Pertinent Observations

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.

Alcohol, dinner time and sleep

A couple of months back, I presented what I now realise is a piece of bad data analysis. At the outset, there is nothing special about this – I present bad data analysis all the time at work. In fact, I may even argue that as a head of Data Science and BI, I’m entitled to do this. Anyway, this is not about work.

In that piece, I had looked at some of the data I’ve been diligently collecting about myself for over a year, correlated it with the data collected through my Apple Watch, and found a correlation that on days I drank alcohol, my sleeping heart rate average was higher.

And so I had concluded that alcohol is bad for me. Then again, I’m an experimenter so I didn’t let that stop me from having alcohol altogether. In fact, if I look at my data, the frequency of having alcohol actually went up after my previous blog post, though for a very different reason.

However, having written this blog post, every time I drank, I would check my sleeping heart rate the next day. Most days it seemed “normal”. No spike due to the alcohol. I decided it merited more investigation – which I finished yesterday.

First, the anecdotal evidence – what kind of alcohol I have matters. Wine and scotch have very little impact on my sleep or heart rate (last year with my Ultrahuman patch I’d figured that they had very little impact on blood sugar as well). Beer, on the other hand, has a significant (negative) impact on heart rate (I normally don’t drink anything else).

Unfortunately this data point (what kind of alcohol I drank or how much I drank) I don’t capture in my daily log. So it is impossible to analyse it scientifically.

Anecdotally I started noticing another thing – all the big spikes I had reported in my previous blogpost on the topic were on days when I kept drinking (usually with others) and then had dinner very late. Could late dinner be the cause of my elevated heart rate? Again, in the days after my previous blogpost, I would notice that late dinners would lead to elevated sleeping heart rates (even if I hadn’t had alcohol that day). Looking at my nightly heart rate graph, I could see that the heart rate on these days would be elevated in the early part of my sleep.

The good news is this (dinner time) is a data point I regularly capture. So when I finally got down to revisiting the analysis yesterday, I had a LOT of data to work with. I won’t go into the intricacies of the analysis (and all the negative results) here. But here are the key insights.

If I regress my resting heart rate against the binary of whether I had alcohol the previous day, I get a significant regression, with a R^2 of 6.1% (i.e. whether I had alcohol the previous day or not explains 6.1% of the variance in my sleeping heart rate). If I have had alcohol the previous day, my sleeping heart rate is higher by about 2 beats per minute on average.

Call:
lm(formula = HR ~ Alcohol, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.6523 -2.6349 -0.3849  2.0314 17.5477 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  69.4849     0.3843 180.793  < 2e-16 ***
AlcoholYes    2.1674     0.6234   3.477 0.000645 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.957 on 169 degrees of freedom
Multiple R-squared:  0.06676,   Adjusted R-squared:  0.06123 
F-statistic: 12.09 on 1 and 169 DF,  p-value: 0.000645

Then I regressed my resting heart rate on dinner time (expressed in hours) alone. Again a significant regression but with a much higher R^2 of 9.7%. So what time I have dinner explains a lot more of the variance in my resting heart rate than whether I’ve had alcohol. And each hour later I have my dinner, my sleeping heart rate that night goes up by 0.8 bpm.

Call:
lm(formula = HR ~ Dinner, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.6047 -2.4551 -0.0042  2.0453 16.7891 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  54.7719     3.5540  15.411  < 2e-16 ***
Dinner        0.8018     0.1828   4.387 2.02e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.881 on 169 degrees of freedom
Multiple R-squared:  0.1022,    Adjusted R-squared:  0.09693 
F-statistic: 19.25 on 1 and 169 DF,  p-value: 2.017e-05

Finally, for the sake of completeness, I regressed with both. The interesting thing is the adjusted R^2 pretty much added up – giving me > 16% now (so effectively the two (dinner time and alcohol) are uncorrelated). The coefficients are pretty much the same once again.

Call:
lm(formula = HR ~ Dinner, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.6047 -2.4551 -0.0042  2.0453 16.7891 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  54.7719     3.5540  15.411  < 2e-16 ***
Dinner        0.8018     0.1828   4.387 2.02e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.881 on 169 degrees of freedom
Multiple R-squared:  0.1022,    Adjusted R-squared:  0.09693 
F-statistic: 19.25 on 1 and 169 DF,  p-value: 2.017e-05

So the takeaway is simple – alcohol might be okay, but have dinner at my regular time (~ 6pm). Also – if I’m going out drinking, I better finish my dinner and go. And no – having beer won’t work – it is going to be another dinner in itself. So stick to wine or scotch.

I must mention things I analysed against and didn’t find significant – whether I have coffee, what time I sleep, the time gap between dinner time and sleep time – all of these have no impact on my resting heart rate. All that matters is alcohol and when I have dinner.

And the last one is something I should never compromise on.

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?

