Design of Optimal Online Matching Markets – DOOM

Future markets are going to be online, wide (even international) and the pairing between offer and demand is going to be facilitated, organized, and regularized by some platforms, obviously learning from gathered data, past errors, and successes, in some automatic fashion. Artificial intelligence will certainly be their core technique. Formally, the objective is to match supply to demand agents sequentially (and even if some delay is sometimes admissible, irrevocable decisions must be made), typically at some cost (that depends on both sides). On the other hand, this generates utilities, for both the supply and demand agent and some revenue for the platform. Reconciling those contradictory objectives is quite difficult and challenging; it relies on bargaining, auctions or any other mechanisms. Finally, as these markets might involve real persons, with their own interests, whose lives can be strongly impacted by AI decisions; it is crucial to take this aspect into account, for obvious ethical questions, but also because the data-generating process is quite intricate, as it is the consequence of many multi-agent interactions.

The global, far-reaching objective of project DOOM is to design new theoretical AI approaches for admissible online matching market platforms, i.e., that are efficient, but remains respectful of its participating agents. We will develop and analyze algorithms that will answer the following questions. Who should be matched to who, and when? What are the best mechanisms for incentive agents and/or price transactions? What data are going to be used and how?