easy business, but I managed to read out the whole story.

Nevertheless, Kannada is not a language that is easy to read aloud, because the way it’s written is so different from the way it is spoken. It almost feels like the spoken language has evolved significantly over the years, but the written language hasn’t  kept up. If you have to read silently, you can just substitute the “normal words” for the “formal words” and get on. However, reading aloud, that is not a choice.

In any case, now I’m worried that with my way of reading aloud (speak the words as I would speak them, rather than the way they are written), I’m messing with my daughter’s Kannada reading skills. And having spent two of her first three years in London, Kannada is not even her first language (she basically learnt to talk in her nursery)!

Average skill and peak skill

One way to describe how complex a job is is to measure the “average level of skill” and “peak level of skill” required to do the job. The more complex the job is, the larger this difference is. And sometimes, the frequency at which the peak level of skill is required can determine the quality of people you can expect to attract to the job.

Let us start with one extreme – the classic case of someone  turning screws in a Ford factory. The design has been done so perfectly and the assembly line so optimised that the level of skill required by this worker each day is identical. All he/she (much more likely a he) has to do is to show up at the job, stand in the assembly line, and turn the specific screw in every single car (or part thereof) that passes his way.

The delta between the complexity of the average day and the “toughest day” is likely to be very low in this kind of job, given the amount of optimisation already put in place by the engineers at the factory.

Consider a maintenance engineer (let’s say at an oil pipeline) on the other hand. On most days, the complexity required of the job is very close to zero, for there is nothing much to do. The engineer just needs to show up and potter around and make a usual round of checks and all izz well.

On a day when there is an issue however, things are completely different – the engineer now needs to identify the source of the issue, figure out how to fix it and then actually put in the fix. Each of this is an insanely complex process requiring insane skill. This maintenance engineer needs to be prepared for this kind of occasional complexity, and despite the banality of most of his days on the job, maintain the requisite skill to do the job on these peak days.

In fact, if you think of it, a lot of “knowledge” jobs, which are supposed to be quite complex, actually don’t require a very high level of skill on most days. Yet, most of these jobs tend to employ people at a far higher skill level than what is required on most days, and this is because of the level of skill required on “peak days” (however you define “peak”).

The challenge in these cases, though, is to keep these high skilled people excited and motivated enough when the job on most days requires pretty low skill. Some industries, such as oil and gas, resolve this issue by paying well and giving good “benefits” – so even an engineer who might get bored by the lack of work on most days stays on to be able to contribute in times when there is a problem.

The other way to do this is in terms of the frequency of high skill days – if you can somehow engineer your organisation such that the high skilled people have a reasonable frequency of days when high skills are required, then they might find more motivation. For example, you might create an “internal consulting” team of some kind – they are tasked with performing a high skill task across different teams in the org. Each time this particular high skill task is required, the internal consulting team is called for. This way, this team can be kept motivated and (more importantly, perhaps) other teams can be staffed at a lower average skill level (since they can get help on high peak days).

I’m reminded of my first ever real taste of professional life – an internship in an investment bank in London in 2005. That was the classic “high variance in skills” job. Having been tested on fairly extreme maths and logic before I got hired, I found that most of my days were spent just keying in numbers in to an Excel sheet to call a macro someone else had written to price swaps (interest rate derivatives).

And being fairly young and immature, I decided this job is not worth it for me, and did not take up the full time offer they made me. And off I went on a rather futile “tour” to figure out what kind of job has sufficient high skill work to keep me interested. And then left it all to start my own consultancy (where others would ONLY call me when there was work of my specialty; else I could chill).

With the benefit of hindsight (and having worked in a somewhat similar job later in life), though, I had completely missed the “skill gap” (delta between peak and average skill days) in my internship, and thus not appreciated why I had been hired for it. Also, that I spent barely two months in the internship meant I didn’t have sufficient data to know the frequency of “interesting days”.

And this is why – most of your time might be spent in writing some fairly ordinary code, but you will still be required to know how to reverse a red-black tree.

Most of your time might be spent in writing SQL queries or pulling some averages, but on the odd day you might need to know that a chi square test is the best way to test your current hypothesis.

Most of your time might be spent in managing people and making sure the metrics are alright, but on the odd day you might have to redesign the process at the facility that you are in charge of.

In most complex jobs, the average day is NOT similar to the most complex day by any means. And thus the average day is NOT representative of the job. The next time someone I’m interviewing asks me what my “average day looks like”, I’ll maybe point that person to this post!

