Comment by gwern | Hacker News Reader

Comment by gwern | Hacker News Reader

> Who knows why? I’m usually more willing to spend than she is, and I bet that's represented on my user profile. I was paying with a gift card, which surely contributes. Maybe it was a price scraping update, comparison shopping detection, or a system that explores “face-in-the-door” high prices before backing down. From the outside, no one really knows.

The most obvious possibility omitted is that your wife got the first, easy, cheap car and then Uber had to quote you a higher price to get a second car. Cars don't fall from the sky; if two people successively ask for bids, how else could it work? What if the app quoted you both the cheap price for the only car within X blocks, and you bought it before she did? Is it suddenly going to go 'oops sorry, changed my mind, it now costs twice as much'? Sounds like a very bad experience to me! More sensible to give the first person a low quote and then when - unexpected and unpredictably - someone requests something similar, quote them the higher price reflecting the sudden local micro-shortage.

(author here) I believe I had checked first in this case, which is why it was surprising. Sorry not to mention that in the post. This was in San Francisco, and there were multiple cars shown on the map.

In my experience, I usually don't see this kind of price change before the request has actually been confirmed - and I have seen Lyft change the price between showing me the estimate and confirming the request (with an apologetic confirmation dialog, possibly only after some holding period has timed out).

Maybe in my case where the high quote came first, the opposite scenario happened - a glut of drivers appeared between my request and hers, raising supply.

Opaque pricing is powerful partly because we don't know. This enables people to construct a plausible story to explain any price.

Well, whichever one checked first, the point still holds. Checking is not a free operation and you should expect it to have consequences and it to affect Uber's demand forecasts etc., and affect other price quotes. You do not have independence here and so the comparison is not as meaningful as it seems. As the Roman wit said, "when two people do the same thing, it's not the same thing."

(The right way to do this is to randomize multiple independent occasions - wait until one of you was about to call an Uber, immediately flip a coin to decide who does, each time checking you or your wife's Uber, and never both, and compare the long-run average.)

> with an apologetic confirmation dialog, possibly only after some holding period has timed out

Right. I've seen the same thing myself. They would prefer not to apologize to the customer because they changed the price, because it is in fact annoying and a bad customer experience. So the prices are surely carefully set in many ways with an eye towards not changing as much as possible.

> This enables people to construct a plausible story to explain any price.

Indeed. So you should mention one of the most plausible stories if you're going to list a bunch of them.

it would be great if this were the case. unfortunately, Uber has been documented to practice individual price discrimination at a massive scale, using factors like if you’re in a low-income vs high-income neighborhood, individual rider “price sensitivity”, etc, in addition to market conditions (surge pricing), and as a result they have netted billions in profit [1]. i would guess this is why Uber AI researchers are paid so much.

[1] https://len-sherman.medium.com/how-uber-became-a-cash-genera...

That raises an interesting question: if 10 people in a room request ubers without confirming the ride-hail, does the price go up for successive requests?

Hm... I think I would predict that it would depend on speed because querying prices reveals information, but not accepting also reveals information, so I would expect that it would go up initially due to an expected shortage, but then when it becomes clear that no one is accepting, it reverts to normal or lower, because that suggests low urgency or willingness-to-pay compared to what the models had forecast for that exact location & time, so they have to be offered lower prices to accept at all.

(This is the flipside of personalized pricing: it's also lower prices, as noted in some of the anecdotes, although he mostly emphasizes the potential for higher prices.)

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