Category: randomness

Why authors need convertible debt

At the end of a recent blogpost, I had referred to a piece by Matthew Yglesias where he refers to author advances as “convertible debt”.

 An advance is bundled with a royalty agreement in which a majority of the sales revenue is allocated to someone other than the author of the book. In its role as venture capitalist, the publisher is effectively issuing what’s called convertible debt in corporate finance circles — a risky loan that becomes an ownership stake in the project if it succeeds.

While I agreed with Yglesias’s piece when I had first read it (around the time it was published), I’m not so sure I agree with it now. As I approache the “home stretch” with the first draft of my first book (it’s a popular economics book on liquidity and market design), I’m plunged in self-doubt every time I sit down to write it.

The problem with writing a book is that the author needs to work for months together without any feedback whatsoever. It is occasionally possible for the author to take feedback from a few family members and friends. While such feedback is sometimes useful, the problem is that the people providing the feedback represent only a very tiny fraction of the book’s overall client base (I hope lots of people will read my book once it gets published).

So there is always a reasonable chance that months of effort might result in an absolute dud, implying zero returns. It is also mildly probable, of course, that these months of efforts might result in a blockbuster, but while you are producing it you have no clue which way it will turn out.

This can create serious motivation issues, and on the occasional bad day at work you might be tempted to abandon the project altogether and get back to doing something more predictable. You can have some internal deadlines but they need not be binding (like I’d set the deadline to finish my first draft as the day I went for my vacation to al-Andalus. However I’ve already reneged on that and given myself a further fifteen days). Unless there is extremely strong internal motivation, it is hard to sustain your effort.

This is where convertible debt, in the form of a publisher’s advance, can help. On the upside, the advance will guarantee you some returns (however meagre) from the project. On the downside, the advance from the publisher comes with a deadline, which acts as a Damocles’s sword to ensure you are motivated and finish your book on time.

As a first time author however, whose only published work so far has been 2000 odd posts on this blog and a 100 odd articles for Mint, I didn’t give myself too good a chance of snagging convertible debt, and so I soldier on, hoping my book turns out well.

Soon, once I finish the draft, I hope to start taking the book to publishers. If any of you has leads on who to approach, do let me know. It’s a non-fiction (popular economics) book with an Indian core but written for a global audience. For now I’m ruling out self-publication, since I’m looking at this book as providing me far more than royalty revenues and can do with some publisher validation.

Also, that might help me get some convertible debt for my next book!

Sudden death and the discount rate

It’s six years today since my mother passed away. She died in the early hours of Friday, 23rd October 2009 following a rather brief illness. The official death summary that the hospital issued reported the cause of her death as “sepsis”. She only officially died on the 23rd. As far as I’m concerned, I’d lost her two Mondays earlier, on 12th October 2009, when she complained of extreme breathing difficulty and was put on ventilator in the ICU.

Looking back (this year’s calendar is identical to that of 2009, so memories of that year have been coming back rather strongly this year), I realise that the suddenness with which it all happened have left me with a deep sense of paranoia, which can be described in financial terms as a “high discount rate”.

Having moved back from Gurgaon in June of that year, my mother and I had settled down in a rented house in Tata Silk Farm (she didn’t want to go back to our own house in Kathriguppe where we’d lived until 2008). She had settled well, and living not far from her sisters, had developed a nice routine. There were certain temples she would visit on certain days of the week, for example.

And then suddenly one day in September she complained of breathing trouble (she took thirty minutes to walk from our then house to my aunt’s house, which is only a ten minute walk away). Initial medical tests revealed nothing. More tests were prescribed, as her breathing got worse. There was no diagnosis yet.

She started seeing specialists – a pulmonologist and her cardiovascular surgeon (she had had trouble with some veins for a few years). More tests. Things getting worse. And before we knew it, she was in hospital – for a “routine three day admission” for an invasive test. The test got postponed, and the surgery finally done a week later. She got out of the ICU and remained there for hardly two days before she complained of insane breathing trouble and had to be put on ventilator – the only purpose the 12 days she spent on that served was to help me prepare for her impending death.

In all, it took less than a month end to end – from initially complaining of breathlessness to going on ventilator. What seemed to be a harmless problem leading to death.