Stereotypes and correlations

Earlier on this blog, I’ve argued in favour of stereotypes. “In the absence of further information, stereotypes give you a strong Bayesian prior”, I had written (I’m paraphrasing myself here). I had gone on to say (paraphrasing myself yet again), “however, it is important that you treat this as a weak prior and update them as and when you get new information. So in the presence of additional information, you need to let go of the stereotypes”.

A lot of stereotyping is due to spurious correlations, often formed due to small number of training samples. My mother, for example, strongly believed that if you drink alcohol, you must be a bad person. Sometime, she had explained to me why she thought so – there were a few of her friends whose fathers or husbands drank alcohol, and they had had to endure domestic abuse.

That is only one extreme correlation stereotype. We keep making these stereotypes based on correlation all the time. I’m not saying that the correlation is not positive – sometimes it can be extremely positive. Just that it may not have full explainability.

For example, certain ways on dressing have come to be associated with certain attitudes (black tshirts and heavy metal, for example). So when we see someone exhibiting one side of this correlation, our minds are naturally drawn to associating them with the other side of the correlation as well (so you see someone in a black heavy metal band t-shirt, and immediately assume that they must be interested in heavy metal – to take a trivial example).

And then when their further behaviour belies the correlation that you had instinctively made, your mind gets messed up.

There was this guy in my batch at IIT Madras, who used to wear a naama (vertical religious mark on forehead commonly worn by Iyengars) on his forehead a lot of the time. Unlike most other undergrads, he also preferred to wear dhotis. So you would see him in his dhoti and naama and assume he was a religious conservative. And then you would see his hand, which would usually be held up showing a prominent middle finger, and all your mental correlations would go for a toss.

Another such example that I’ve spoken about on this blog before is that of the “puritan topper” – having seen a few topper types who otherwise led austere lives, I had assumed that kind of behaviour was correlated with being a topper (in some ways I can now argue that this blog is getting a bit meta).

I find myself doing this all the time. I observe someone’s accent and make assumptions on their abilities or the lack of it. I see someone’s dressing sense and build a whole story in my head on that single data point. I see the way someone is walking, and that supposedly tells me about their state of mind that day.

The good thing I’ve done is to internalise my last year’s blogpost – while all these single data point correlations are fine as a prior (in the absence of other information), the moment I get more information I immediately update them, and the initial stereotypes go out of the window.

The other thing I’m thinking of is – sometimes some of these random spurious correlations are so ingrained in our heads that we let them influence us. We take a certain job and decide that it is associated with a certain way of dressing and also start dressing the same way (thus playing up the stereotypes). We know the sort of clothes most people wear to a certain kind of restaurant, and also dress that way – again playing up the stereotypes.

Without realising it, maybe because of mimetic desire or a desire to fit in, we end up furthering random correlations and stereotypes. So maybe it is time to make a conscious effort to start breaking these stereotypes? But no – you won’t see me wear a suit to work any time soon.

I’ll end with another school anecdote. For whatever reason, many of the topper types in my 11th standard class would wear the school uniform sweater to school every single day, irrespective of how hot or cold it was. And then one fine (and not cold) day, yet another guy showed up in the uniform sweater. “How come you’re wearing this sweater”, I asked. He replied, “Oh, I just wanted to look more intellectual!”


Why social media went woke

When Elon Musk took over twitter recently, one of the “drain the swamp” things he did was to get rid of the platform’s overt bias towards political correctness and “wokeness”. Out went most of the “trust and safety” team. In came Donald Trump (though he hasn’t tweeted since) and the guy who stupidly got himself arrested in Romania.

As some people in my office have never tired of saying, Musk let go of 70% of the company, and the app still largely runs fine (apart from some weird bugs that creep in once in a while). One part of twitter that is NOT running fine, though, is advertising – you might be able to guess that from the quality of ads you are getting served on your timeline nowadays. There are two theories behind this – one is that Musk got rid of most of the ad sales team, and the other is that advertisers don’t want to advertise on twitter given it is more prone to free speech now.

The latter was a bit of a surprising theory to me, since my assumption had always been that what advertisers largely care for is audiences, and relevance of the audiences to their products; and as long as the audiences were there, the advertisers would come.

However, something I heard on a podcast this morning on my way to work made me question this assumption. Listen to this (the link is from the approximate point I want you to listen):

So in this conversation, Jeff Green talks about “brand safety” in the context of advertising. What he effectively says is that advertisers are finicky about what kind of content their ads come next to. He says “right now I would say the value of user generated content has actually gone down dramatically because of brand safety”.

