arbit – Page 7 – Pertinent Observations

More on CRM

On Friday afternoon, I got a call on my phone. It was “+91 9818… ” number, and my first instinct was it was someone at work (my company is headquartered in Gurgaon), and I mentally prepared a “don’t you know I’m on vacation? can you call me on Monday instead” as I picked the call.

It turned out to be Baninder Singh, founder of Savorworks Coffee. I had placed an order on his website on Thursday, and I half expected him to tell me that some of the things I had ordered were out of stock.

“Karthik, for your order of the Pi?anas, you have asked for an Aeropress grind. Are you sure of this? I’m asking you because you usually order whole beans”, Baninder said. This was a remarkably pertinent observation, and an appropriate question from a seller. I confirmed to him that this was indeed deliberate (this smaller package is to take to office along with my Aeropress Go), and thanked him for asking. He went on to point out that one of the other coffees I had ordered had very limited stocks, and I should consider stocking up on it.

Some people might find this creepy (that the seller knows exactly what you order, and notices changes in your order), but from a more conventional retail perspective, this is brilliant. It is great that the seller has accurate information on your profile, and is able to detect any anomalies and alert you before something goes wrong.

Now, Savorworks is a small business (a Delhi based independent roastery), and having ordered from them at least a dozen times, I guess I’m one of their more regular customers. So it’s easy for them to keep track and take care of me.

It is similar with small “mom-and-pop” stores. Limited and high-repeat clientele means it’s easy for them to keep track of them and look after them. The challenge, though, is how do you scale it? Now, I’m by no means the only person thinking about this problem. Thousands of business people and data scientists and retailers and technology people and what not have pondered this question for over a decade now. Yet, what you find is that at scale you are simply unable to provide the sort of service you can at small scale.

In theory it should be possible for an AI to profile customers based on their purchases, adds to carts, etc. and then provide them customised experiences. I’m sure tonnes of companies are already trying to do this. However, based on my experience I don’t think anyone is doing this well.

I might sound like a broken record here, but my sense is that this is because the people who are building the algos are not the ones who are thinking of solving the business problems. The algos exist. In theory, if I look at stuff like stable diffusion or Chat GPT (both of which I’ve been playing around with extensively in the last 2 days), algorithms for stuff like customer profiling shouldn’t be THAT hard. The issue, I suspect, is that people have not been asking the right questions of the algos.

On one hand, you could have business people looking at patterns they have divined themselves and then giving precise instructions to the data scientists on how to detect them – and the detection of these patterns would have been hard coded. On the other, the data scientists would have had a free hand and would have done some unsupervised stuff without much business context. And both approaches lead to easily predictable algos that aren’t particularly intelligent.

Now I’m thinking of this as a “dollar bill on the road” kind of a problem. My instinct tells me that “solution exists”, but my other instinct tells that “if a solution existed someone would have found it given how many companies are working on this kind of thing for so long”.

The other issue with such algos it that the deeper you get in prediction the harder it is. At the cohort (of hundreds of users) level, it should not be hard to profile. However, at the personal user level (at which the results of the algos are seen by customers) it is much harder to get right. So maybe there are good solutions but we haven’t yet seen it.

Maybe at some point in the near future, I’ll take another stab at solving this kind of problem. Until then, you have human intelligence and random algos.

Cross docking in Addis Ababa

I’m writing this from Addis Ababa bole international airport, waiting for my connection to Kilimanjaro. We arrived here some 3 hours back, on a direct flight from bangalore.

The flight was fine, and uneventful. It was possibly half empty, though – the guy in the front seat had all 3 seats to himself and had lay down across them.

Maybe the only issue with the flight was that they gave us “dinner” at the ungodly time of 3am (1230 Eastern Africa time). I know why – airlines prefer to serve as soon as they take off since food is freshest then (rather than reheating at the end of the flight). And if they serve two meals the second one is usually a cold one (sandwiches cakes etc)

The airport here is also uneventful. There are a couple of bars and a few nondescript looking coffee shops. It is linear, with all gates being laid out in a row (reminds me of KL, and very unlike “star shaped airports” such as Barcelona or Delhi).