I realise it’s caused insane paranoia in me which I’m yet to come out of. Every time I, or a relative or a friend, show minor signs of sickness, I start fearing the worst. I stop thinking about the symptoms in a Bayesian fashion – by looking at prior probabilities of the various illnesses that could be causing them – and overweight the more morbid causes of the symptoms. And that adds paranoia and anxiety to what I’m already suffering from.

Like two weeks back I had a little trouble breathing, but no apparent cold. It wasn’t something that happens to me normally. A quick Bayesian analysis would have revealed that the most probable cause is a sinus (which it was), but I spent half a day wondering what had become of me before I applied Vicks and quickly recovered. When my wife told me a week after she reached the US that she had got a high fever, I got paranoid again before realising that the most probable cause was a flu caused due to a change of seasons (which it was!).

Another consequence of my mother’s rather sudden death in 2009 (and my father’s death in 2007, though that was by no means sudden, as he had been diagnosed with cancer two years earlier) was that I suddenly stopped being able to make plans. I started overestimating the odds of something drastic happening, and planning didn’t make sense in such scenarios, I reasoned. As a consequence I became extremely short-term in my thinking, and couldn’t see beyond a few days away.

There have been several occasions where I’ve left a decision (such as booking tickets for something, for example) until it has been too late. There have been times when I’ve optimised for too short a term in some of my decisions, effectively jacking up my “discount rate”.

I’d written a while earlier about how in case of rare events, the probabilities we observe can be much higher than actual probabilities, and how that can lead to impaired decision-making. Thinking about it now, I’ve seen that playing out in my life over the last six years.  And it will take a considerable amount of effort to become more rational (i.e. use the “true” rather than “observed” probabilities) in these things.

Sweet nothings

The problem with a long-distance relationship is that there is only one way you can spend time with each other – by talking to each other. Of course there are many ways of doing this now, given the advances in communication technologies – text chat, voice chat, video chat, …

But as you can see, the common thing to all this is chat. And to chat, you need things to chat about. Which means that the amount of time you spend with each other is limited by how interesting your days have been and what you have to say to each other.

It is a rather common occurrence that you want to spend more time with each other than what what you have to say to each other dictates, and in such times, you have a few options.

You could talk about everyday happenings like the weather or some news or sports (extremely unlikely, though, that a couple will spend time discussing sports). Another popular option is repeatedly saying lovey-dovey things such as “I love you” or “I miss you”, so that you prolong the conversation. You could even tell each other the mundane details of your lives that day, like “and then I got into bus 37 and … “. Else you could talk about your respective mental states, like “I got so psyched out while writing this algorithm this morning” or “I felt so happy I answered that question in class”. And so forth.

While all of these make for excellent fillers, and help you spend more time with each other by way of creating things to talk about, the problem is that they seldom add value (except perhaps for small doses of lovey-dovey talk). And when you over-indulge in filler conversations, they end up subtracting value from your conversation by overemphasising attention on the mundane at the cost of talk with positive information content.

Contrast this with a “normal” relationship where there are so many other ways of spending time with each other other than talking – I don’t need to enumerate them here.

In other words, when you do long distance, you only have access to a small subset of your relationship. Which makes long distance hard. And occasionally makes you go mad.

PS: tools such as FaceTime allow you to “virtually be with each other” by dialling and then going about with your own lives. But you are still stuck to the fixed point which is the computer, and that means whatever life you go about is unreal, and that can further add to the pressure! And hence subtract value.

 

VC Funding, Ratchets and Optionality

A bug (some call it a “feature”) of taking money from VCs is that it comes in with short optionality. VCs try to protect their investments by introducing “ratchets” which protect them against the reduction in valuation of the investee in later rounds.

As you might expect, valuation guru Aswath Damodaran has a nice post out on how to value these ratchets, and how to figure out a company’s “true valuation” after accounting for the ratchets.

A few months back, I’d mentioned only half in jest that I want to get into the business of advising startups on optionality and helping them value investment offers rationally after pricing in the ratchets, so that their “true valuation” gets maximised.

In a conversation yesterday, however, I figured that this wouldn’t be a great business, and startups wouldn’t want to hire someone like me for valuing the optionality in VC investments. In fact, they wouldn’t want to hire anyone for valuing this optionality.

