Category: arbit

Intelligent and Diligent

For whatever reason, when I was a schoolboy and first learnt of the word “diligent”, I assumed that it should be the opposite on intelligent. “Only people who are not intelligent need to be diligent”, the young I had reasoned.

And nearly thirty years later, I came across this stellar 2×2 on intelligence and diligence. I’ve read it in many places now, but will link to the version on farnam street blog. I’m copying this quote from the blog, which is apparently credited to two different military officers.

I divide my officers into four groups. There are clever, diligent, stupid, and lazy officers. Usually two characteristics are combined. Some are clever and diligent — their place is the General Staff. The next lot are stupid and lazy — they make up 90 percent of every army and are suited to routine duties. Anyone who is both clever and lazy is qualified for the highest leadership duties, because he possesses the intellectual clarity and the composure necessary for difficult decisions. One must beware of anyone who is stupid and diligent — he must not be entrusted with any responsibility because he will always cause only mischief.

Maybe I was up to something interesting back in the 1990s, even if it was rather self-serving. And maybe it is this concept I reprised in the late 2000s when I came up with “studs and fighters“. It was possibly my irritation with the “stupid and diligent” variety.

Now I’m thinking of this “stupid and diligent” 2×2 in terms of our schooling and education. Maybe there is this general feeling among parents, teachers and suchlike that intelligence is something you are “born with”, and you cannot become intelligent.

So the moment they spot a kid who is stupid and lazy, they decide that the best way to “improve” this kid is to make him/her more diligent, rather than more intelligent. In the short run this might work, since the kid is now able to do better in the school exams (which is what most teachers are optimising for). The long run effect, though, is that the kid, instead of ending up in the numerous but harmless “general staff” (stupid and lazy), ends up in the seemingly more competent but actually “dangerous, and only causing mischief” stupid and diligent quadrant.

In other words, our general schooling makes our adult population much more dangerous!

When Institutions Decay

A few weeks back, I’d written about “average and peak skills“. The basic idea in this blogpost is that in most jobs, the level of skills you need on most days (or the “average skill” you need) is far far inferior to the “peak skill” level required occasionally.

I didn’t think about this when I wrote that blogpost, but now I realise that a lot of institutional decay can be simply explained by ignoring this gap between average and peak skills required.

I was at my niece’s wedding this morning, and was talking to my wife about the nadaswara players (and more specifically about this tweet):

“Why do you even need a jalra”, she asked. And then I pointed out that the jalra guy had now started playing the nadaswara (volga). “Why do we need this entire band”, she went on, suggesting that we could potentially use a tape instead.

This is a classic case of peak and average skill. The average skill required by the nadaswara player (whether someone sitting there or just operating a tape) is to just play, play it well and play in sync with the dhol guy. And if you want to maximise for the sheer quality of the music played, then you might as well just buy a tape and play it at the venue.

However, the “peak skill” of the nadaswara player goes beyond that. He is supposed to function without instruction. He is supposed to keep an eye on what is happening at the wedding, have an idea of the rituals (given how much the rituals vary by community, this is nontrivial) and know what kind of music to play when (or not play at all). He is supposed to gauge the sense of the audience and adjust the sort of music he is playing accordingly.

And if you consider all these peak skills required, you realise that you need a live player rather than a tape. And you realise that you need someone who is fairly experienced since this kind of judgment is likely to come more easily to the player.

The problem with professions with big gaps between average and peak skills, and where peak skills are seldom called upon, is that penny-pinching managers can ignore the peak and just hire for average skill (I had mentioned this in my previous post on the topic as well).

In the short run, there is an advantage in that people with average skills for the job are far cheaper than those with peak skills for the job (and the former are unlikely to suffer motivational issues as well). Now, over a period of time you find that these average skilled people are able to do rather well (and are much cheaper and much lower maintenance than peak skilled people).

Soon you start questioning why you need the peak skill people after all. And start replacing them with average people. The more rare the requirement of peak skill is in the job, the longer you’ll be able to go on like this. And then one day you’ll find that the job on that day required a little more nuance and skill, and your current team is wholly incapable of handling it.

