Channelling

I’m writing this five minutes after making my wife’s “coffee decoction” using the Bialetti Moka pot. I don’t like chicory coffee early in the morning, and I’m trying to not have coffee soon after I wake up, so I haven’t made mine yet.

While I was filling the coffee into the Moka Pot, I was thinking of the concept of channelling. Basically, if you try to pack the moka pot too tight with coffee powder, then the steam (that goes through the beans, thus extracting the caffeine) takes the easy way out – it tries to create a coffee-less channel to pass through, rather than do the hard work of extracting coffee as it passes through the layer of coffee.

I’m talking about steam here – water vapour, to be precise. It is as lifeless as it could get. It is the gaseous form of a colourless odourless shapeless liquid. Yet, it shows the seeming “intelligence” of taking the easy way out. Fundamentally this is just physics.

This is not an isolated case. Last week, at work, I was wondering why some algorithm was returning a “negative cost” (I’m using local search for that, and after a few iterations, I found that the algorithm is rapidly taking the cost – which is supposed to be strictly positive – into deep negative territory). Upon careful investigation (thankfully it didn’t take too long), it transpired that there was a penalty cost which increased non-linearly with some parameter. And the algo had “figured” that if this parameter went really high, the penalty cost would go negative (basically I hadn’t done a good job of defining the penalty well). And so would take this channel.

Again, this algorithm has none of the supposedly scary “AI” or “ML” in it. It is a good old rule-based system, where I’ve defined all the parameters and only the hard work of finding the optimal solution is left to the algo. And yet, it “channelled”.

Basically, you don’t need to have got a good reason for taking the easy way out now. It is not even human, or “animal” to do that – it is simply a physical fact. When there exists an easier path, you simply take that – whether you are an “AI” or an algorithm or just steam!

I’ll leave you with this algo that decided to recognise sheep by looking for meadows (this is rather old stuff).

Beckerian Disciplines

When Gary Becker was awarded the “Nobel Prize” (or whatever its official name is) for Economics, the award didn’t cite any single work of his. Instead, as Justin Wolfers wrote in his obituary,

He was motivated by the belief that economics, taken seriously, could improve the human condition. He founded so many new fields of inquiry that the Nobel committee was forced to veer from the policy of awarding the prize for a specific piece of work, lauding him instead for “having extended the domain of microeconomic analysis to a wide range of human behavior and interaction, including nonmarket behavior.”

Or as Matthew Yglesias put it in his obituary of Becker,

Becker is known not so much for one empirical finding or theoretical conjecture, as for a broad meta-insight that he applied in several areas and that is now so broadly used that many people probably don’t realize that it was invented relatively recently.

Becker’s idea, in essence, was that the basic toolkit of economic modeling could be applied to a wide range of issues beyond the narrow realm of explicitly “economic” behavior. Though many of Becker’s specific claims remain controversial or superseded by subsequent literature, the idea of exploring everyday life through a broadly economic lens has been enormously influential in the economics profession and has altered how other social sciences approach their issues

Essentially Becker sort of pioneered the idea of using economic reasoning for fields outside traditional economics. It wasn’t always popular – for example, his use of economics methods in sociology was controversial, and “traditional sociologists” didn’t like the encroachment into their field.

However, Becker’s ideas endured. It is common nowadays for economists to explore ideas traditionally considered outside the boundaries of “standard economics”.

I think this goes well beyond economics. I think there are several other fields that are prone to “go out of syllabus” – where concepts are generic enough that they can be applied to areas traditionally outside the fields.

One obvious candidate is mathematics – most mathematical problems come from “real life”, and only the purest of mathematicians don’t include an application from “real life” (well outside of mathematics) while writing a mathematical paper. Immediately coming to mind is the famous “Hall’s Marriage Theorem” from Graph Theory.

Speaking of Graph Theory, Computer Science is another candidate (especially the area of algorithms, which I sort of specialised in during my undergrad).  I remember being thoroughly annoyed that papers and theses that would start so interestingly with a real-life problem would soon involve into inscrutable maths by the time you got to the second section. I remember my B.Tech. project (this was taken rather seriously at IIT Madras) being about what I had described as a “Party Hall Problem” (this was in Online Algorithms).

Rather surprisingly (to me), another area whose practitioners are fond of encroaching into other subjects is physics. This old XKCD sums it up

Complex Systems (do you know most complex systems scientists are physicists by training?) is another such field. There are more.

