Artificial intelligence, algorithmic pricing, and collusion

Correct!

Regulated sectors are indicators of markets that aren't competitive since regulation tends to come in after it's well past obvious that markets have failed. When Amazon gets to their end game (which they seem to be pretty close to IMO) I think you'll be surprised at how much and long they'll be able to screw customers... after all, most of their competitors will be long gone and the few that remain will be weak so who's going to be able to offer an alternative in the short to intermediate term?

And that observation makes the article doubly weird:

1) Nobody needs an AI to figure out Tit-for-Tat is a good strategy. It isn't complicated.

2) The technical economic term for this collusion is probably something like 'efficient market price', where the sellers have agreed on what the fair price is to offer their service.

Collusion can't mean that the sellers all have an implicitly coordinated price, because the market is always going to settle on an implicitly coordinated price if conditions aren't changing.

Collusion is something like companies agreeing to strategically offer and not offer services to control their competitors. Like, maybe FedEx has a deal where they refuse to carry online goods that aren't bought from Amazon (don't ask me how to implement that), that would be collusion. I'm not getting a service for my online store because there is an exclusionary deal amongst the big players.

For the sake of a more informed discussion, here is a copy of the actual paper:

https://a.qoid.us/SSRN-id3310310.pdf

(Couldn’t find it on Sci-Hub, so I paid $5 for it.)

Edit: in particular, it addresses a question I had after seeing the original article and graph… or tries to:

> On the face of it, one may wonder whether the algorithms are effectively punishing the deviation, or whether instead the price cuts simply serve to regain market share. Looking only at the non-deviating firm’s behavior, in fact, it is hard to tell these alternative explanations apart. But if you focus on the behavior of the deviating firm, the difference becomes perfectly clear. Given that in the deviation period (i.e., period τ = 1) the rival has stuck to its old price, in period τ = 2 the deviating algorithm, which meanwhile has regained control of the pricing, has no reason to cut its price endogenously unless it is taking part in the punishment itself. If its only concern were to maintain its market share, the deviating algorithm would cut its price only in period τ = 3, i.e. after observing the rival’s price reduction in period τ = 2. Actually, however, in period τ = 2 the deviating algorithm prices almost exactly the same as the other. This clearly shows that the deviating algorithm is responding not only to its rival’s but also to its own action. Such self-reactive behavior is often crucial to achieve genuine collusion, and it would be difficult to rationalize otherwise.

The problem with this explanation is that the “AI” algorithm they’re using is ridiculously simple. They talk about its input being “the set of all past prices in the last k periods”… and then, for their main experiment, they set k to 1! So unless I’m misunderstanding something, the input at each round literally consists of both players’ chosen prices from the immediately previous round; the algorithm has no memory beyond that. So all it knows is that its price is lower than its competitor’s; how is it in any way surprising that it would decide to increase the price for the next round?

And yes, I mean increase – the authors seem to claim it “cut its price endogenously”, but on the graph, the price clearly increases at τ = 2 compared to τ = 1. It does keep its price “cut” compared to two rounds ago, before the intervention, but again that’s not surprising since it only has memory of the last round.

Am I missing something?

Also what is happening with wages. Companies relying sharing their wage data with each other through in organizations like Options Impact, and effectively colluding to match each others’ comp.