In any case I’ve been doing the rounds since morning looking for information of my flight gate. The last time I saw it hadn’t yet been published. But there was something very interesting about the flight schedule.

Basically, this airport serves as a cross dock between Africa and the rest of the world, taking advantage of its location in one corner of the continent.

For example, all flights that have either departed in the last hour or due to depart in the next 2 hours are to various destinations in Africa (barring one flight to São Paulo and Buenos Aires).

Kinshasa. Cape Town. Douala. Antananarivo. Entebbe. Accra. Lubumbashi via Lilongwe. Mine to Kilimanjaro (and then onward to Zanzibar). Etc. etc.

No flight that goes north or east, barring one to Djibouti. And no take offs between 6am (when we landed here) till 815 (Cape Town). And until around 8, people kept streaming into the airport (and the lines at the toilets kept getting longer!)

Ethiopian’s schedule at bangalore is also strange. Flights arrive at 8am 3 days of the week and then hang in there idly till 230 am the next morning. Time wise, that’s incredibly low utilisation of a costly asset like an aircraft (that said it’s a Boeing 737Max).

After looking at the airport schedule though it makes more sense to me. Basically in the morning, flights bring in passengers from all over Asia and Europe, and connect them to various places in Africa.

In the evenings, flights stream in from all around Africa and cross dock people to destinations in Europe and Asia. Currently the cross dock is one way – out of Africa in the evenings and into Africa in the mornings.

This means that there are some destinations where, given time of travel, the only way to make this cross dock work is to keep the aircraft idle at the destination. In African destinations for example, I expect shorter turnarounds – this morning I noticed that the first set of departures were to far away locations – Cape Town, Johannesburg, Accra, Harare and then to Lusaka, etc.

I don’t expect this to last long though. In a few years (maybe already delayed by the pandemic) I expect ethiopian to double its flight capacity across all existing destinations. Then, it can operate both into Africa and out of Africa cross docks twice in a day. And won’t need to waste precious flight depreciation time at faraway airports such as bangalore.

PS: so far I haven’t seen a single flight from any other airline apart from Ethiopian at the airport here.

Heads of departments

Recently I was talking to someone about someone else. “He got an offer to join XXXXXX as CTO”, the guy I was talking to told me, “but I told him not to take it. Problem with CTO role is that you just stop learning and growing. Better to join a bigger place as a VP”.

The discussion meandered for a couple of minutes when I added “I feel the same way about being head of analytics”. I didn’t mention it then (maybe it didn’t flash), but this was one of the reasons why I lobbied for (and got) taking on the head of data science role as well.

I sometimes feel lonely in my job. It is not something anyone in my company can do anything about. The loneliness is external – I sometimes find that I don’t have too many “peers” (across companies). Yes, I know a handful of heads of analytics / data science across companies, but it is just that – a handful. And I can’t claim to empathise with all of them (and I’m sure the feeling is mutual).

Irrespective of the career path you have chosen, there comes a point in your career where your role suddenly becomes “illiquid”. Within your company, you are the only person doing the sort of job that you are doing. Across companies, again, there are few people who do stuff similar to what you do.

The kind of problems they solve might be different. Different companies are structured differently. The same role name might mean very different things in very different places. The challenges you have to face daily to do your job may be different. And more importantly, you might simply be interested in doing different things.

And the danger that you can get into when you get into this kind of a role is that you “stop growing”. Unless you get sufficient “push from below” (team members who are smarter than you, and who are better than you on some dimensions), there is no natural way for you to learn more about the kind of problems you are solving (or the techniques). You find that your current level is more than sufficient to be comfortable in your job. And you “put peace”.

