Category: business

George Mallory and Metrics

It is not really known if George Mallory actually summited the Everest in 1924 – he died on that climb, and his body was only found in 1999 or so. It wasn’t his first attempt at scaling the Everest, and at 37, some people thought he was too old to do so.

There is this popular story about Mallory that after one of his earlier attempts at scaling the Everest, someone asked him why he wanted to climb the peak. “Because it’s there”, he replied.

George Mallory (extreme left) and companions

In the sense of adventure sport, that’s a noble intention to have. That you want to do something just because it is possible to do it is awesome, and can inspire others. However, one problem with taking quotes from something like adventure sport, and then translating it to business (it’s rather common to get sportspeople to give “inspirational lectures” to business people) is that the entire context gets lost, and the concept loses relevance.

Take Mallory’s “because it’s there” for example. And think about it in the context of corporate metrics. “Because it’s there” is possibly the worst reason to have a metric in place (or should we say “because it can be measured?”). In fact, if you think about it, a lot of metrics exist simply because it is possible to measure them. And usually, unless there is some strong context to it, the metric itself is meaningless.

For example, let’s say we can measure N features of a particular entity (take N = 4, and the features as length, breadth, height and weight, for example). There will be N! was in which these metrics can be combined, and if you take all possible arithmetic operations, the number of metrics you can produce from these basic N metrics is insane. And you can keep taking differences and products and ratios ad infinitum, so with a small number of measurements, the number of metrics you can produce is infinite (both literally and figuratively). And most of them don’t make sense.

That doesn’t normally dissuade our corporate “measurer”. That something can be measured, that “it’s there”, is sometimes enough reason to measure something. And soon enough, before you know it, Goodhart’s Law would have taken over, and that metric would have become a target for some poor manager somewhere (and of course, soon ceases to be a metric itself). And circular logic starts from there.

That something can be measured, even if it can be measured highly accurately, doesn’t make it a good metric.

So what do we do about it? If you are in a job that requires you to construct or design or make metrics, how can you avoid the “George Mallory trap”?

Long back when I used to take lectures on logical fallacies, I would have this bit on not mistaking correlation for causation. “Abandon your numbers and look for logic”, I would say. “See if the pattern you are looking at makes intuitive sense”.

I guess it is the same for metrics. It is all well to describe a metric using arithmetic. However, can you simply explain it in natural language, and can the listener easily understand what you are saying? And more importantly, does that make intuitive sense?

It might be fashionable nowadays to come up with complicated metrics (I do that all the time), in the hope that it will offer incremental benefit over something simpler, but more often than not the difficulty in understanding it makes the additional benefit moot. It is like machine learning, actually, where sometimes adding features can improve the apparent accuracy of the model, while you’re making it worse by overfitting.

So, remember that lessons from adventure sport don’t translate well to business. “Because it’s there” / “because it can be measured” is absolutely NO REASON to define a metric.

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.

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.

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.

It’s not just about status

Rob Henderson writes that in general, relative to the value they add to their firms, senior employees are underpaid and junior employees are overpaid. This, he reasons, is because senior employees trade off money for status.

Quoting him in full:

Robert Frank suggests the reason for this is that workers would generally prefer to occupy higher-ranked positions in their work groups than lower-ranked ones. They’re forgoing more earnings to hold a higher-status position in their organization.

But this preference for a higher-status position can be satisfied within any given organization.

After all, 50 percent of the positions in any firm must always be in the bottom half.

So the only way some workers can enjoy the pleasure inherent in positions of high status is if others are willing to bear the dissatisfactions associated with low status.

The solution, then, is to pay the low-status workers a bit more than they are worth to get them to stay. The high-status workers, in contrast, accept lower pay for the benefit of their lofty positions.

I’m not sure I agree. Yes, I do agree that higher productivity employees are underpaid and lower productivity employees are overpaid. However, I don’t think status fully explains it. There are also issues of variance and correlation and liquidity (there – I’m talking like a real quant now).

One the variance front – the higher you are in the organisation and the higher your salary is, the more the variance of your contribution to the organisation. For example, if you are being paid $350,000 (the number Henderson hypothetically uses), the actual value you are bringing to your firm might have a mean of $500,000 and a standard deviation of $200,000 (pulling all these numbers out of thin air, while making some sense checks that broadly risk pricing holds).