Back in IIMB, there were a couple of fellows who formed a quiz team called “Mary Magdalenes: The Reformed Prostitutes”. During our annual fest Unmaad, they conducted a quiz, which (I think) was sponsored by IBM. I I remember right, the title slide of the quiz said “Unmaad Open Quiz, brought to you by Mary Magdalenes: The Reformed Prostitutes”, with the logo of IBM (or whoever the sponsor was) somewhere on the slide.

The sponsors did not take to it too kindly – I was doing a quiz the following day and the sponsorship coordinators demanded to inspect my deck so that there were no such potentially embarrassing juxtapositions.

As it happens, one, or maybe both, of Mary Magdalenes: The Reformed Prostitutes, went into a career in marketing. However, contrary to the image you get by looking at advertising “creatives”, advertisers are fundamentally boring people. They are insanely risk averse, and very very loathe to bring even the slightest hint of controversy to their brands.

So, this is why social media goes woke. They don’t care about “misinformation” and “fake news” and porn and slander for the sake of you or me – as long as we are visiting their sites and looking at the ads there, they are happy. The reason they clamp down on free speech in the name of “trust and safety” is for the sake of the (normally rather boring) advertisers, who want certainty on the sort of content next to which their ads are shown.

And so, driven by risk-averse advertisers, social media platforms censor free speech and “go woke”, much to the chagrin of people like Musk and me.

Recently I read this fantastic essay by Robin Hanson on why most people are boring. Only a very long quote will do justice, but that too partially. You should read the whole essay.

If we act interesting, passionate, and opinionated in public, we are likely to seem to claim high status for ourselves, and to touch on sacred subjects, either by word or deed. And this makes us quite vulnerable to accusations of arrogance and violating the sacred. Because: a) the sacred is full of contradictions, so that saying truths clearly does not protect you, b) observers feel free to use complex codings to attribute to you intentions that you did not literally say (or have), and c) observers are much more willing to accept unfair and unproven accusations if they are seen as “punching up” at presumed dominant or evil races, genders, ages, professions, or political factions.

The degree of this danger is made clear, I think by the reaction of the “gods” among us. The public tone of huge powerful firms and other orgs is consistently “officious”, i.e., mild boring supplication.

Mild boring supplication is all okay. Just that they impose upon you and me with their ad dollars, meaning that places where their ad dollars go also tend to mild boring supplication. And thus for us, it is death by a thousand bores.

Discoverability and chaos

Last weekend (4-5 Feb) I visited Blossom Book House on Church Street (the “second branch” (above Cafe Matteo), to be precise). I bought a total of six books that day, of which four I was explicitly looking for (including two of Tufte’s books). So only two books were “discovered” in the hour or so I spent there.

This weekend (11-12 Feb) I walked a little further down Church Street (both times I had parked on Brigade Road), and with wife and daughter in tow, to Bookworm. The main reason for going to Bookworm this weekend is that daughter, based on a limited data points she has about both shops, declared that “Bookworm has a much better collection of Geronimo Stilton books, so I want to go there”.

This time there were no books I had intended to buy, but I still came back with half a dozen books for myself – all “discovered”. Daughter got a half dozen of Geronimos. I might have spent more time there and got more books for myself, except that the daughter had finished her binge in 10 minutes and was now desperate to go home and read; and the wife got bored after some 10-20 minutes of browsing and finding one book. “This place is too chaotic”, she said.

To be fair, I’ve been to Blossom many many more times than I’ve been to Bookworm (visits to the latter are still in single digits for me). Having been there so many times, the Blossom layout is incredibly familiar to me. I know  that I start with the section right in front of the billing counter that has the bestsellers. Then straight down to the publisher-wise shelves. And so on and so forth.

My pattern of browsing at Blossom has got so ritualised that I know that there are specific sections of the store where I can discover new books (being a big user of a Kindle, I don’t really fancy very old books now). And so if I discover something there, great, else my browsing very quickly comes to a halt.

At Bookworm, though, I haven’t yet figured out the patterns in terms of how they place their books. Yes, I agree with my wife that it is “more random”, but in terms of discoverability, this increased randomness is a feature for me, not a bug! Not knowing what books to expect where, I’m frequently pleasantly surprised. And that leads to more purchases.

That said, the chaos means that if I go to the bookstore with a list of things to buy, the likelihood of finding them will be very very low (that said, both shops have incredibly helpful shopkeepers who will find you any book that you want and which is in stock at the store).

Now I’m thinking about this in the context of e-commerce. If randomness is what drives discoverability, maybe one bug of e-commerce is that it is too organised. You search for something specific, and you get that. You search for something vague, and the cost of going through all the results to find something you like is very high.