And then one day you find ten years have got behind youNo one told you when to run, you missed the starting gun

(I want you to now imagine the gong sound at the beginning of “Time” playing in your ears at this point in the blogpost)

One thing I tell pretty much everyone I meet is that my networking within my own industry (analytics and data science) is shit. And this is something I need to improve upon. Apart from the “push from below” (which I get), the only way to continue to grow in my job is to network with peers and learn from them.

The other thing is to read. Over the weekend I snatched the new iPad (which my daughter had been using; now she has got my wife’s old Macbook Air) and put all my favourite apps on it. I feel like I’m back in 2007 again, subscribing to random blogs (just that most of them are on substack now, rather than on Blogspot or Livejournal or WordPress), in the hope that I will learn. Let me see where this takes me.

And maybe some people decide that all this pain is simply not worth it, and choose to grow by simply becoming more managerial, and “building an empire”.

Signalling, anti-signalling and dress codes

A few months back, I read Rob Henderson‘s seminal work on signalling and anti-signalling. To use a online community term, I’ve been “unable to unsee”. Wherever I see, I see signalling, and anti-signalling. Recently, I thought that some things work as signals to one community but anti-signals to others. And so on.

I was reminded of this a couple of weekends back when we were shopping at FabIndia. Having picked up a tablecloth and other “house things”, my wife asked if I wanted to check out some shirts. “No, I have 3 FabIndia shirts in the washing pile”, I countered. “I like them but maintenance is too hard, so not buying”.

The issue with FabIndia shirts is that they leech colour, so you cannot put them in the washing machine (especially not with other clothes). Sometimes you might get lucky to get a quorum of indigos (and maybe jeans) to put in the machine at a time, but if you want to wear your FabIndia clothes regularly you have no option but to wash them by hand. Or have them someone wash them for you.

That gave rise to the thought that FabIndia shirts can possibly send out a strong signal that you are well to do, since you have domestic help – since these shirts need to be hand washed and then pressed before wearing (the logistics of giving clothes for pressing near my house aren’t efficient, and if I’ve to do it consistently, I need help with that. I end up wearing Tshirts that don’t need much ironing instead).

On the other hand, the black T-shirts (I have several in various styles, with and without my company logo) I wear usually are very low maintenance. Plonk them into the washing machine with everything else. No need of any ironing. I don’t need no help to wear such clothes.

And then I started thinking back to the day when I would wear formal shirts regularly. Those can go into the washing machine (though you are careful on what you put in with them), but the problem is that they need proper ironing. You either spend 20 minutes per shirt, or figure out dynamics of giving them out for ironing regularly (if you’re lucky enough to have an iron guy close to your house) – which involves transaction costs. So again wearing well cleaned and ironed formals sends out a signal that you are well to do.

I think it was Rob Henderson again (not sure) who once wrote that the “casualisation” of office dress codes has done a disservice to people from lower class backgrounds. The argument here is that when there is a clear dress code (suits, say), everyone knows what to wear, and while you can still signal with labels and cufflinks and the cut of your suit, it is hard to go wrong.

In the absence of formal dress codes, however, people from lower class are at a loss on what to wear (since they don’t know what the inherent signals of different clothes are), and the class and status markers might be more stark.

My counterargument is that the effort to maintain the sort of clothes most dress codes demand is significant, and imposing such codes puts an unnecessary burden on those who are unable to afford the time or money for it. The lack of a dress code might make things ambiguous, but in most places, the Nash equilibrium is most people wearing easy-to-maintain clothes (relative to the image they want to portray), and less time and money going in conformity.

As it happened, I didn’t buy anything at FabIndia that day. I came home and looked in the washing bin, and found a quorum of indigo shirts (and threw in my 3-month old jeans) to fill the washing machine. My wife requested our domestic helper to hand-wash the brown FabIndia shirts. While watching the T20 world cup, I ironed the lot. I’m wearing one of them today, as I write this.