You replace your live music by tapes, and find that your music has got static and boring. You replace your bank tellers with a combination of ATMs and call centres, and find it impossible to serve that one customer with an idiosyncratic request. You replace your software engineers with people who don’t have that good an idea of algorithmic theory, and one day are saddled with inefficient code.

Ignoring peak skill required while hiring is like ignoring tail risk. Because it is so improbable, you think it’s okay to ignore it. And then when it hits you it hits you hard.

Maybe that’s why risk management is usually bundled into a finance person’s job – if the same person or department in charge of cutting costs is also responsible for managing risk, they should be able to make better tradeoffs.

Everyone can be above average

All it requires is some selection bias

There were quite a few teachers during my time at IIT Madras who were rumoured to have said the line “I want everyone in class to be above average”. Some people credit a professor of mathematics for saying this. At other times, the quote is ascribed to a lecturer of Engineering Drawing. In the last 20 years I’m sure even some statistics professors would have been credited with this line.

The absurdity in the line is clear. By definition, everyone cannot be above average. The average is a measure of central tendency. However you define it (arithmetic mean, geometric mean, harmonic mean, median, mode), the average is by definition a “central value”, meaning you will have numbers both above and below it. In the worst case (assuming you are using a mode or median for a highly skewed distribution), there will be a large number of data points EQUAL to the average. Everyone cannot be above (strictly greater than) average.

However, based on some recent incidents, I figured out a way in which everyone can actually be above average. All it takes is some kind of selection bias. Basically you need to be clever in terms of how you count – both when you calculate the average and when you define the “everyone”.

Take one example – you have an exam you need to pass to go from Grade 1 to Grade 2. Let’s say the class average (let’s use the simple mean here) is 41, and you need to have scored at least 40 to pass. Let’s also assume that nobody has scored exactly 40 or 41.

Now, if you come back next month and look at the exam scores of all the Grade 2 students, you will find that all of them would have scored strictly more than 41 – the old “average”. In other words, since the below average students are no longer part of the sample (since they have “not passed”), everyone left is above average! The below average set has simply been eliminated!

Another way is simple relative grading. Let’s say there are 3 sections in the class. Telling one section that “everyone should be above average” is fairly legit – all it says is that this particular section should outperform the others so significantly that everyone in this section will be above the average defined by all sections!

It is easier to do in code – using some statistical packages, as long as you slip in a few missing values into your dataset, you will find that the average is meaningless, and when you ask your software for how many are above average, the program defaults can mean that everyone can be classified as “above average” (even the ones with missing values).

I must have recommended this a few times already, but Darrell Huff’s 1954 book How to Lie With Statistics remains a masterpiece.

 

An interesting experiment

Hello from kochi.

I’ve volunteered to accompany a group of children from my daughters school on a trip to the kochi biennale. And here we are.

I sometimes like to say that my daughters school curriculum is like an office – they had invited me for a day of “observation” in January and what I saw was a lot of group work, collaboration, research, etc. I also know there is plenty of making presentations and reports. (there are no lectures)

And in an extension of “office life” they made the kids catch a 7am flight as well! Most kids woke ip ~3 to meet outside the airport at 5. While my initial reaction was “too early” the slack helped significantly in terms of taking the large group through the airport.

In any case, so far we’ve only been at the hotel and rested and had lunch, and yet to go see town (writing this in the interregnum between lunch and heading out)

Yet, quite a few pertinent observations so far

  • Kids are resourceful. One has produced a pack of cards and another has carried a whole monopoly set (neither is my daughter)
  • Kids are inventive. In the absence of playing material I’ve seen them invent a “pen cap hide and seek”. One counts while the other hides a pen cap somewhere in the room. And the counter searches. A hotel room is a small space to hide an entire person (however small) so this is a nice workaround
  • This is a harsh lesson of growing up – in the presence of their friends, your children sometimes don’t really want you. Or want to talk to you.
  • Left to themselves kids sometimes do “constructive play”. This morning one boy said to another “do you want to sketch?” And the other agreed. The first then produced a notebook and colour pencil box and the two quietly say drawing for the next half hour.
  • The noise cancelling feature of AirPods Pro rocks. And can sometimes be a lifesaver.

More later!

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!”

 

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.

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.