On the other hand, if you are being paid $35,000, then it is far more likely that the average value you bring to the firm is $40,000 with a standard deviation of $5,000 (again numbers entirely pulled out of thin air). Notice the drastic difference in the coefficient of variation in the two cases.

Putting it another way, the more productive you are, the harder it is for any organisation to put a precise value on your contribution. Henderson might say “you are worth 500K while you earn 350K” but the former is an average number. It is because of the high variance in your “worth” that you are paid far lower than what you are worth on average.

And why does this variance exist? It’s due to correlation.

More so at higher ranked positions (as an aside – my weird career path means that I’ve NEVER been in middle management) the value you can add to a company is tightly coupled with your interactions with your colleagues and peers. As a junior employee your role can be defined well enough that your contributions are stable irrespective of how you work with the others. At senior levels though a very large part of the value you can add is tied to how you work with others and leverage their work in your contributions.

So one way a company can get you to contribute more is to have a good set of peers you like working with, which increases your average contribution to the firm. Rather paradoxically, because you like your peers (assuming peer liking in senior management is two way), the company can get away with paying you a little less than your average worth and you will continue to stick on. If you don’t like working with your colleagues, there is the double whammy that you will add less to the company and you need to be paid more to stick on. And so if you look at people who are actually successful in their jobs at a senior level, they will all appear to be underpaid relative to their peers.

And finally there is liquidity (can I ever theorise about something without bringing this up?). The more senior you go, the less liquid is the market for your job. The number of potential jobs that you want to do, and which might want you, is very very low. And as I’ve explained in the first chapter of my book, when a market is illiquid, the bid-ask spread can be rather high. This means that even holding the value of your contribution to a company constant, there can be a large variation in what you are actually paid. And that is a gain why, on average, senior employees are underpaid.

So yes, there is an element of status. But there are also considerations of variance, correlation and bid-ask. And selection bias (senior employees who are overpaid relative to the value they add don’t last very long in their jobs). And this is why, on average, you can afford to underpay senior employees.

Compensation at the right tail

Yesterday I was reading this article ($) about how Liverpool FC is going about (not) retaining its star forwards Sadio Mane and Mo Salah, who have been key parts of the team that has (almost) “cracked it” in the last 5 seasons.

One of the key ideas in the (paywalled) piece is that Liverpool is more careful about spending on its players than other top contemporary clubs. As Oliver Kay writes:

[…] the Spanish club have the financial strength to operate differently — retaining their superstars well into their 30s and paying them accordingly until they are perceived to have served their purpose, at which point either another A-list star or one of the most coveted youngsters in world football (an Eder Militao, an Eduardo Camavinga, a Vinicius Junior, a Rodrygo and perhaps imminently, an Aurelien Tchouameni) will usually emerge to replace them.

In an ideal world, Liverpool would do something similar with Salah and Mane, just as Manchester City did with Vincent Kompany, Fernandinho, Yaya Toure, David Silva and Sergio Aguero — and as they will surely do with De Bruyne.

But the reality is that the Merseyside club are more restricted. Not dramatically so, but restricted enough for Salah, Mane and their agents to know there is more to be earned elsewhere, and that presents a problem not just when it comes to retaining talent but also when it comes to competing for the signings that might fill the footsteps of today’s heroes.

To go back to fundamentals, earnings in sport follow a power law distribution – a small number of elite players make a large portion of the money. And the deal with the power law is that it is self-similar – you can cut off the distribution at any arbitrary amount, and what remains to the right is still a power law.

So income in football follows a power law. Income in elite football also follows the same power law. The English Premier League is at the far right end of this, but wages there again follow a power law. If you look at really elite players in the league, again it is a (sort of – since number of data points would have become small by now) power law.

What this means is that if you can define “marginal returns to additional skill”, at this far right end of the distribution it can be massive. For example, the article talks about how Salah has been offered a 50% hike (to make him the best paid Liverpool player ever), but that is still short of what some other (perceptibly less skilled) footballers earn.

So how do you go about getting value while operating in this kind of a market? One approach, that Liverpool seems to be playing, is to go Moneyball. “The marginal cost of getting a slightly superior player is massive, so we will operate not so far out at the right tail”, seems to be their strategy.