They look nice (though some might think they’re funny – that’s an anti-signal I’m sending out). They’re comfortable. But they require too much maintenance. Tomorrow I’m likely to be in a plain black t-shirt again.

Speed, Accuracy and Shannon’s Channel Coding Theorem

I was probably the CAT topper in my year (2004) (they don’t give out ranks, only percentiles (to two digits of precision), so this is a stochastic measure). I was also perhaps the only (or one of the very few) person to get into IIMs that year despite getting 20 questions wrong.

It had just happened that I had attempted far more questions than most other people. And so even though my accuracy was rather poor, my speed more than made up for it, and I ended up doing rather well.

I remember this time during my CAT prep, where the guy who was leading my CAT factory once suggested that I was making too many errors so I should possibly slow down and make fewer mistakes. I did that in a few mock exams. I ended up attempting far fewer questions. My accuracy (measured as % of answers I got wrong) didn’t change by much. So it was an easy decision to forget above accuracy and focus on speed and that served me well.

However, what serves you well in an entrance exam need not necessarily serve you well in life. An exam is, by definition, an artificial space. It is usually bounded by certain norms (of the format). And so, you can make blanket decisions such as “let me just go for speed”, and you can get away with it. In a way, an exam is a predictable space. It is a caricature of the world. So your learnings from there don’t extend to life.

In real life, you can’t “get away with 20 wrong answers”. If you have done something wrong, you are (most likely) expected to correct it. Which means, in real life, if you are inaccurate in your work, you will end up making further iterations.

Observing myself, and people around me (literally and figuratively at work), I sometimes wonder if there is a sort of efficient frontier in terms of speed and accuracy. For a given level of speed and accuracy, can we determine an “ideal gradient” – on which way a person needs to move in order to make the maximum impact?

Once in a while, I take book recommendations from academics, and end up reading (rather, trying to read) academic books. Recently, someone had recommended a book that combined information theory and machine learning, and I started reading it. Needless to say, within half a chapter, I was lost, and I had abandoned the book. Yet, the little I read performed the useful purpose of reminding me of Shannon’s channel coding theorem.

Paraphrasing, what it states is that irrespective of how noisy a channel is, using the right kind of encoding and redundancy, we will be able to predictably send across information at a certain maximum speed. The noisier the channel, the more the redundancy we will need, and the lower the speed of transmission.

In my opinion (and in the opinions of several others, I’m sure), this is a rather profound observation, and has significant impact on various aspects of life. In fact, I’m prone to abusing it in inexact manners (no wonder I never tried to become an academic).

So while thinking of the tradeoff between speed and accuracy, I started thinking of the channel coding theorem. You can think of a person’s work (or “working mind”) as a communication channel. The speed is the raw speed of transmission. The accuracy (rather, the lack of it) is a measure of noise in the channel.

So the less accurate someone is, the more the redundancy they require in communication (or in work). For example, if you are especially prone to mistakes (like I am sometimes), you might need to redo your work (or at least a part of it) several times. If you are the more accurate types, you need to redo less often.

And different people have different speed-accuracy trade-offs.

I don’t have a perfect way to quantify this, but maybe we can think of “true speed of work” by dividing the actual speed in which someone does a piece of work by the number of iterations they need to get it right. OK it is not so straightforward (there might be other ways to build redundancy – like getting two independent people to do the same thing and then tally the numbers), but I suppose you get the drift.

The interesting thing here is that the speed and accuracy is not only depend on the person but the nature of work itself. For me, a piece of work that on average takes 1 hour has a different speed-accuracy tradeoff compared to a piece of work that on average takes a day (usually, the more complicated and involved a piece of analysis, the more the error rate for me).

In any case, the point to be noted is that the speed-accuracy tradeoff is different for different people, and in different contexts. For some people, in some contexts, there is no point at all in expecting highly accurate work – you know they will make mistakes anyways, so you might as well get the work done quickly (to allow for more time to iterate).