There are two reasons for this. Firstly, startups want to show the highest valuation possible, even if it comes embedded with a short put option. A better valuation gives them bigger press, which has some advertising effect for sales, hiring and future valuations. A larger number always has a larger impact than a smaller number.

Then, startup founders tend to be an incredibly optimistic bunch of people, who are especially bullish about their own company. If they don’t believe enough in the possible success of their idea, they wouldn’t be running their company. As a consequence, they tend to overestimate the probability of their success and underestimate the probability of even a small decrease in future valuation. In fact, the probability of them estimating the latter probability at zero is non-zero.

So as the founders see it, the probability of these put options coming into the money is near-zero. It’s almost like they’re playing a Queen of Hearts strategy. The implicit option premium they get as part of their valuation they see as “free money”, and want to grab it. The strikes and structures don’t matter.

I have no advice left to offer them. But I have some advice for you – given that startups hardly care about optionality, make use of it and write yourself a fat put option in the investment you make. But then this is an illiquid market and there is reputation risk of your option expiring in the money. So tough one there!

Optimal risk sharing

The wife moved to Ann Arbor over the weekend, where she will be spending three months. She took an Air France flight (AF191) in the wee hours of Sunday morning, and then switched to a Delta flight at the legendary Charles de Gaulle. I must mention upfront that she seems to have had a peaceful journey.

Except that people following the same schedule exactly twenty four hours earlier would not have. AF191 that departed from Bangalore i n the wee hours of Saturday morning returned to Bangalore after a bomb scare. The flight was subsequently cancelled.

There are many risks to flying. Schedules nowadays are packed so closely that your flight might be delayed. Occasionally it might be cancelled even, sometimes without a good reason. A delay might sometimes mean that you miss your connecting flight.

The question is who bears the risk on this one. If I’m booked on a flight that gets cancelled or delayed (because of which I miss my connection), whose responsibility is it that I’m transported to my destination? There are three possibilities – the passenger himself, the airline and an external insurer. The question is which of these is most optimal.

The traditional model in aviation as I understand it is that it is the airline’s responsibility. While this makes sense because a large number of delays/cancellations are on account of faults on account of the airline, even when the delay is not due to the airline’s fault, the airline is best placed in terms of mitigating the risk.

Leaving the risk on the passenger has the advantage that he can choose his own risk profile. If you are flexible about your trip, you might choose to go without insurance, and take the hit yourself. If you’re a frequent flyer, then the “insurance cost” thus saved will compensate for the occasional delay. Yet, the problem with this kind of a model is that people tend to underestimate the risks, and will more often than not not insure, and get hit badly when the delay happens.

Which brings us to the final absorber of risk – the insurance company. I’d purchased “travel insurance” for a recent trip, and there was a component on account of delayed or missed flights. If my flight was delayed by a certain amount of time, my insurer would pay me a fixed amount of money.

While this financial hedging is good, it may not adequately represent the costs of making a new booking (including the hassles) when my flight is delayed or cancelled. So this is not a workable solution at scale.

Another solution is for the insurer to guarantee that you will reach your destination by a certain time in case your flight gets delayed or cancelled. This might work out to be more expensive than a fixed cash payout but this removes the cost and hassle of figuring out the next best alternative on the part of the customer. The problem, however, is correlation. Insurance works when people’s risks are uncorrelated or negatively correlated. Here they are positively correlated – all passengers on Saturday’s AF191 to Paris were affected similarly, and this pushes up the cost for the insurer to rebook people.

Unless they tie up with the airline itself! If they reach an agreement with the airline such that the airline commits to transport the stranded passengers, then this “positive correlation” I mentioned earlier will be taken care of. Seems workable, right? Except that what is being insured here is the risk that the airline abandoned in favour of the passenger, who insured against it from an insurer, who reinsured it with the airliner! Can we just cut out the middle men?

From this rather unscientific argument above, it looks like airlines are best placed to insure passengers against disrupted flight schedules. Back in the days of regulated air fares where competition had to be “on service”, airlines would take responsibility. This might have disappeared with the move towards unbundling over the last 2-3 decades. For good reason – insuring a schedule results in an additional (albeit hidden) cost, and getting rid of it can result in cheaper (base) fares.

Yet, given that airlines are best placed to insure schedules, we need a solution. Maybe they can charge a premium for insuring schedules apart from the base fares? Or would they argue that the current “unrestricted fares” are such insured fares (implying the premium is rather high)?

