Murray Gell-Mann Amnesia and the Vodnoy Paradox

I’ve written about the Murray Gell-Mann amnesia here before. The idea there is that you trust whatever is printed in the newspaper, except for the section which is your domain of expertise. And despite the newspaper falling short in this section, you read the rest of the newspaper as if everything there is “correct”.

Yesterday I came across a similar idea when it comes to big government – what University of Nebraska economist Arthur M Diamond calls as a “Vodnoy Paradox” after his optomerist – it’s basically about big government advocates who advocate big government in all fields except for the one they’re expert in.

Government regulations sound plausible in areas where we know little and have thought less. But usually those who know an area well can tell us of the unexpected harmful consequences of seemingly plausible and well-intentioned regulations. As a result, the same person often advocates government regulations in areas in which they are ignorant and opposes them in areas where they have knowledge. I call this the “Vodnoy Paradox.” 

In the field they’re expert in they know that regulation doesn’t work, or is misguided, yet they support regulation in all other fields. Isn’t this just Murray Gell-Mann Amnesia in a different context? (quote via David Henderson of Econlog)

How markets work

A long time back, there was this picture that was making the rounds on Twitter and (more prominently) LinkedIn. It featured three boys of varying heights trying to look over a fence to see a ball game.

Here is what it looked like:

Source: http://www.freshshropshire.org.uk/about-us/equality-and-diversity/equality-of-opportunity/

These pictures were used to illustrate that equality of outcomes is not the equality of opportunity, or some such things, and to make a case for “justice”.

As it must be very clear, the allocation of blocks on the right is more efficient than the allocation of the blocks on the left – the tallest guy simply doesn’t need any blocks, while the shortest guy needs two.

And if you think about it, you don’t need any top-down “justice” to allocate the blocks in the right manner. All it takes is a bit of logical thinking and markets – and not even efficiently.

Think about how this scenario might play out at the ball park. The three boys go to see the ball game, and see three blocks at the fence. Each of them climbs a block, and we get the situation on the left.

Shortest boy realises he can’t see and starts crying. There are many ways in which this story can play out from here onward:

  1. Tallest boy realises that he doesn’t really need that extra block, and steps down and gives it to the shortest guy, giving the picture on the right.
  2. Tallest boy continues to stand on his block. Shortest boy realises that the tallest boy doesn’t need it, and requests him for the block. Assuming tallest boy likes him, he will give him the block.
  3. Tallest boy continues on the block. Shortest boy requests for it, but tallest boy refuses saying “this is my block why should I give it to you?”. Shortest boy negotiates. Tells tallest boy he’ll give him a chocolate or some such in return for the block. And gets the block.
  4. Tallest boy doesn’t want chocolate or anything else the shortest boy offers. In fact he might want to settle a score with the shortest boy and refuses to give the block. In this case, the shortest boy realises there is no point being there and not watching the ball game, and makes an exit. In some cases, the middle boy might negotiate with the tallest boy on his behalf, leading to the transfer of the block. In other situations, the shortest boy simply goes away.

Notice that in none of these situations (all of them reasonably “spontaneous”) does the picture on the left happen. In other words, it’s simply unrealistic. And you don’t need any top down notion of “justice” to enable the blocks to be distributed in a “fair” manner.

Pertinent observations on liquidity in startup markets

“Liquidity” was one of those words Wall Street people threw around when they wanted the conversation to end, and for brains to go dead, and for all questioning to cease

– Michael Lewis in Flash Boys

The quote that begins this blog post is also the quote that begins my book, which was released exactly a year ago. Despite its utility in everyday markets and economics, the concept of liquidity has not been explored too much outside of financial markets. In fact, one reason I wrote my book was that it appeared as if there was a gap in the market for material using the concept of liquidity to analyse everyday markets.

From this perspective, I was pleasantly surprised to come across a bunch of blog posts written by investors and tech analysts and startup fellows about the concept of “liquidity”. Most of these posts I came across by way of this excellent blog post by Andrew Chen of Andreessen Horowitz. It is always good to see others analysing topics in the same way as you are, so I thought I’ll share some insights from these posts here – some quotes, some pertinent observations. This is best done in bullet points. If you want to know more, I urge you to click through and read the blog posts in full. They’re all excellent.