In any case, assuming no one else has done this already, I hereby christen all these fields (whose practitioners are prone to venturing into “out of syllabus matters”) as “Beckerian Disciplines” in honour of Gary Becker (OK I have a economics bias but I’m pretty sure there have been scientists well before Becker who have done this).

And then you have what I now call as “anti-Beckerian Disciplines” – areas that get pissed off that people from other fields are “invading their territory”. In Becker’s own case, the anti-Beckerian Discipline was Sociology.

When all university departments talk about “interdisciplinary research” what they really need is Beckerians. People who are able and willing to step out of the comfort zones of their own disciplines to lend a fresh pair of eyes (and a fresh perspective) to other disciplines.

The problem with this is that they can encounter an anti-Beckerian response from people trying to defend their own turf from “outside invasion”. This doesn’t help the cause of science (or research of any kind) but in general (well, a LOT of exceptions exist), academics can be a prickly and insecure bunch forever playing zero-sum status games.

With the covid-19 virus crisis, one set of anti-Beckerians who have emerged is epidemiologists. Epidemiology is a nice discipline in that it can be studied using graph theory, non-linear dynamics or (as I did earlier today) simple Bayesian maths or so many other frameworks that don’t need a degree in biology or medicine.

And epidemiologists are not happy (I’m not talking about my tweet specifically but this is a more general comment) that their turf is being invaded upon. “Listen to the experts”, they are saying, with the assumption that the experts in question here are them. People are resorting to credentialism. They’re adding “, PhD” to their names on twitter (a particularly shady credentialist practice IMHO). Questioning credentials and locus standi of people producing interesting analysis.

Enough of this rant. Since you’ve come all the way, I leave you with this particularly awesome blogpost by Tyler Cowen, who is a particularly Beckerian economist, about epidemiologists. Sample this:

Now, to close, I have a few rude questions that nobody else seems willing to ask, and I genuinely do not know the answers to these:

a. As a class of scientists, how much are epidemiologists paid?  Is good or bad news better for their salaries?

b. How smart are they?  What are their average GRE scores?

c. Are they hired into thick, liquid academic and institutional markets?  And how meritocratic are those markets?

d. What is their overall track record on predictions, whether before or during this crisis?

e. On average, what is the political orientation of epidemiologists?  And compared to other academics?  Which social welfare function do they use when they make non-trivial recommendations?

On Being a Geek

I’ve always been a “topper types”. I started topping class when I was in first standard (and no, they didn’t announce ranks before that), and as if that wasn’t enough, my parents made sure that all relatives, and all teachers in school knew about my superhuman arithmetic skills. And as if even this wasn’t enough, I became the first guy in my class to wear spectacles. In a few years’ time, I went on to represent my school in supposedly intellectual pursuits such as quizzing and chess. I had been consigned to living life as a geek.

There were several occasions when I wasn’t really the topper; wasn’t even close to being a topper. However, something or the other ensured that I managed to maintain that geeky aura. In school, and at IIMB, I was supposed to be really good at math, and that made me geeky. Things were differnet at IIT – since a number of my classmates who trumped me in acads were also better than me at other geeky things. However, I think the fact that I was studying CompSci made me feel geeky, and I never lost any opportunity to show off my geekiness.

In this context, the last two years were quire awkward, as I was in a couple of non-geeky jobs. For the first time in almost twenty years, I had to go out of my way to demonstrate my geekiness, and given that those jobs didn’t need me to be a geek, things didn’t go quite well. I used to try and shove in lines into my conversation such as “we used to play chess in the classroom at IIT. since we couldn’t carry in chessboards, we used to imagine a board and play on that”.

It was very awkward. Thinking back, maybe that was one of the major contributing reasons to my not being too happy in the jobs. I wasn’t able to play my natural game. I had to invent a new me that would go to work daily. And it wasn’t just about the geekiness factor, but this was one of the important reasons, I believe.

Now, working as a strategy guy in a quant hedge fund, I feel I have every right to be geeky, and am well and truly back in form. I lose no opportunity to crack geeky jokes. I try to bring in analogies from various geeky fields I’ve been acquainted with – math, computer science, finance, and even physics. And I don’t mind making things complicated just so that I can slip in that geeky analogy that I think is “beautiful” and “elegant”.

Two days back, i was talking to Baada on the phone, and I smelt an opportunity to crack a geeky joke. We were discussing football while watching Liverpool play Chelski. And then suddenly I asked him if he knew the concept of inversion in geometry. When he replied in the negative, I spetn the next ten minutes explaining the concept to him, all so that I could slip in that one little geeky joke.

Beware of me, I would say.