Category: science

Slow deaths and sudden deaths

My parents both died slow deaths. My father spent the last three months of his life in hospital, of which the last month was in intensive care on ventilator support. He had been rendered immobile, and when the ventilator tube and food pipe went in, there was absolutely no way in which he could communicate to us during the brief times we were allowed to meet him.

My mother’s was a different story, but on a shorter time scale. She spent her last month in hospital, with the last ten days in intensive care and on ventilator, again what I think was fairly painful existence for her, living in a fairly isolated and airconditioned room, not being able to communicate with anyone, with all sorts of tubes and measuring devices stuck all over the body.

In hindsight, I regret my decision to allow them to be put on ventilator. I feel guilty for having extended their lives in a way which was both painful to them and where there was little meaning, for they lived cut off, and unable to communicate (and in both cases, had I thought rationally, I would’ve known that there was little chance the time on ventilator would allow them to recover). The only upside to this was that it gave me time to prepare. That it gave me time to prepare for their impending passing,

People who attended either of my parents’ funerals might have been surprised, a bit shocked even, to see that I was quite composed and in control of things. I wouldn’t be wrong if a number of them thought I was a heartless emotionless wretch. The reason I behaved thus was because it was only an incremental change as far as my mental preparedness was concerned. Till the day prior to both my parents’ deaths, I knew that the chances that they would recover and get back home was minimal. Delta. Epsilon. The death, normally a “discrete event” had only pushed this chance to zero, not a big change in probability.

I was thinking about all this two nights back when my grandfather-in-law passed away, once again after a prolonged illness (he refused to be admitted to hospital or be put on life support so in a way he was spared of time on ventilator), but his condition had deteriorated steadily enough for us to know that he would be gone soon. Several family members reacted quite badly, but several others were quite brave and acted bravely. The slow death was the reason for this, I thought.

There are too many factors that affect death, and no one can choose either the time or mode or pace of dying, but I have been thinking if slow deaths are better than sudden deaths or vice versa. The upside of a sudden death is that there is little suffering on the part of the dyer, but the discrete nature of the change (probability that the person would be no more the next day would jump suddenly from close to zero to one) would imply a huge shock for family members and friends, which they would take considerable time and effort to come out of.

A slow death, on the other hand, is extremely painful for the dyer, while it gives time to the family members to come to terms with the reality. Here, too, of course there is usually one big discrete step involved (like that Monday night when in the matter of less than an hour, my mother went from happily chatting with me to gasping for breath so uncontrollably that they had to immediately wheel her to intensive care and a ventilator; or that Thursday morning when my father suddenly realized he had lost all the power in his legs and couldn’t stand on his own), so it is more like a time-shifting of pain (for relatives/friends) rather than the pain being amortized over a number of days.

Once again, there are no clear answers to this question about which mode of death is better, but ever since I saw my father spend his last three months in hospital I’ve believed that sudden deaths are superior. I’ve found myself reacting to other people’s sudden deaths saying “good for them they went without suffering”. Again, no one really has control about how or when they’ll die. It’s only a question about what to hope for in life.

The Trouble with Mental Illness

  • The “patient” has an incentive to overestimate the extent of his illness, since he can “get away” with certain things by claiming to be more sick than he is
  • People around the patient have an incentive to underestimate the extent of illness. They think the person is claiming illness only to extract sympathy and get away with things that would be otherwise not permissible
  • The second point here leads to internal conflict in the patient, as he can’t express himself fully (since others tend to underestimate). Feelings of self-doubt begin to creep in, and only make the problem worse
  • There are no laboratory tests in order to detect most kinds of “mental illness”. Diagnosis is “clinical” (eg. if 8 out of following 10 check boxes are ticked, patient suffers from XYZ). This leads to errors in diagnosis
  • The method of diagnosis also leads to a lot of people in believing that psychiatry is unscientific and some reduce it to quackery. So there is little the medical profession can do to help either the patient or people around him
  • That diagnosis is subjective means patients have incentive to claim they’re under-diagnosed and people around are incentivized to say they’re over-diagnosed
  • I don’t think the effect of a lot of medicines to cure mental illness have been studied very rigorously. There are various side effects (some cause you to sleep more, others cause you to sleep less, some cause impotence, others increase your mojo, and so on ), and these medicines are slow to act making it tough to figure out their efficacy.
  • There is a sort of stigma associated with admitting to mental illness. Even if one were to “come out” to people close to him/her, those people might dissuade the patient from “coming out” to a larger section of people
  • If you were to be brave and admit to mental illness, people are likely to regard you as a loser, and someone who gives up too soon. That’s the last thing you need! And again, the underestimate-overestimate bias kicks in.
  • Some recent studies, though, show a positive correlation between mental illness and leadership and being able to see the big picture. So there is some hope, at least.