  • You wonder why some startups make a big deal of how many cities they are in. This is because they usually function as within-city marketplaces, and so they need to be launched one city at a time. Uber famously started operations in San Francisco and remained there for a while.
  • “The best way to measure liquidity in the marketplace is to track the % of items or services that get sold/booked, and within what period of time. The higher the % and shorter period of time, the more sellers are making money and buyers are becoming loyal customers” – from here
  • “Where absolute pricing management makes most sense (i.e., where the marketplace operator sets prices) is where there isn’t a proper barometer for what the supply side should be charging and when the software can leverage systems should to optimize for liquidity” – from this excellent post
  • “In a zero sum game there, it’s most likely the marketplace with the most demand wins”. This was in the context of delivery marketplaces, and why Uber was likely to win that game (though it’s not clear if they’ve “won” it yet)
  • Trust is critical in building marketplaces. Both sides of the market need to trust the intermediary, and this can make marketplaces fragile. I had a recent incident where I appreciated the value of AirBnB landlord insurance (a lamp at a property I stayed at broke just after my stay, and the landlord wanted compensation). This post talks about how this insurance was critical to AirBnB’s growth
  • The same post talks about why even early stage businesses often make acquisitions – usually earlier stage businesses. “Marketplaces are normally winner-take-all markets. If we had lost ground to European competitors in 2012, we may have never gotten it back”
  • Ratings are a critical measure to build trust in a marketplace. And two-way ratings can help establish trust on both sides of the market
  • During the book launch function last year, there was a question on how marketplaces should build liquidity. I had given an example of the Practo/OpenTable model where you first sell a standalone service to one side of the market and then develop a marketplace. Another method (something I helped put in place for one of my current clients) is for the marketplace itself to become a “proprietary supplier”. The third, as this blog post describes, is about building markets where buyers are also sellers and the other way round (classic financial markets, for example).

For more on liquidity, and how it affects just about every market that you participate in on a daily basis, read my book!

Revenue management and transaction costs

So I just sent off a letter to India. To be precise, it is a document I had to sign and send to my accountant there – who sends regular “letters” any more?

The process at the post office (which, in my suburb, is located inside a large bookstore) was simple. In the first screen of the touch screen kiosk, there was an option for “worldwide < 20 grams”. A conveniently placed scale told me my letter weighed 18 grams, and one touch and one touch of my debit card later, I had my stamp. Within a minute, my letter was in the letterbox.

The story of how we pay the same amount for sending mail over large areas (“worldwide” in my case today) is interesting. Earlier, mail rates were based on distance, but as new roads kept being built in the 19th century America, and distances kept changing, figuring out how much to charge for a letter became “expensive”. A bright fellow figured out that the cost (in terms of time) of figuring out how much to charge for mail was of the same order of magnitude as the cost of the mail itself. And so the flat rate scheme for mail, that is prevalent worldwide today, was born.

Putting it in technical terms, transaction costs trumped price discrimination in this case. Price discrimination is the art (yes, it’s an art) of charging different amounts to different people based on their differential willingness to pay. Uber surge pricing is one example (I have a chapter in my book on this). Airline fares are another common example.

Until the late 18th century (well after mail prices had gone “flat”), price discrimination was rather common everywhere, a concept I have devoted a chapter to in the book. In fact, the initial motivation for fixed price retail was religious – Quakers, who owned many departmental stores in the US North-East, thought “all men are created equal before God” and so it was incorrect to charge different amounts to different people.

Soon other benefits of fixed prices became apparent (faster billing; less training for staff; in fact it was fixed prices that permitted the now prevalent supermarket format), and it took off. The concept is the same as stamps – the transaction cost of figuring out how much to charge whom is higher than the additional revenue you can make with such price differentiation (not counting possible loss of reputation, and fairness issues). Price discrimination at the shop is now confined to high value high margin businesses such as cars.

And it works in other high gross margin businesses such as airlines, hotels and telecom. These are all businesses with high fixed costs and low marginal costs for the suppliers. Low marginal costs has meant that price discrimination ha been termed as “revenue management” in the airline industry.

During the launch function of my book last year, I got asked if Uber’s practice of personalising fares for passengers is fair (I had given a long lecture on how Uber’s surge pricing is a necessary component of keeping average prices low and boosting liquidity in the taxi market). I had answered that a marketplace needs to ensure that its pricing is perceived as being “fair”, else they might lose customers to competitors. But what if all players in a market practice extreme price discrimination?

Thinking about it, transaction costs will take care of price discrimination before businesses and marketplaces start thinking of fairness. Beyond a point (the point varies by industry), the marginal revenues from price discrimination will fall below the transaction cost of executing this discrimination. And that poses a natural limit to how much price discrimination a business can practice.

Vacation Shopping

This is yet another of those questions whose answer seems rather obvious to everyone, and to me in full hindsight, but which has taken me a long time to appreciate

For a long time I never understood why people shop during vacations, when both time and luggage space are precious commodities. With global trade, I reasoned that most clothes should be available at reasonably comparable prices worldwide, and barring some special needs (such as a certain kind of shoes, for example), there was no real need to shop on vacations.

The last day of our trip to Munich in June convinced me otherwise. That was the only day on the trip that the wife was free from work, and we could go out together before our afternoon flight. The only place we ended up going out to turned out to be a clothing store, where the wife freaked out shopping.

It didn’t make sense to me – she was shopping at a chain store which I was pretty certain that I had seen in London as well. So why did she shop while travelling? And she shopped far more than she does in a normal shopping trip in London.