And in a way, figuring out speed-accuracy tradeoffs of the people who work for you is an important step in getting the best out of them.

Financial ratio metrics

It’s funny how random things stick in your head a couple of decades later. I don’t even remember which class in IIMB this was. It surely wasn’t an accounting or a finance class. But it was one in which we learnt about some financial ratios.

I don’t even remember what exactly we had learnt that day (possibly return on invested capital?). I think it was three different financial metrics that can be read off a financial statement, and which then telescope very nicely together to give a fourth metric. I’ve forgotten the details, but I remember the basic concepts.

A decade ago, I used to lecture frequently on how NOT to do data analytics. I had this standard lecture that I called “smelling bullshit” that dealt with common statistical fallacies. Things like correlation-causation, or reasoning with small samples, or selection bias. Or stocks and flows.

One set of slides in that lecture was about not comparing stocks and flows. Most people don’t internalise it. It even seems like you cannot get a job as a journalist if you understand the distinction between stocks and flows. Every other week you see comparisons of someone’s net worth to some country’s GDP, for example. Journalists make a living out of this.

In any case, whenever I would come to these slides, there would always be someone in the audience with a training in finance who would ask “but what about financial ratios? Don’t we constantly divide stocks and flows there?”

And then I would go off into how we would divide a stock by a flow (typically) in finance, but we never compared a stock to a flow. For example, you can think of working capital as a ratio – you take the total receivables on the balance sheet and divide it by the sales in a given period from the income statement, to get “days of working capital”. Note that you are only dividing, not comparing the sales to the receivables. And then you take this ratio (which has dimension “days”) and then compare it across companies or across regions to do your financial analysis.

If you look at financial ratios, a lot of them have dimensions, though sometimes you don’t really notice it (I sometimes say “dimensional analysis is among the most powerful tools in data science”). Asset turnover, for example, is sales in a period divided by assets and has the dimension of inverse time. Inventory (total inventory on BS divided by sales in a period) has a dimension of time. Likewise working capital. Profit margins, however, are dimensionless.

In any case, the other day at work I was trying to come up with a ratio for something. I kept doing gymnastics with numbers on an excel sheet, but without luck. And I had given up.

Nowadays I have started taking afternoon walks at office (whenever I go there), just after I eat lunch (I carry a box of lunch which I eat at my desk, and then go for a walk). And on today’s walk (or was it Tuesday’s?) I realised the shortcomings in my attempts to come up with a metric for whatever I was trying to measure.

I was basically trying too hard to come up with a dimensionless metric and kept coming up with some nonsense or the other. Somewhere during my walk, I thought of finance, and financial metrics. Light bulb lit up.

My mistake had been that I had been trying to come up with something dimensionless. The moment I realised that this metric needs to involve both stocks and flows, I had it. To be honest, I haven’t yet come up with the perfect metric (this is for those colleagues who are reading this and wondering what new metric I’ve come up with), but I’m on my way there.

Since both a stock and a flow need to be measured, the metric is going to be a ratio of both. And it is necessarily going to have dimensions (most likely either time or inverse time).

And if I think about it (again I won’t be able to give specific examples), a lot of metrics in life will follow this pattern – where you take a stock and a flow and divide one by the other. Not just in finance, not just in logistics, not just in data science, it is useful to think of metrics that have dimensions, and express them using those dimensions.

Some product manager (I have a lot of friends in that profession) once told me that a major job of being a product manager is to define metrics. Now I’ll say that dimensional analysis is the most fundamental tool for a product manager.

Stereotypes, K-Dramas and ADHD

My wife is currently watching a K-drama which she said I might like, because the leading female character in that is autistic. “You have ADHD, and you might be on the spectrum, so you can at least half watch with me”, she said.

Given that it is in a language that I don’t know, I can’t really “half watch”, but I’ve sat through an aggregate of about ten-fifteen minutes of the show in the last 2-3 days.