In search of uncertainty

Back when I was in school, I was a math stud. At least people around me thought so. I knew I wanted to pursue a career in science, and that in part led me to taking science in class XI, and subsequently writing JEE which led to the path I ultimately took. Around the same time (when I was in high school), I started playing chess competitively. I was quite good at it, and I knew that with more effort I could make it big in the game. But then, that never happened, and given that I would fall sick after every tournament, I retired.

It was in 2002, I think, that I was introduced to contract bridge, and I took an instant liking for it. All the strategising and brainwork of chess would be involved once again, and I knew I’d get pretty good at this game, too. But there was one fundamental difference which made bridge so much more exciting – the starting position was randomized (I’m not making a case for Fischer Chess here, mind you). The randomization of starting positions meant that you could play an innumerable number of “hands” with the same set of people without ever getting bored. I simply loved it.

It was around that time that I started losing interest in math and other hard sciences. They had gotten to the point where they were too obscure, and boring, I thought, and that to make an impact in them, I wanted to move towards something less precise, and hard. That was probably what led me to do an MBA. And during the course of my MBA I discovered my interest in economics and social sciences, but am yet to do anything significant on that front, though, apart from the odd blog here or there.

I think what drove me from what I had thought is my topic of interest to what I think now it is is the nature of open problems. In hard sciences, where a lot of things are “known” it’s getting really hard to do anything of substance unless you get really deep in, into the territories of obscurity. In “fuzzy sciences”, on the other hand, nothing too much is “known”, and there will always be scope for doing good interesting work without it getting too obscure.

Similarly, finance, I thought, being a people-driven subject (the price of a stock is what a large set of people think its price is, there are no better models) will have lots of uncertainty, and scope to make assumptions, and thus scope to do good work without getting too obscure. But what I find is that given the influx of hard science grads in Wall Street over the last three decades, most of the large organizations are filled with people who simply choose to ignore the uncertainty and “interestingness” and instead try and solve deterministic problems based on models that they think completely represents the market.

And this has resulted in you having to do stuff that is really obscure and deep (like in the hard sciences) even in a non-deterministic field such as finance, simply because it’s populated by people from hard science background, and it takes way too much in order to go against the grain.

PS: Nice article by Tim Harford on why we can’t have any Da Vincis today. Broadly related to this, mostly on scientific research.

Models

This is my first ever handwritten post. Wrote this using a Natraj 621 pencil in a notebook while involved in an otherwise painful activity for which I thankfully didn’t have to pay much attention to. I’m now typing it out verbatim from what I’d written. There might be inaccuracies because I have a lousy handwriting. I begin

People like models. People like models because it gives them a feeling of being in control. When you observe a completely random phenomenon, financial or otherwise, it causes a feeling of unease. You feel uncomfortable that there is something that is beyond the realm of your understanding, which is inherently uncontrollable. And so, in order to get a better handle of what is happening, you resort to a model.

The basic feature of models is that they need not be exact. They need not be precise. They are basically a broad representation of what is actually happening, in a form that is easily understood. As I explained above, the objective is to describe and understand something that we weren’t able to fundamentally comprehend.