Short of  government mandated regulation, what is the best way for allocating the risk of disrupted flight schedules, and pricing it appropriately?

Tailpiece: A decade ago, our valuation professor (at IIM Bangalore) had told us that “risk cannot be eliminated. It can only be mitigaged by selling it to someone who can handle it better”.

The toin coss method

My mother had an interesting way to deal with dilemmas for which she had no solution – she would just toss a coin. She had only one rule for the “game” – that once she had decided to toss the coin, she would accept the “coin’s decision” and not think further about it.

This enabled her to get over many instances of decision fatigue – you have a dilemma only when you have two comparable choices, and won’t do too much worse by picking either.

So there’s this dilemma that’s hit me since this morning and facing trouble in making the decision (one of the choices has unquantifiable benefits so an objective cost-benefit analysis is not possible), I thought I should go back to my mother’s old method. And conveniently I see a coin lying on the table a metre away from me.

Thinking about it, tossing it and accepting its decision is acceptable only if I’m equally inclined to the two possibilities (assuming it’s a fair coin). Let’s say that I want to pick choice A three out of four times (“mixed strategies” can be rational in game theory), then I should toss the coin twice and pick A if either of the tosses returns a head. And so forth.

Considering how much decision fatigue I face (there have been times when I’ve actually turned around a dozen times after having taken only one step in each direction, not able to make up my mind), I should perhaps adopt this method. This makes me think that decision fatigue is also hereditary – and it was because she faced so much decision fatigue that my mother had to invent the coin toss method.

The title of this post is a tribute to an old colleague who would unfailingly say “toin coss” every time he intended to say “coin toss”, and tossing coins was an analogy he would make fairly often.

Correlation in defence purchases

Nitin Pai has a nice piece on defence procurement in Business Standard today. He writes:

Even if the planning process works as intended, it still means that the defence ministry merely adds up the individual requirements and goes about buying them. This is sub-optimal: consider a particular emerging threat that everyone agrees India needs to be prepared for. The army, navy and air force then prepare their own strategies and operational plans, for which they draw up a list of requirements. At the back of their minds, they know that the defence budget is more-or-less divided in a fixed ratio among them.

What he is saying, in other words, is that the defence ministry simply takes the arithmetic sum of demands from various components of the military, rather than taking correlation into account.

Let me explain using a toy example.

Let’s say that the Western wing of the Indian army (I’m making this up), the one that guards the border with Pakistan, wants 100 widgets that will come useful in case of a war. Let’s say that the Eastern wing of the Indian army, which guards the China border, wants 150 such widgets for the same purpose. The question is how many you should purchase.

According to Nitin, the defence ministry now doesn’t think. It simply adds up and buys 250. The question is if we actually need 250.

Let’s assume that these widgets are easily transportable, and let’s assume that the probability of a simultaneous conventional conflict with Pakistan and China is zero (given all three are nuclear states, this is a fair assumption). Do we still need 250 widgets? The answer is no, we only need 150, since we can quickly swing them over to where they are most required, and at the maximum, we need 150!

This is a case of negative correlation. There could be a case of positive correlation also – perhaps the chance of an India-China conventional conflict actually goes up when an India-Pakistan conventional conflict is on, and this might lead to more prolonged battles, meaning we might need more than 250 widgets! Or we have positive correlation.

The most famous example of ignoring correlation was the 2008 financial crisis, when ignored positive correlation led to mortgage backed securities and their derivatives blowing up. The Indian defence ministry can’t afford such a mistake.

Where Uncertainty is the killer: Jakarta Traffic Edition

So I’m currently in Jakarta. I got here on Friday evening, though we decamped to Yogyakarta for the weekend, and saw Prambanan and Borobudur. The wife is doing her mid-MBA internship at a company here, and since it had been a while since I’d met her, I came to visit her.

And since it had been 73 whole days since the last time we’d met, she decided to surprise me by receiving me at the airport. Except that she waited three and a half hours at the airport for me. An hour and quarter of that can be blamed on my flight from Kuala Lumpur to Jakarta being late. The rest of the time she spent waiting can be attributed to Jakarta’s traffic. No, really.

