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Consider a user-to-user marketplace, where sellers sell a good at a price that they decide and buyers choose which product to buy.

When a seller decides the price for a product, the seller should try to form some expectation on the prices set by competitors on the same (or similar) product(s), by engaging in a sort of pricing game.

As some marketplaces are quite complex environments, it could be that a seller makes pricing mistakes, i.e., he is not able to play the pricing game correctly.

I'm interested in the economics implications of this phenomenon from the point of view of the platform. In particular:

(1) Does the platform suffer any negative externalities from hosting sellers doing systematic pricing mistakes? Can this externality be measured somehow?

(2) Does the platform has any incentive to ensure that its sellers avoid pricing mistakes? (this is related to question 1)

(3) If the answer to 1 and 2 is "YES", is there any discussion in the economic literature or policy scene on replacing human pricing decisions with algorithmic pricing decisions in such platforms?

Could you give me a practical example by referring to a real platform? Could you mention anything you think can be related to questions 1,2,3, even if not exactly answering them. I'm trying to understand this phenomenon from a broad perspective.

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  • $\begingroup$ No, I'm not sure. Happy to put other tags, if you have suggestions. $\endgroup$
    – Star
    Feb 4, 2022 at 9:10

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1 ) This is impossible to measure. Every single client has a different "demand function", that also varies considerable over time. Who would have thought that toilet paper will ever be sold out prior to COVID? Even if you skyrocket prices (most natural approach), there would be a massive backlash in the media - which in itself hurts the reputation (of the seller, but also the platform).

In a real world scenario. Would the seller have had higher revenue based on a price of £579 or $589? How about toothpaste, what is the (aggregate) elasticity of demand for toothpaste and how about substitutes?

Even the model the platform itself uses to get income is not clear to determine. For instance, revenue maximation is not equal profit maximization. On the other hand, Amazon shows "short" term profits may not matter as long as in the long run your model succeeds.

2 ) Of course, just google pricing mistakes (not systematic - which is difficult to determine in the first place, but obvious) - you will find plenty of news articles where M&S, Amazon, Asda and co had negative publicity or customer / media backlash. In case of Amazon for example, it had a so called Price Parity Agreement. This was under scrutiny for antitrust issue. Nowadays, Amazon "forces" 3rd party seller to abide to a Fair Pricing Policy.

Other platforms like Ebay have similar contractual obligations. Insofar, they have not only an incentive, but direct, contractual rules that aim to help the platform's sellers to get the "best" price.

3 ) While algorithmic trading in finance is indeed a big deal, it cannot be compared to retail selling platforms. It is like comparing a Formula 1 car with a mid-sized family sedan.

  • listed products (exchange traded) have well defined supply AND demand data (order books)
  • OTC (over the counter) products have usually a multitude of quotes available on platforms like Bloomberg and the underlying products are a lot easier to price. A issuer callable floating rate bond may be hard to price properly, but everyone knows exactly how the cashflows look like, given certain conditions.
  • everyone can sell and buy the product within seconds (if needed, even commercially available software allows for 20,000+ orders per second per single connection.

However, google "price discrimination cookies". For example, the [U.S. Department of Transportation (DOT) has approved a new passenger data collection system. This essentially allows airlines and travel agencies to collect personal data, such as marital status, addresses and travel history, to offer a “more agile pricing and more personalised offerings”. It was revealed that the online travel company Orbitz shows MAC users different, primarily more expensive, travel options (e.g. costlier hotel rooms) than windows users.

The book Information Rules offers a great non technical explanation and summary of examples how e-commerce can engage in price setting. This is somewhat dated, but general principles do not change and it shows that price setting was already supported by algos a long time ago (1998 is a long time in computing).

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(1) and (2) If a seller accidentally misprices a book on flies that has an approx. value of \$50, and offers it instead for \$1 million, then probably no one will buy the book, and the platform does not get a commission. This is a negative externality. Estimating lost sales would probably be tricky though.

(3) There is literature on the antitrust implications of algorithmic pricing, e.g.,
"An empirical analysis of algorithmic pricing on amazon marketplace" (2016)
"Artificial intelligence, algorithmic pricing, and collusion" (2020)

Algorithmic trading is huge in finance, and there is a lot of literature on this as well.

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  • $\begingroup$ Thanks. Is there any discussion on the fact that the marketplace itself would benefit from sellers adopting algorithmic pricing? $\endgroup$
    – Star
    Feb 3, 2022 at 21:28
  • $\begingroup$ I don't know; I recommend you add some clever keywords to "algorithmic trading" then try browsing the abstracts. $\endgroup$
    – Giskard
    Feb 4, 2022 at 7:11
  • $\begingroup$ This is a negative externality ... Is it? In what sense? Externalities are typically taken to be unpriced indirect effects on uninvolved third parties as the result of a consumer or producer market transaction. In what sense of the word do a broker's (lost) commissions on (lost) sales qualify? The broker aren't an uninvolved third party, they are directly involved in the transaction (in fact the transaction is made precisely because of their involvement), and the commission is already directly priced into the market price that the seller asks from the consumer. $\endgroup$ Feb 7, 2022 at 11:49
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    $\begingroup$ Right, I would take it that negative externalities would be ruled out by definition; of course there might be negative consequences for the broker. (Similarly if a wholesaler systematically pushed unrealistic price levels for their goods this would be negative, i.e., bad for the business of retailers who sell them on to consumers; but the theory of negative externalities wouldn't be a good fit for explaining why or what to do about it, since in this case the costs of decisions are already "fully internalized" among the full set of market actors involved along the chain of transactions.) $\endgroup$ Feb 7, 2022 at 16:32
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    $\begingroup$ OTOH mistaken or foolish or otherwise blameworthy practices by sellers might have negative externalities for other sellers in the same marketplace, with whom they don't have a business relationship. E.g. if mis-pricing by widget seller A erodes consumer trust or interest in all widget sellers, that could plausibly be treated as a negative externality for widget seller B, but it is an already-internalized cost in the Seller A-broker-consumer relationship. $\endgroup$ Feb 7, 2022 at 16:38

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