This means not breaking the bank for any particular player. It means ruthlessly assessing each player’s costs and benefits and acting accordingly (though sometimes it comes across as acting without emotion). For example, James Milner has just got an extension in his contract, but at lower wages to reflect his marginally decreased efficiency as he gets older.

Some of the other elite clubs (Real Madrid, PSG, Manchester City, etc.), on the other hand, believe that the premium for marginal quality is worth it, and so splurge on the elite players (including keeping them till fairly late in their careers even if it costs a lot). The rationale here is that the differences (to the “next level”) might be small, but these differences are sufficient to outperform compared to their peers (for example, Manchester City has won the league by one point over Liverpool twice in the last four seasons).

(Liverpool’s moneyball approach, of course, means that they try to get these “marginal advantages” in other (cheaper) ways, like employing a throw in coach or neuroscience consultants).

This approach is not without risk, of course. At the far right end of the tail, the variance in output can be rather high. Because the marginal cost of small increases in competence is so high, even if a player slightly underperforms, the effective monetary value of this underperformance is massive – you have paid for insanely elite players to win you everything, but they win you nothing.

And the consequences can be disastrous, as FC Barcelona found out last year.

In any case, the story doing the rounds now is that Barcelona want to hire Salah, but given their financial situation, they can’t afford to buy out his contract at Liverpool. So, they are hoping that he will run down his contract and join them on a free transfer next year. Then again, that’s what they had hoped from Gini Wijnaldum two years ago as well. And he’s ended up at PSG, where (to the best of my knowledge) he doesn’t play much.

Structures of professions and returns to experience

I’ve written here a few times about the concept of “returns to experience“. Basically, in some fields such as finance, the “returns to experience” is rather high. Irrespective of what you have studied or where, how long you have continuously been in the industry and what you have been doing has a bigger impact on your performance than your way of thinking or education.

In other domains, returns to experience is far less. After a few years in the profession, you would have learnt all you had to, and working longer in the job will not necessarily make you better at it. And so you see that the average 15 years experience people are not that much better than the average 10 years experience people, and so you see salaries stagnating as careers progress.

While I have spoken about returns to experience, till date, I hadn’t bothered to figure out why returns to experience is a thing in some, and only some, professions. And then I came across this tweetstorm that seeks to explain it.

Now, normally I have a policy of not reading tweetstorms longer than six tweets, but here it was well worth it.

It draws upon a concept called “cognitive flexibility theory”.

Basically, there are two kinds of professions – well-structured and ill-structured. To quickly summarise the tweetstorm, well-structured professions have the same problems again and again, and there are clear patterns. And in these professions, first principles are good to reason out most things, and solve most problems. And so the way you learn it is by learning concepts and theories and solving a few problems.

In ill-structured domains (eg. business or medicine), the concepts are largely the same but the way the concepts manifest in different cases are vastly different. As a consequence, just knowing the theories or fundamentals is not sufficient in being able to understand most cases, each of which is idiosyncratic.

Instead, study in these professions comes from “studying cases”. Business and medicine schools are classic examples of this. The idea with solving lots of cases is NOT that you can see the same patterns in a new case that you see, but that having seen lots of cases, you might be able to reason HOW to approach a new case that comes your way (and the way you approach it is very likely novel).

Picking up from the tweetstorm once again:

 

It is not hard to see that when the problems are ill-structured or “wicked”, the more the cases you have seen in your life, the better placed you are to attack the problem. Naturally, assuming you continue to learn from each incremental case you see, the returns to experience in such professions is high.

In securities trading, for example, the market takes very many forms, and irrespective of what chartists will tell you, patterns seldom repeat. The concepts are the same, however. Hence, you treat each new trade as a “case” and try to learn from it. So returns to experience are high. And so when I tried to reenter the industry after 5 years away, I found it incredibly hard.

Chess, on the other hand, is well-structured. Yes, alpha zero might come and go, but a lot of the general principles simply remain.

Having read this tweetstorm, gobbled a large glass of wine and written this blogpost (so far), I’ve been thinking about my own profession – data science. My sense is that data science is an ill-structured profession where most practitioners pretend it is well-structured. And this is possibly because a significant proportion of practitioners come from academia.