Yesterday evening, as soon as we got back from Yogyakarta, we went to visit a friend. Since this is Jakarta, notorious for its traffic, we landed up at his house straight from the airport. To everyone’s surprise, we took just forty minutes to get there, landing up much earlier than expected in the process.

So I’ve described two situations above which involved getting to one’s destination much ahead of schedule, and attributed both of them to Jakarta’s notorious traffic. And I’m serious about that. I might be extrapolating based on two data points (taking into the prior that Jakarta’s traffic is notorious), but I think I have the diagnosis.

The problem with Jakarta’s traffic is its volatility. Slow-moving and “bad” traffic can be okay if it can be predictable. For example, if it takes between an hour and half to hour and three-quarters most of the time to get to a place, one can easily plan for the uncertainty without the risk of having to wait it out for too long. Jakarta’s problem is that its traffic is extremely volatile, and the amount of time taken to go from one place to the other has a massive variance.

Which leads to massive planning problems. So on Friday evening, the wife’s colleague told her to leave for the airport at 7 pm to receive me (I was scheduled to land at 10:45 pm). The driver said they were being too conservative, and suggested they leave for the airport at 8, expecting to reach by 10:30. As it happened, she reached the airport at 8:45, even before my flight was scheduled to take off from KL! And she had to endure a long wait anyways. And then my flight got further delayed.

That the variance of traffic can be so high means that people stop planning for the worst case (or 95% confidence case), since that results in a lot of time being wasted at the destination (like for my wife on Friday). And so they plan for a more optimistic case (say average case), and they end up being late. And blame the traffic. And the traffic becomes notorious!

So the culprit is not the absolute amount of time it takes (which is anyway high, since Jakarta is a massive sprawling city), but the uncertainty, which plays havoc with people’s planning and messes with their minds. Yet another case of randomness being the culprit!

And with Jakarta being such a massive city and personal automobile (two or four wheeled) being the transport of choice, the traffic network here is rather “complex” (complex as in complex systems), and that automatically leads to wild variability. Not sure what (apart from massive rapid public transport investment) can be done to ease this.

Selection bias and recommendation systems

Yesterday I was watching a video on youtube, and at the end of it it recommended another (the “top recommendation” at that point in time). This video floored me – it was a superb rendition of Endaro Mahaanubhaavulu by Mandolin U Shrinivas. Listen and enjoy as you read the rest of the post.

https://www.youtube.com/watch?v=gvC4Pleog_0

I was immediately bowled over by youtube’s recommendation system. I had searched for both Shrinivas and Endaro … in the not-so-distant past so Youtube had put two and two together and served me up an awesome rendition! I was so happy that I went to town twitter about it.

It was then that I realised that this was the firs time ever that I had noticed the top recommendation of Youtube. In other words, every time I use youtube, it recommends a video to me, but I seldom notice it. And I seldom notice it for a reason – they’re usually irrelevant and crap. The one time I like the video it throws up, though, I feel really happy and go gaga over the algorithm!

In other words, there’s a bias which I don’t know what its exactly called – the bias that when event happens in a certain direction, you tend to notice it and give credit where you think it’s due. And when it doesn’t happen that way, you simply ignore it!

In terms of larger implications, this is similar to how legends such as “lucky shirts” are born. When something spectacular happens, you notice everything that is associated with that spectacular event and give credit where you think it’s due (lucky shirt, lucky pen, etc.). But when things don’t go your way you think it’s despite the lucky shirt, not because the shirt has become unlucky.

It’s the same thing with belief in “god”. When you pray and something good happens to you after that, you believe that your prayers have been answered. However, when you pray and something good doesn’t happen, you ignore the fact that you prayed.

Coming back to recommendation systems such as Youtube’s, the problem is that it is impossible for a recommendation system to get recommendations right all the time. There will be times when you get it wrong. In fact, going by my personal experience with Youtube, Amazon, etc. most of the time you will get your recommendation wrong.

The key to building a recommendation system, thus, is to build it such that you maximise the chances of getting it right. Going one step further I can say that you should maximise the chances of getting it spectacularly right, in which case the customer will notice and give you credit for understanding her. Getting it “partly right” most of the time is not enough to catch the customer’s attention.

Putting marketing jargon on it, what you should focus on is delighting the customer some of the time rather than keeping her merely happy most of the time!