Aamir Khan and Alcohol Buddies

Over the weekend I was watching Koffee with Karan, the episode featuring Aamir Khan and Kareena Kapoor. It was one of the better episodes in the season, along with the one featuring Ranveer Singh and Alia Bhatt (I did not finish watching any of the others, they were damn boring).

The thing with Koffee With Karan is that it is highly dependent on how interesting the guests are, and not all bollywood stars are equally interesting. Even in this episode, Kareena Kapoor came off as a bit of a bore, refusing to answer most questions, but Aamir Khan was great.

In the early part of the episode, both Kareena and Karan accused Aamir of being “boring”. “You come to a party stand alone and just leave; You catch one or two people and just hang out only with them for the full party”, they said. And then a bit later, one of them (I now forget who – possibly Kareena) said “when I meet you in small groups of 5-6 or less you talk a lot and you are such an interesting person, but why is it that you are such a bore at parties?”

Then Aamir went on to talk about a party at Karan’s house where the music was so loud everyone had to shout to be heard. Nobody was dancing to the music. Nothing was happening. “What is the point of such a party?” he asked.

My friend Hari The Kid has this concept of “alcohol buddies”. These are basically people who you can hang out with only if at least one of you is drunk (there are some extreme cases who are so difficult to hang out with that the only way to do it is for BOTH of you to be drunk). The idea is that if both of you are sober there is nothing really to talk about and you will easily get bored. But hey, these are your friends so you need to hang out with them, and the easiest way of doing so is to convert them into alcohol buddies.

Bringing together this concept and Aamir Khan being “boring”, we can classify people into two kinds – those that are fun when drunk, and those that are fun when sober (some, I think, are both). And people who prefer to have fun when drunk consider the sober sorts boring, and people who prefer to have fun sober think the “alcohol buddies” are boring.

Aamir, for example, appears to be a “have fun when sober” guy, who likes to hang out in small groups and make interesting conversation. Most of Bollywood, however, doesn’t seem to operate that way, hanging out in large groups and not really bothering about conversation.

Yesterday, my wife and I were talking, after an event, about how if you are the sort that likes to hang out in small groups and make conversations, large parties can be rather boring. The problem is that you would have just about started making a nice conversation with someone, when someone else will butt in (hey, this is a party, so this is allowed) and change the topic massively or massively bring down the interest level in the conversation. Every conversation ultimately goes down to its lowest common denominator, leaving you rather frustrated.

And if you are the types who likes large parties and alcohol buddies, small conversations will drain you. You struggle to find things to talk about, and there are only so many people to talk to.

PS: Alcohol and good conversations are not mutually exclusive. Some of my best conversations have happened in very small groups, massively fuelled by alcohol. That said, these have largely been with people I can have great conversations with even when everyone is sober.

Mo Salah and Machine Learning

First of all, I’m damn happy that Mo Salah has renewed his Liverpool contract. With Sadio Mane also leaving, the attack was looking a bit thin (I was distinctly unhappy with the Jota-Mane-Diaz forward line we used in the Champions League final. Lacked cohesion). Nunez is still untested in terms of “leadership”, and without Salah that would’ve left Firmino as the only “attacking leader”.

(non-technical readers can skip the section in italics and still make sense of this post)

Now that this is out of the way, I’m interested in seeing one statistic (for which I’m pretty sure I don’t have the data). For each of the chances that Salah has created, I want to look at the xG (expected goals) and whether he scored or not. And then look at a density plot of xG for both categories (scored or not).

For most players, this is likely to result in two very distinct curves – they are likely to score from a large % of high xG chances, and almost not score at all from low xG chances. For Salah, though, the two density curves are likely to be a lot closer.

What I’m saying is – most strikers score well from easy chances, and fail to score from difficult chances. Salah is not like that. On the one hand, he creates and scores some extraordinary goals out of nothing (low xG). On the other, he tends to miss a lot of seemingly easy chances (high xG).

In fact, it is quite possible to look at a player like Salah, see a few sitters that he has missed (he misses quite a few of them), and think he is a poor forward. And if you look at a small sample of data (or short periods of time) you are likely to come to the same conclusion. Look at the last 3-4 months of the 2021-22 season. The consensus among pundits then was that Salah had become poor (and on Reddit, you could see Liverpool fans arguing that we shouldn’t give him a lucrative contract extension since ‘he has lost it’).

It is well possible that this is exactly the conclusion Jose Mourinho came to back in 2013-14 when he managed Salah at Chelsea (and gave him very few opportunities). The thing with a player like Salah is that he is so unpredictable that it is very possible to see samples and think he is useless.

Of late, I’ve been doing (rather, supervising (and there is no pun intended) ) a lot of machine learning work. A lot of this has to do with binary classification – classifying something as either a 0 or a 1. Data scientists build models, which give out a probability score that the thing is a 1, and then use some (sometimes arbitrary) cutoff to determine whether the thing is a 0 or a 1.