As for my books, my first task is to finish most of the books I got these weekends. And I’ll continue to play it random, and patronise both these shops.

Product management and Bengaluru Cafe

My favourite restaurant within “normal walking distance” (i.e. a quick dash – not a long walk that I’m fully capable of) of my house is Bengaluru Cafe in Jayanagar 2nd Block. The masaldose there is very very good, right up there with that at CTR (and far less crowded; Vidyarthi Bhavan dose is a different genus).

It’s crisp outside and soft inside, and what I really like about the dose there is the red chutney that they put inside. Spicy and garlicky, and a nice throwback to masaldose in Bangalore in the 1990s (Adigas, for whatever reason, replaced this red chutney with Chutney  puDi, which is far inferior, and now a lot of the new places put Tamil style chutney puDi which is massively overwhelming).

I had discovered the place in mid 2019, while driving back after closing a long client assignment. The dose was absolutely fantastic. We started going there regularly – rather, bringing the dose parcelled from there (since it’s close enough and crowded). It was with this dose that I had my first “unpaternal instinct” – I had got 3 doses (one for each of us), and kept hoping the daughter wouldn’t finish hers so that I could get some of it. As it happened, the then sub-3-year-old fully polished it off.

And  then something changed – I came home to find that there was no red chutney in the dose (which made it significantly suboptimal). And it happened once again. The next time I went I asked about it, and was told that if I want it I need to ask for it.

It is basically the minority rule in action. A large part of the clientele of the Bengaluru Cafe don’t eat garlic, so don’t want the red chutney. Initially the default was to have the chutney, but the number of requests meant the defaults flipped! And that entirely changed the product.

There was a further caveat – if I wanted red chutney in my dose on Sunday I was entirely out of luck. The crowd on Sunday meant that they would not offer any customisations (red chutney became a “customisation”) so that they could mass produce. So I entirely stopped going there on Sundays.

I went there yesterday morning to buy breakfast. It wasn’t crowded so I could stand near the counter watching them make the dose. In the full griddle of 15 doses, only 2 had the red chutney smeared on – the two that I had ordered. Just one small change in the defaults meant that the produce has changed so much!

Bengaluru Cafe was recently featured on a YouTube food channel that we happeened to watch.

If you watch the video till ~3:25 you will find an interesting thing the host says “the difference with their masaldose is that they don’t spread chutney inside it at all!”

Which means the default has changed so much that people don’t even know what used to be the old product!

As far as I’m concerned, it’s a bit stressful – the reason we all love the dose there is because of the red chutney inside. So I know that if I end up bringing dose without the chutney the family will be disappointed. So I need to make sure I stand at the counter to ensure they put the chutney on our doses.

Recreating Tufte, and Bangalore weather

For most of my life, I pretty much haven’t understood what the point of “recreating” is. For example, in school if someone says they were going to “act out ______’s _____” I would wonder what the point of it was – that story is well known so they might as well do something more creative.

Later on in life, maybe some 12-13 years back, I discovered the joy in “retelling known stories” – since everyone knows the story you can be far more expressive in how you tell it. Still, however, just “re-creation” (not recreation) never really fascinated me. Most of the point of doing things is to do them your way, I’ve believed (and nowadays, if you think of it, most re-creating can be outsourced to a generative AI).

And the this weekend that changed. On Saturday, I made the long-pending trip to Blossom (helped that daughter had a birthday party to attend nearby), and among other things, I bought Edward Tufte’s classic “The Visual Display of Quantitative Information“. I had read a pirated PDF of this a decade ago (when I was starting out in “data science”), but always wanted the “real thing”.

And this physical copy, designed by Tufte himself, is an absolute joy to read. And I’m paying more attention to the (really beautiful) graphics. So, when I came across this chart of New York weather, I knew I had to recreate it.

A few months earlier, I had dowloaded the dataset for Bangalore’s hourly temperature and rainfall since 1981 (i.e. a bit longer than my own life). This dataset ended in November 2022, but I wasn’t concerned. Basically, this is such a large and complex dataset that so far I had been unable to come up with an easy way to visualise it. So, when I saw this thing from Tufte, recreating would be a good idea.

I spent about an hour and half yesterday doing this. I’ve ignored the colour schemes and other “aesthetic” stuff (just realised I’ve not included the right axis in my re-creation). But I do think I’ve got something fairly good.