My first impression of the show and the character was “gosh she’s such a stereotype”. They showed her in court or something (the character is a lawyer), and she takes something someone says extremely literally. And then there was something else that seemed rather stereotypical and then I almost wrote off the show.

And then they showed one scene, which is also possibly stereotypical (I don’t know) but which I massively massively empathised with, and then my view of the show turned, and at this point in time I’m “half watching” the show (to the best extent you can when you need subtitles) as I write this.

I might have written about this before – back in 2013, after about six months of taking methylphenidate for my ADHD, I had started to believe that it was crimping my creativity. What I thought had defined me until then, which is also something you see a lot on this blog, is connecting very random and seemingly unconnected things.

In fact, I considered that to be one of my superpowers – to see connections that a lot of other people can’t. After a few days of not taking the medication (when I saw myself making those connections again), I decided to get off them. I didn’t get back on till 2020 (as things stand I take them).

Anyway, back to the show, the protagonist is shown having a vision of a whale, and that vision reminds her of something else, and she keeps connecting one thing to another (I was really empathising with her in this snippet), and gets a massive insight that solves the case that she is on. My view of the show turned.

A few pertinent observations before I continue:

One of the speakers at one of the early episodes of NED Talks made a point about how some of have possibly evolved to have what are now considered as “disorders”. “Hunting and gathering are team activities, and you need different skills for it. Not everyone needs to run after the prey. The autistic person in the tribe will be able to detect where the prey is and the rest can hunt it”.
So we have evolved to be different like this. Putting together genetics and game theory, it is a “mixed strategy”.
The downside of being able to connect seemingly unconnected things is that you tend to hallucinate. I’ve written about this, in a completely different context.
Another downside of seeing visions and connecting unconnected things to find a solution to the problem that you’re working on is that it makes it incredibly difficult to communicate your solution. Having seen it in a “vision”, it is less explainable. You cannot “show steps”. Then again I don’t think this trait is specific to people with ADHD or on the autism spectrum – I know one person (very well) who doesn’t have ADHD by any stretch of imagination, but has a worse problem than me in showing steps
I have always been happy that I didn’t study law because it’s “too fighter” and “involves too much mugging”. But then the protagonist in this show shows remarkable attention to detail on things that she can hyperfocus on (and which her visions of whales can lead to). I’ve also read about how Michael Burry found holes in CDOs (back in 2008 during the global financial crisis) because he was able to hyperfocus on some details because he has Aspergers (now classified under the autism spectrum in general)

Anyway as I was writing this, I half watched parts of the second episode. In this again, the protagonist had another vision of the whales, which led to something else and an insight that led her to win her case. Now it appears stereotyping again, after I saw the same setup in two different episodes – it seems like the standard format the show has set up on.

I don’t know if I’ll half watch any more.

Discrete Actions and Inverted Incentives

I remember, about a year or so back, the US weekly non-farm payroll data had shown an uptick in unemployment. Intuitively, a higher unemployment rate indicates lower economic activity, since (among other things) the average purchasing power goes down and fewer things are getting produced (since fewer people are at work). So you would expect the stock market to react to this by going down.

The exact opposite happened. The higher unemployment was greeted with a big rise in the S&P 500. I remember tweeting about it but can’t find it now. But I can find some research someone has done about this:

But here’s the kicker: the S&P500 is inversely related to the unemployment rate, and thus the market actually goes up as a response to a release of a higher than expected unemployment rate. This may seem illogical conceptually, but historical analysis and statistics show that it is true.

In the last 3 years, the unemployment rate in the United States has been surprisingly higher than expected 11 times. The result? The S&P500 went up 80% of those times within a time-frame of 90 minutes (see Fig. 2, click to enlarge the image).

The basic issue (as I see it) is that higher unemployment means lesser likelihood that the US Federal Reserve will raise interest rates. Which means lower rates for the longer foreseeable future, which translates to higher stock prices.