There are a bunch of metrics in data science on how good a model is, and it all comes down to what the model predicted and what “really” happened. And I’ve seen data scientists work super hard to improve on these accuracy measures. What can be done to predict a little bit better? Why is this model only giving me 77% ROC-AUC when for the other problem I was able to get 90%?

The thing is – if the variable you are trying to predict is something like whether Salah will score from a particular chance, your accuracy metric will be really low indeed. Because he is fundamentally unpredictable. It is the same with some of the machine learning stuff – a lot of models are trying to predict something that is fundamentally unpredictable, so there is a limit on how accurate the model will get.

The problem is that you would have come across several problem statements that are much more predictable that you think it is a problem with you (or your model) that you can’t predict better. Pundits (or Jose) would have seen so many strikers who predictably score from good chances that they think Salah is not good.

The solution in these cases is to look at aggregates. Looking for each single prediction will not take us anywhere. Instead, can we predict over a large set of data whether we broadly got it right? In my “research” for this blogpost, I found this.

Last season, on average, Salah scored precisely as many goals as the model would’ve predicted! You might remember stunners like the one against Manchester City at Anfield. So you know where things got averaged out.

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.

40 and growing old

Recently (less than a month ago) my daughter came to me and said “appa, this December you’ll be turning 40. Then you will start becoming old”. Instinctively I got a little upset, and then gave her a little lecture on how aging is a continuous process, and not a discrete one.

That how much I age between 38 and 39, and between 39 and 40, and between 40 and 41 is not so different. You age just a little more each year, but well at a faster rate (aging is nonlinear). And so using an arbitrary cutoff like 40 is not proper, I told her.

But then, thinking about it, I realised that my daughter is not alone in feeling this way. I actually remember, back in the day, calling my father “old” when he turned 40. Maybe it was due to his grey hair. Maybe because most sportspersons retired well before 40 (that said, Martina Navratilova and John McEnroe were both very much active then (1992-93) ).

I don’t think my father gave me a lecture on continuous aging, but I remember him feeling rather annoyed that I had called him “old”.

And then recently an aunt sent a photo to one of my family WhatsApp group. It featured my parents, and they were 42 when the photo was taken. And in that, my father visibly looks old.

Now, we had bought our “family camera” by then (a Canon SnappyQ), but we seldom took photos, so I don’t have too many recollections of what my father looked like at that age. I frequently see family albums from 1990 and 1992 (some vacations), and from much later in the 90s, and there is a discontinuity in how my father looks in both (grey and thinning hair, paunch, etc.).

What this 1995 photo that my aunt sent recently showed me is that by then my father already looked much closer to what he looked like in his late forties and early fifties (he didn’t live much longer beyond that) than what he looked like in his thirties.

I would be lying if I were to say that the picture didn’t scare me. And instinctively I felt a bit better about calling him “old” when he was 40. And I felt a bit better about my daughter saying that “this december I will start becoming old”.

Then again I’m starting to wonder what I can do to not suddenly start aging now. Hair volume and colour I have no control over. General fitness I guess I do. Or maybe not – I have too much of a sweet tooth.

On which point I need to go full bimodal about food – as things stand I end up having “a little” junk food and “a little” alcohol on most days, but in terms of returns in terms of feeling good, I’m not sure if this is the best strategy. Should I go barbell instead?

PS: In most places where I need to submit a photo, I use one that was taken when I was 36, when an old friend was trying to build a career in portrait photography and used me as a guinea pig. I wonder how long I can use that.

Hybrid events

In general I’m short tempered and have a short attention span. One thing that annoys me more than anything else is if someone I’m talking to gets a phone call and moves away from the conversation.

In fact if I think about it more than 90% of my fights with my wife have been triggered by phone calls she gets while she’s taking to me, as a result of which she abandons me for the moment.

I’m writing this from a “hybrid event”. My wife is giving a talk at the Goa project, and this event is happening both online and offline. I’m offline, as are some twenty others. Another dozen people are online.

As an offline audience member I’m finding this damn annoying. The most annoying thing is that the moderator is online. And the way the event has been set up, online seems to take precedence over offline. The online moderator can interrupt. He can ask a speaker to repeat the last five minutes of her talk. And as a live audience member I find this insanely irritating.

The other problem with hybrid events is there is no scope for banter. Small offline events with 20 people can be rather intimate and have a high scope for banter. Like I cracked a wisecrack a few minutes back. People around me seemed to like it. And then one of the local moderators had to repeat the wisecrack to the zoom audience.

I wrote until this point in the first of the three talks. After that I decided writing this blog is not enough and protested (a tad too) loudly that the hybrid format was boring.

Then someone figured a simple nudge. They muted the zoom while talks were on. The remote people couldn’t interrupt as much as they used to. And the event became so much better.

So I guess, like everything else in design, its just about the defaults. Then again I don’t know if the online people were happy with the new default. Not that I care.

Though: the quality of CP is far superior from people in the room than from those who can hide behind a screen without camera on

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.