My re-creation of Tufte’s New York weather map, in the context of Bangalore in 2022

2022 was an unusual weather year for Bangalore and it shows in this graph. May wasn’t as hot as usual, and there were some rather cold days. Bangalore recorded its coldest October and November days since the 90s (though as this graph shows, not a record by any means). It was overall a really wet year, constantly raining from May to November. The graph shows all of it.

Also if you look at the “noraml pattern” and the records, you see Bangalore’s unusual climate (yes, I do mean “climate” and not “weather” here). Thanks to the monsoons (and pre-monsoons), April is the hottest month. Summer, this year, has already started – in the afternoons it is impossible to go out now. The minimum temperatures are remarkably consistent through the year (so except early in the mornings, you pretty much NEVER need a sweater here – at least I haven’t after I moved back from London).

There is so much more I can do. I’m glad to have come across a template to analyse the data using. Whenever I get the enthu (you know what this website is called) I’ll upload my code to produce this graph onto github or something. And when I get more enthu, I’ll make it aesthetically similar to Tufte’s graph (and include December 2022 data as well).


Ants and grasshoppers and mental health

There is the old fable of the ant and the grasshopper – the ant saves and saves and saves and at the end has plenty. The grasshopper splurges and splurges and enjoys and at the end has nothing. In some versions, the grasshopper dies. In others, he borrows from the ant. Most tellings of the fable don’t end well for the grasshopper.

“Be like the ant”, goes the moral of the story.

I’m not so sure if that is the right strategy for “real life”. Talking about myself, I have spent large parts of my life living like an ant, and a lot of it has not been fun. I’m not talking about money here – credit cards apart, I’m entirely debt-free, and my wife and I paid off our home loan (the only big loan I’ve taken) in a fifth of the term. That has allowed us to take risks in terms of careers, and do more interesting things, so that part of “living like an ant” I don’t regret at all.

It is more on the non-monetary fronts. I might have written about this in the past, likening it to the movie Ganesha Subramanya. The plot there is a classic ant plot – that you “need to achieve something in life” before you can find a girlfriend or get married. And various people making fun of the protagonists for this philosophy.

Quoting from my old blogpost on this:

In the two years prior to going to IIT, it had been drilled into my head that it was wrong to relax or have fun until I had “achieved my goals”, which at that point in time was basically about getting into IIT. I did have some fun in that period, but it usually came with a heavy dose of guilt – that I was straying from my goal.

In any case, I got into IIT and the attitude continued. I felt that I couldn’t relax until I had “finished my work”. And since IIT was this constant treadmill of tests and exams and assignments and grades, this meant that this kind of “achievement” of finishing work didn’t come easily. And so I went about my life without chilling. And was unhappy.

Sometimes I think this problem went away in my twenties, but now that I think deeper about it, whether I think like an ant or a grasshopper is related to my state of mind, and it is self-fulfilling. When I am feeling contented and fine (what I like to think is my “normal state”) I’m a grasshopper. I sometimes bite off too much. I want to do everything. I want to enjoy also. And sometimes that means putting off work (or “borrowing from my future time”).

However, when I’m going through a rough patch or not in the best of mental health, I suddenly go off into ant mode. I don’t want to risk going lower, so I become extra cautious. Extra caution means fulfilling my responsibilities as and when they come, and putting off the fun for later (rather than the other way round). In other words you don’t want to borrow – from your future time!

If you think of utility theory, your “happiness” (or “welfare”) as a function of your “wealth” (need not always be monetary – can be physical or mental health as well) is concave. The more wellness you have, the less the marginal utility of getting more wellness (among other things, this explains why insurance, on average, can get away with offering a lower rate of return).

Among other things, what this means is that the loss of wellness from the loss of a rupee far exceeds the gain of wellness from the gain of a rupee (and this is consistent at all wealth levels – again I’m using rupees only for convenience here). And so when you are in a bad mental state, if you are optimising for not slipping further, you will necessarily follow a low-risk policy. And you become more “anty” (and antsy, of course).

Somewhere you need to break off that cycle. Even when you are otherwise not feeling well, you need to somehow give yourself that stimulus, and that means being a grasshopper. It is a conscious effort that you need to make – that yes, your life is shit and you are not doing well, but being an ant is most likely NOT going to help you get out of it.

And slowly you transition your way out. You will realise that occasionally you CAN borrow from your future time – that maximises your overall happiness over time (while at the same time not shirking). And you start being more of a grasshopper. And so forth until you are in “ground state”.

In some way a lot of fables have their morals the wrong way around – favouring the ant over the grasshopper; favouring the hedgehog over the fox. I guess a lot of them simply haven’t aged well enough to our current context and lifestyles!

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.