The kicker here is the “discrete action” on part of the Fed. Because their decision (on whether to hike rates or not) is binary, news that decreases their odds of hiking rates, even if it (the news) is bad for the market, leads the market to go up.

You can see this in action elsewhere as well. Let’s say you are the number two at a manufacturing plant, and you are not happy with the way things have been run. However, you know that with the current level of production, the company management will not bother – they only see the numbers and see that the plant is being run well, and they won’t listen to you.

However, if the production drops below a certain level, the management is certain to review the operations, at which point you will be able to make your point to them and be heard, and you will be able to hopefully better influence how the plant is run.

Normally, your incentive is in keeping production as high as possible. But now, with this discrete action (management’s review of your operations) in the picture, your incentives get reversed. It suddenly becomes rational for you to not work so hard to increase production, since lower production means higher chance of a management review.

The problem with a lot of standard economics teaching is that it abstracts away the messiness of real world “step functions” and instead uses a deceptively simple continuously increasing or decreasing demand and supply curves. And so we are conditioned to think that incentives are linear as well.

However, given the step functions inherent in everyday business (which are only made worse (steps become steeper) with discrete actions), the incentives are not linear at all, and there are points in the curve where incentives are actually inverted! And this is everywhere.

I’m writing this on a lazy Sunday morning, having postponed this for over a week, so no enthu da to make pictures and explain my point. However, I guess I’ve explained sufficiently for you to catch my pOint.

Actually – since I have an iPad with a pencil, I did make a simple sketch. Limited by my drawing (and mentally adding curves) skillsBasically normal incentives is like the red line, but the discrete action (modelled here like a negative sigmoid) means that there is a region where the overall payoff is massively downward sloping. Which means your incentives are inverted.

Luxury and frugal managers

You remember very random things from business school, nearly two decades on. Usually none of this is academic – the lessons are only “internalised”, not “learnt”. A lot of it is from outside the classroom, silly things someone said or did or posted on the internal bulletin board. Most of the stuff you remember are rather arbitrary things that professors said, and made it seem like something profound.

“Management is like making music”, one professor lectured to us in the first week of classes at IIMB, back in 2004. “First you make music with what you have, and when you don’t have that, you make music with what you have left”. It was rather random, but random enough to stick in my head 18 years on.

It has been another disappointing season beginning for Liverpool. I didn’t watch the Crystal Palace game last night, but I clearly remember feeling at multiple points during the draw at Fulham that this was “like 2020-21 all over again”. The sort of mistakes that Virgil Van Dijk made. The length of the injury list. More players (Thiago) going off injured midway through the game. Nat Phillips starting. And add some new issues – like having your shiny new striker getting himself sent off and suspended for 3 games for a stupid show of anger.

I see the list of substitutes.

2

Joe Gomez (s 63′)

8

Naby Keita

13

del Castillo Adrian

14

Jordan Henderson (s 63′)

21

Konstantinos Tsimikas (s 63′)

28

Fabio Carvalho (s 79′)

43

Stefan Bajcetic

72

Sepp van den Berg

42

Bobby Clark

Yes, there are youngsters (unlike 2021-22) but that is fully understandable. What I don’t understand is seeing youngsters I’ve never heard of. Two games in, I’m already getting the feeling that this will be a really hard league campaign.

I wonder if Klopp is more of a “luxury manager” than a “frugal manager”. These are two very different management styles, requiring very different skillsets. The names are fairly descriptive.

Luxury managers need luxury. They need resources for “option value”. In the corporate context, they need large budgets and space and little control over how they operate. And given all of this, a lot of the time, they deliver big. Yes – there are cases where they spectacularly fail (in which case they don’t stay on in their management jobs), but when they do deliver they deliver big.