All this is okay but the problem starts when we ignore the assumptions that were made while building the model, and instead treat the model as completely representative of the phenomenon it is supposed to represent. While this may allow us to build on these models using easily tractable and precise mathematics, what this leads to is that a lot of the information that went into the initial formulation is lost.

Mathematicians are known for their affinity towards precision and rigour. They like to have things precisely defined, and measurable. You are likely to find them going into a tizzy when faced with something “grey”, or something not precisely measurable. Faced with a problem, the first thing the mathematician will want to do is to define it precisely, and eliminate as much of the greyness as possible. What they ideally like is a model.

From the point of view of the mathematician, with his fondness for precision, it makes complete sense to assume that the model is precise and complete. This allows them to bringing all their beautiful math without dealing with ugly “greyness”. Actual phenomena are now irrelevant.The model reigns supreme.

Now you can imagine what happens when you put a bunch of mathematically minded people on this kind of a problem. And maybe even create an organization full of them. I guess it is not hard to guess what happens here – with a bunch of similar thinking people, their thinking becomes the orthodoxy. Their thinking becomes fact. Models reign supreme. The actual phenomenon becomes a four-letter word. And this kind of thinking gets propagated.

Soon the people fail to  see beyond the models. They refuse to accept that the phenomenon cannot obey their models. The model, they think, should drive the phenomenon, rather than the other way around. The tails wagging the dog, basically.

I’m not going into the specifics here, but this might give you an idea as to why the financial crisis happened. This might give you an insight into why obvious mistakes were made, even when the incentives were loaded in favour of the bankers getting it right. This might give you an insight as to why internal models in Moody’s even assumed that housing prices can never decrease.

I think there is a lot more that can be explained due to this love for models and ignorance of phenomena. I’ll leave them as an exercise to the reader.

Apart from commenting about the content of this post, I also want your feedback on how I write when I write with pencil-on-paper, rather than on a computer.

 


The Impact of Wall Street on Grad School

I don’t need to be an insider to tell you that Wall Street employs lots of PhDs. PhDs of various denominations, but mostly those with backgrounds in Math, Physics and Engineering are employed by various Wall Street firms by the thousand. I don’t think too many of them exactly work on the kind of stuff that they were doing in grad school, but certain general skills that they pick up and hone through their multiple years in grad school are found extremely useful by banks.

So while scores of older scientists and economists and policymakers lament the “loss” of so many bright minds to science, has anyone at all considered the reverse possibility? Of the impact that Wall Street has had on grad schools in the US?

One thing you need to face is that there are not a lot of academic jobs going around. The number of people finishing with PhDs each year is far more than the number of academic jobs that open up each year. I’m mostly talking about “assistant professor” kind of jobs here, and assuming that becoming a post-doc just delays your entry into the job market rather than removing you from the market altogether.

In certain fields such as engineering, there are plenty of jobs in the industry for PhDs who don’t get academic jobs, for whatever reason. Given this, it is “cheaper” to do a PhD in these subjects, since it is very likely that you will end up with a “good job”. Hence, there is more incentive to do a PhD in subjects like this, and universities usually never have a problem in finding suitable candidates for their PhD programs. However, there is no such cushion in the pure sciences (math/physics). There are few “industry employers” who take on the slack after all the academic positions have been filled up. And that is where Wall Street steps in.

The presence of Wall street jobs offers a good backstop to potential Math and Physics PhD candidates. If they aren’t able to do the research that they so cherish, they needn’t despair since there exists a career path which will enable them to make lots of money. And knowing the existence of this career option means more people will be willing to take the risk of doing a PhD in these subjects (since the worst case isn’t so bad now). Which in turn enhances the candidate pool available to grad schools.

So even if you were to believe that complex derivatives are financial “weapons of mass destruction”, there is reason for them to exist, to encourage the financial sector to pick up PhDs. For if PhDs were kept out of these jobs, it is real academic research in “real subjects” such as the pure sciences that will suffer. By picking up PhDs in large numbers, the financial sector is making its own little contribution to research in pure sciences.