Two out of Five

Phil Price over at Andrew Gelman’s blog had an interesting post about a certain unnamed principle that caught my attention:

Many years ago I saw an ad for a running shoe (maybe it was Reebok?) that said something like “At the New York Marathon, three of the five fastest runners were wearing our shoes.” I’m sure I’m not the first or last person to have realized that there’s more information there than it seems at first. For one thing, you can be sure that one of those three runners finished fifth: otherwise the ad would have said “three of the four fastest.” Also, it seems almost certain that the two fastest runners were not wearing the shoes, and indeed it probably wasn’t 1-3 or 2-3 either: “The two fastest” and “two of the three fastest” both seem better than “three of the top five.” The principle here is that if you’re trying to make the result sound as impressive as possible, an unintended consequence is that you’re revealing the upper limit.

My first thought was naturally that this sentence is an instance of a scalar implicature. The unintended consequence is a scalar implicature that arises because of a violation of the maxim of quantity. Reebok doesn’t want to say “The third, fourth and fifth fastest runners of the marathon were wearing our shoes” (in the worst case), so they choose to say this instead. We, as Gricean listeners are licensed to derive the hidden inference because of the form of the utterance.

But does Reebok want us to make the inference that one of those three runners finished fifth? Of course not. The construction “three of the five fastest runners” is interesting for this reason – it doesn’t hide the inference, but it weakens it. Consider the alternatives:

  1. Some of the five fastest runners were wearing our shoes.
  2. Three of the fastest runners were wearing our shoes.
  3. Three of the five fastest runners were wearing our shoes.

(3) seems ideal – it’s technically the truth, and it sounds more impressive than (1) and (2). But I can’t shake the feeling that I’m missing something obvious in my thinking. For one, the partitive construction “Three of the five” should lead to a probabilistically stronger inference as Judith Degen has shown in her work. Also (3) sounds more impressive than (2) right? But why is the inference in (3) non-obvious on a first reading of the marketing slogan? Is there an effect due to the usage of the numeral determiners three and five? Assuming the truth of the statement, is Forty of the fifty fastest runners were wearing our shoes as impressive as (3)?

I’ll need to see what work has been done on these constructions, but sentences of this form seem to illustrate Degen’s non-homogeneity assertion: not all scalar implicatures have the same strength.


Andrew Gelman makes an interesting point in reply to my question of why Reebok would use this phrasing when it could lead to a weaker implicature: people know that something is up but we’re still taken in, similar to how we’re more likely to buy something if its ₹99 rather than ₹100.


20 April 2021

Vaibhav Vats in Caravan magazine, on the pathetic orchestrated tweets by Indian celebrities following Rihanna’s tweet bringing attention to the farmer’s protests:

Tendulkar became the epitome of values prized in the conventional, hierarchal and self-congratulatory milieu of the middle class, showing no eagerness to challenge the many prejudices of society and state. His notion of ethics remained limited purely to the realm of his own personal conduct … this personal decency has always been accompanied by a deeply ingrained timidity towards authority, a primal fear of upsetting any establishment, whether cricketing or otherwise.

This comment on the middle class resonated with me: respect authority, don’t step out of line, don’t cause trouble, etc. Why risk being anti-authoritarian when you can maintain your place in the social hierarchy by being obedient to authority?

Money Laundering for Bias

19 December 2020

It’s terrifying to choose an area of study for your PhD research – what if it turns out to be a dead end in a year? That thought is inescapable as I start (in earnest) studying the Linguistic Intergroup Bias.

At this critical juncture in my research (and life), I’m glad I found this talk by Maciej Ceglowski. It might seem overtly simplistic, but I appreciate its sentiment, and find it useful to keep in mind when studying bias from a computational perspective:

Instead of relying on algorithms, which we can be accused of manipulating for our benefit, we have turned to machine learning, an ingenious way of disclaiming responsibility for anything. Machine learning is like money laundering for bias. It’s a clean, mathematical apparatus that gives the status quo the aura of logical inevitability. The numbers don’t lie.

False Friends

3 November 2020

pardés in Hebrew:

  1. orchard, grove, fruit garden

paradesi in Tamil:

  1. Foreigner, stranger … 2. Sojourner, pilgrim, traveller;

What the latter definition leaves out is that paradesi is almost always used as a derogatory word. Are the two words cognates? The Hebrew word’s roots are Proto-Iranian as the etymology describes, while the Tamil word is probably derived from Sanskrit. Proto-Iranian and Sanskrit are both theorized to have developed from Proto-Indo-European. They are probably false friends — true cognates with different meanings due to semantic change.

My interest in these two words began when I learned about the Paradesi Jews of India — is it an accident that a word with negative connotations ties back to people who faced persecution and antisemitism? Maybe, but the sad truth today is that few people in Chennai are aware of its Jewish past. History is the story of people - many stories are tragic but deserve to be remembered.

Don't Make Me Talk

31 October 2020

John Siracusa in episode 19 of his pre-eminent podcast Hypercritical:

What were the earliest mass-market PC interfaces like?…they were like conversations…you’d tell it what to do, it gives information back to you…That was the basic paradigm until the Macintosh…What it gave you was not a conversation, but a thing…you could poke the thing and see how it reacts…it worked like a physical object

I really liked the comparison of early command-prompt user interfaces to conversations. It struck me that today’s AI assistants (Alexa, Google Assistant, Siri) are all based around having conversations. If these systems ever approached anything close to human intelligence and common-sense, perhaps having a conversation is the best way to interact with AI. But I wonder if there is a better interface to interact with AI? What’s the next leap from conversational AI? Perhaps Augmented Reality is the answer — artificial intelligence dispersed in our lived reality, giving us glancable information and the illusion of a physical object we can interact with. I wonder if this is why Apple is so bullish on AR as well.

Or maybe the best way to interact with artificial intelligence is the same way we interact with other people — using conversations.