Frugal managers don’t need any of this luxury. They are experts at making the most of whatever they have been given. In Ramnath’s words, they are adept at “making music with what they have left”. Any kind of luxury, any kind of optionality, seems like a waste to them. Why pay the option premium when you can get the same payoff through a complicated basket of one deltas?

And just like any other dichotomies (think of studs vs fighters, for example), luxury and frugal managers struggle in the opposite settings. Without the luxury, luxury managers are simply out of their depth. They are necessarily wasteful (a bit like Salah) and cannot produce if they are not able to waste some. However, they win big when they do.

Frugal managers are good at eking out solutions in terms of adversity, but abundant resources can overwhelm them. They won’t know what to do with it. More importantly, they are unable to deal with the expectations of delivering big (which come with the luxury) – they have been experts at delivering small against nonexistent expectations.

What about teams though? If you’ve been used to working for a luxury manager, what happens when you get a frugal manager? And the other way round? I don’t have immediate answers for this but I suppose you will struggle as well?

Pirate organisations

It’s over 20 years now since I took a “core elective” (yeah, the contradiction!) in IIT on “design and analysis of algorithms”. It was a stellar course, full of highly interesting assignments and quotable quotes. The highlight of the course was a “2 pm onwards” mid term examination, where we could take as much time as we wanted.

Anyway, the relevance of that course to this discussion is one of the problems in our first assignment. It was a puzzle .

It has to do with a large number of pirates who have chanced upon a number of gold coins. There is a strict rank ordering of pirates from most to least powerful (1 to N, with 1 being the most powerful). The problem is about how to distribute the coins among the pirates.

Pirate 1 proposes a split. If at least half the pirates (including himself) vote in favour of the split, the split is accepted and everyone goes home. If (strictly) more than half vote against the split, the pirate is thrown overboard and Pirate 2 proposes a split. This goes on until the split has been accepted. Assuming all the pirates are perfectly rational, how would you split the coins if you were Pirate 1? There is a Wikipedia page on it.

I won’t go into the logic here, but the winning play for Pirate 1 is to give 1 coin to each of the other odd numbered pirates, and keep the rest for himself. If he fails to do so and gets thrown overboard, the optimal solution for Pirate 2 is to give 1 coin to each of the other even numbered pirates, and keep the rest for himself.

So basically you see that this kind of a game structure implies that all odd numbered pirates form a coalition, and all the even numbered pirates form another. It’s like if you were to paint all pirates in one coalition black, you would get a perfectly striped structure.

Now, this kind of a “alternating coalition” can sometimes occur in corporate settings as well. Let us stick to just one path in the org chart, down to the lowest level of employee (so no “uncles” (in a tree sense) in the mix).

Let’s say you are having trouble with your boss and are unable to prevail upon her for some reason. Getting the support of your peers is futile in this effort. So what do you do? You go to your boss’s boss and try to get that person onside, and together you can take on your boss. This can occasionally be winning.

Similarly, let us say you seek to undermine (in the literal sense) one of your underlings who is being troublesome. What do you do? You ally with one of their underlings, to try and prevail upon your underling. Let’s say your boss and your underling have thought similarly to you – they will then ally to try and take you down.

Now see what this looks like – your boss’s boss, you and your underling’s underling are broadly allied. Your boss and your underling (and maybe your underling’s underling’s underling) are broadly allied. So it is like the pirate problem yet again, with people alternate in the hierarchy allying with each other!

Then again, in organisations, alliances and rivalries are never permanent. For each piece of work that you seek to achieve, you do what it takes and ally with the necessary people to finish it. And so, in the broad scheme of all alliances that happen, this “pirate structure” is pretty rare. And so it hasn’t been studied well enough.

PS: I was wondering recently why people don’t offer training programs in “corporate game theory”. The problem, I guess, is that no HR or L&D person will sponsor it – there is no point in having everyone in your org being trained in the same kind of game theory – they will nullify each other and the training will do down the drain.

I suppose this is why you have leadership coaches – who are hired by individual employees to navigate the corporate games.