In hindsight, the answer is rather simple – diversity. While the same stores might exist in various countries or cities, each is adapted to local tastes and prevailing fashions. And while everyone watches the same “runways” in Milan and Los Angeles, there is always a subtle difference in prevailing styles in different places. And clothes in the stores in the respective places are tailored (no pun intended) to these styles.

And it can happen that the local prevailing styles are not something that you particularly agree with. For example, for years together in Bangalore I struggled to find plain “non-faded” jeans – most people there seemed to demand faced or torn jeans, and stores responded to serve that demand (interestingly, jeans shopping in my last Bangalore trip was brilliantly simple, so I guess things have changed).

Similarly, the wife finds it hard to appreciate most dresses in the shops in London (and I appreciate why she doesn’t appreciate them – most of the dresses are a bit weird to put it mildly), and as a result hasn’t been able to shop as much in recent times. She had taken to claim that “they don’t seem to be making normal clothes any more”.

But the styles in London aren’t correlated with the styles in Munich (or elsewhere), with the result that in that one chain store in Munich, she found more nice dresses than she had in some 20 shopping trips over a year in London.

Fashion suffers from the “tyranny of the majority“. It makes eminent sense for retailers to only stock those styles and models that have a reasonably high demand (or be compensated for stocking low-demand items with a high enough margin – I have a chapter on this in my book). So if your styles don’t match with those of people around you, you are out of luck.  But when you travel, you have the chance to align yourself to another majority. And if that alignment happens, you’re in luck!

PS: On a separate note, I’m quite disappointed with the quality of clothes in London. Across brands, they seem to wear much faster than those bought in continental Europe or even in India.

The utility of utility functions

That is the title of a webinar I delivered this morning on behalf of Kristal.AI, a company that I’ve been working with for a while now. I spoke about utility functions, and how they can be used in portfolio optimisation.

This is related to the work that I’ve been doing for Kristal, and lies at the boundaries between quantitative finance and behavioural finance, and in fact I spoke about utility functions (combined with Monte Carlo methods) as being a great method to unify quantitative and behavioural finance.

Interactive Brokers (who organised the webinar) recorded the thing, and you can find the recording here. 

I think the webinar went well, though I’m not very sure since there was no feedback. This was by design – the webinar was a speaker-only broadcast, and audience weren’t allowed to participate except in terms of questions that were directly sent to me.

In the first place, webinars are hard to do since it feels like talking to an empty room – there is no feedback, not even nods or smiles, and you don’t know if people are listening. In most “normal” webinars, the audience can interject by raising their hands, and you can try make it interactive. The format used here didn’t permit such interaction which made it seem like I was talking into thin air.

Also, the Mac app of the webinar tool used didn’t seem particularly well optimised. I couldn’t share a particular screen from my laptop (like I couldn’t say “share only my PDF, nothing else” which is normal in most online chat tools), and there are times where I’ve inadvertently exposed my desktop to the full audience (you can see it on the recording).

Anyways, I think I’ve spoken about something remotely interesting, so give it a listen. My “main speech” only takes around 20-25 minutes. And if you want to know more about utility functions and behavioural economics, i recommend this piece by John Cochrane to you.

Why AI will always be biased

Out on Marginal Revolution, Alex Tabarrok has an excellent post on why “sexism and racism will never diminish“, even when people on the whole become less sexist and racist. The basic idea is that there is always a frontier – even when we all become less sexist or racist, there will be people who will  be more sexist or racist than the others and they will get called out as extremists.

To quote a paper that Tabarrok has quoted (I would’ve used a double block-quote for this if WordPress allowed it):

…When blue dots became rare, purple dots began to look blue; when threatening faces became rare, neutral faces began to appear threatening; and when unethical research proposals became rare, ambiguous research proposals began to seem unethical. This happened even when the change in the prevalence of instances was abrupt, even when participants were explicitly told that the prevalence of instances would change, and even when participants were instructed and paid to ignore these changes.

Elsewhere, Kaiser Fung has a nice post on some of his learnings from a recent conference on Artificial Intelligence that he attended. The entire post is good, and I’ll probably comment on it in detail in my next newsletter, but there is one part that reminded me of Tabarrok’s post – on bias in AI.

Quoting Fung (no, this is not a two-level quote. it’s from his blog post):

Another moment of the day is when one speaker turned to the conference organizer and said “It’s become obvious that we need to have a bias seminar. Have a single day focused on talking about bias in AI.” That was his reaction to yet another question from the audience about “how to eliminate bias from AI”.

As a statistician, I was curious to hear of the earnest belief that bias can be eliminated from AI. Food for thought: let’s say an algorithm is found to use race as a predictor and therefore it is racially biased. On discovering this bias, you remove the race data from the equation. But if you look at the differential impact on racial groups, it will still exhibit bias. That’s because most useful variables – like income, education, occupation, religion, what you do, who you know – are correlated with race.

This is exactly like what Tabarrok mentioned about humans being extremist in whatever way. You take out the most obvious biases, and the next level of biases will stand out. And so on ad infinatum.