From grandparent: > What's with the "AI" positioning? It's an implementation detail that’s abstracted away from end users, but also one of the enablers/answer

From grandparent: > What's with the "AI" positioning? It's an implementation detail that’s abstracted away from end users, but also one of the enablers/answers to the "why now" question on a business level. This article happened to focus heavily on that, probably because the other aspects (distributed systems and mechanism design) aren't broadly relatable. > Seems like you just need really fast optimization...A robust, shallow, transparent approach seems both safer and easier to sell. With you on that. Academically/literature wise our approach is AI + ML given that it has priors, and doesn't rely on oblivious search techniques such as branch and bound or LP rounding for end-to-end solving. The article states that there's a "black box" involved, which is incorrect. When we talk to regulators and market participants we're very clear on how objective and deterministic the process is, and the bounds. We've structured the problem in a way that uses the economics of a double auction as the floor, meaning that any match we return is equivalent to or better than what would have resulted from matching against a conventional limit order book (CDA) or batch auction. That, combined with the timestamping aspect, results in something that's more fair, transparent, and auditable than the existing market structure. From parent: > There is no collapsing it This is correct for truthful approximations and most formulations of combinatorial VCG. We don't fall into that camp, but we're still left with something that is worst-case exponential. > ...sort of approximation In many cases we can find the true optimal, and the cases where we can't become adversarial inputs for the AI and are still subject to the bounds described above. This has to do with the empirical runtime versus worst-case runtime of an algorithm, which is a fascinating field in its own right.