I keep telling people about my first brush with what can now be called data science – I was asked to build a model to forecast demand for air cargo (2006-7). The said demand being both intermittent (one order every few days for a particular flight) and lumpy (a single order could fill up a flight, for example), it was an incredibly wicked problem.

Having had a rather unique career path in this “industry” I have, over the years, been exposed to a large number of unique “cases”. In 2012, I’d set about trying to identify patterns so that I could “productise” some of my work, but the ill-structured nature of problems I was taking up meant this simply wasn’t forthcoming. And I realise (after having read the above-linked tweetstorm) that I continue to learn from cases, and that I’m a much better data scientist than I was a year back, and much much better than I was two years back.

On the other hand, because data science attracts a lot of people from pure science and engineering (classically well-structured fields), you see a lot of people trying to apply overly academic or textbook approaches to problems that they see. As they try to divine problem patterns that don’t really exist, they fail to recognise novel “cases”. And so they don’t really learn from their experience.

Maybe this is why I keep saying that “in data science, years of experience and competence are not correlated”. However, fundamentally, that ought NOT to be the case.

This is also perhaps why a lot of data scientists, irrespective of their years of experience, continue to remain “junior” in their thinking.

PS: The last few paragraphs apply equally well to quantitative finance and economics as well. They are ill-structured professions that some practitioners (thanks to well-structured backgrounds) assume are well-structured.

Management and Verification

For those of you who are new here, my wife and I used to organise “NED Talks” in our home in Bangalore. The first edition happened in 2015 (organised on a whim), and encouraged by its success, we organised 10 more editions until 2019. We have put up snippets of some talks here.

In the second edition of the NED Talks (February 2015), we had a talk by V Vinay (noted computer scientist, former IISc professor, co-inventor of Simputer, co-founder of Strand Life Sciences, Ati Motors, etc. etc.), where he spoke about “computational complexity”.

Now, having studied computer science, “computational complexity” was not a new topic to me, but one thing that Vinay said has stayed with me – it is that verifying an algorithm is far more efficient than actually executing the algorithm.

To take a simple example, factorising a number into prime factors is NP Hard – in other words, it is a really hard problem. However, verifying the prime factorisation of a number is trivial – you can just multiply the factors and see if it gives back the number you started with.

I was thinking about this paradigm the ohter day when I was thinking about professional managers – several times in life I have wondered “how can this person manage this function when he/she has no experience in that function?”. Maybe it is because I had been subjected to two semesters of workshop in the beginning of my engineering, but I have intuitively assumed that you can only manage stuff that you have personally done – especially if it is a non-trivial / specialist role.

But then – if you think about it, at some level, management is basically about “verification”. To see whether you have done your work properly, I don’t need to precisely know how you have done it. All I need to know is whether you have done bullshit – which means, I don’t need to “replicate your algorithm”. I only need to “verify your algorithm”, which computer science tells us can be an order of magnitude simpler than actually building the algorithm.

The corollary of this is that if you have managed X, you need not be good at X, or actually even have done X. All it shows is that you know how to manage X, which can be an order of magnitude simple than actually doing X.

This also (rather belatedly) explains why I have largely been wary of hiring “pure managers” for my team. Unless they have been hands on at their work, I start wondering if they actually know how to do it, or only know how to manage it (and I’m rather hands on, and only hire hands on people).

And yet another corollary is that if you have spent too long just managing teams, you might have gotten so used to just verifying algorithms that you can’t write algorithms any more.

And yet another before I finish – computer science has a lot of lessons to offer life.

 

Accelerated Cookie Licking

For a few months now, I’ve been reading Hardcore Software, a “sub stack book” that’s being written by Steven Sinofsky, about his time at Microsoft. In one of the “episodes” (the book is literally being written in public chapter by chapter, the same way I would go if I were to write another book), he introduces this spectacular concept called “cookie licking“:

Microsoft developed a vocabulary that to this day dominates discussions between alumni. Cookie licking is when one group would lay claim to innovate in an area by simply pre-emptively announcing (via slides in some deck at some meeting) ownership of an initiative.

Cookie licking is one of those concepts where once you’ve seen it you “can’t unsee”. Now that I’m aware of the concept, I keep finding it all over the place. And thinking about it, it is literally all over the place.

And it can happen in many ways. One way is how it happened at Microsoft – where multiple teams might have “been eligible” to work on a particular project, and one team tries to grab the project by “licking the cookie”. It is a pretty common corporate tactic. “Oh, why do you want to work on it when the XXXX team is already working on it?”.

