Dynamic learning-based mechanism design for dependent valued exchange economies

• Authors: Swaprava Nath
• Venue: Proceedings of the 20th international conference companion on World wide web
• Year: 2011

Learning private information from multiple strategic agents poses challenge in many Internet applications. Sponsored search auctions, crowdsourcing, Amazon's mechanical turk, various online review forums are examples where we are interested in learning true values of the advertisers or true opinion of the reviewers. The common thread in these decision problems is that the optimal outcome depends on the private information of all the agents, while the decision of the outcome can be chosen only through reported information which may be manipulated by the strategic agents. The other important trait of these applications is their dynamic nature. The advertisers in an online auction or the users of mechanical turk arrive and depart, and when present, interact with the system repeatedly, giving the opportunity to learn their types. Dynamic mechanisms, which learn from the past interactions and make present decisions depending on the expected future evolution of the game, has been shown to improve performance over repeated versions of static mechanisms. In this paper, we will survey the past and current state-of-the-art dynamic mechanisms and analyze a new setting where the agents consist of buyers and sellers, known as exchange economies, and agents having value interdependency, which are relevant in applications illustrated through examples. We show that known results of dynamic mechanisms with independent value settings cannot guarantee certain desirable properties in this new significantly different setting. In the future work, we propose to analyze similar settings with dynamic types and population.