Then, I also see it happening in the startup space. You go to a potential customer or mentor or investor with a certain idea. And then they tell you “why do you want to work on it when XXXX is already doing it?” (usually XXXX is a larger or better known company, but not always). And many a time you fall for the bait, assume that the cookie has been “jooThaafied”, and try to do something else. In a large number of cases, though, the licker of the cookie would have done nothing to consume it apart from the act of licking itself.

I don’t know how exactly to describe cookie licking from a game theoretic perspective, but I can imagine concepts such as “cheap talk”, “game of chicken”, “option value” and “bluffing” coming into play there. The question is if you will fold or call (yay, I made a poker analogy) when you are shown this licked cookie.

And while I was talking about this wonderful concept with someone earlier this evening, I realised that there also exists this concept that I will call “accelerated cookie licking”. Here, you not only lick the proverbial cookie, but also get paid for doing so.

For this, you need to have an independently built reputation (either a successful corporate career, or an exit from an earlier startup, or having been a VC, or some such). And thanks to this reputation built elsewhere, all you need to do is to say that you are licking the cookie, and people will come forward to pay you to do so.

And once you have licked the cookie and raised money for your company, you have an automatic moat – anyone else who wants to eat the same cookie will be told by any potential investors “why do you want to get into this when <this hifunda person with an independently built reputation> is already doing it, and is so well capitalised? Do you really want to take him on?”.

Thinking about it, in poker terms, this is equivalent to bluffing with a really large raise. Even if the opponent knows you are bluffing, it takes a lot for them to be able to call your bluff. And so it is with “accelerated cookie licking”.

Shopping for girls

Maybe this can be my “international women’s day” post.

We went shopping yesterday, after a very long time. We had to shop for all three of us (wife, daughter and I). And we went to a few large stores in Mantri Mall and ended up shopping in the men’s section, women’s section, girls’ section and boys’ section.

You read that right. We shopped in the boys’ section. And no, we didn’t buy anything for gifting. The reason we shopped in the boys’ section was to buy our daughter nice clothes.

Last week, union minister Smriti Irani made this statement somewhere:

The problem is that even if we as parents want to be progressive and want to bring up our daughter without creating gender biases, the world conspires to reinforce gender biases into her. We find that visiting relatives and friends gift her Barbie dolls. There is “pattern recognition” from things she sees around her (last year she shocked us by saying that it was OK for a boy to hit others but not for a girl). Boys her age are not beyond making sexist comments.

But the biggest reinforcer of childhood gender norms, we’ve seen, are clothes shops, and this is a thing we’ve seen both in the UK and in India.

For some reason, clothes manufacturers have collectively decided that the only thing little girls want to wear is bling – every shirt, and skirt, and pair of shorts, and shoes, inevitably have some frills or some bling attached to them. Beyond a point, as we are shopping, it becomes unbearable to even consider such clothes. And we naturally gravitate towards the boys’ section.

Where, for whatever reason, the selection is far more palatable. No-frill (pun intended) T-shirts and comfortable trousers are conspicuous by their abundance. The design on the printed T-shirts are far better (like last year we got her a T-shirt with the nine (clearly a pre-2005 design) planets on it, which she loves wearing). Shoes are comfortable and you can actually run in them.

At pretty much any given point of time in her entire lifetime, the daughter has owned at least half a dozen pieces of clothing that have been shopped from boys’ sections of clothes shops.

There are limitations, of course – that women’s shirts have buttons on the left means that it is easy to identify “cross-dressing” when it comes to polos and button-down shirts. A lot of boys’ clothes are franchise driven, and not the sort of franchises that my wife or I would endorse (there is an overabundance of Disney stuff, such as Marvel, and not enough heavy metal).

And we were worried that once the daughter learnt to read, she would herself start objecting to wearing clothes bought from boys’ section – thankfully, until now at least, that fear hasn’t borne out. She happily selected clothes from boys’ sections yesterday, and even bought a cute T-shirt that said “King of … “.

I really don’t know when children’s clothes designers and merchandisers realise that girls want nice clothes as well – and not just frills and bling. Until then, as long as the daughter approves that is, we’ll be shopping in the boys’ section.