document: Dynamic learning-based mechanism design for dependent valued exchange economies 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 can not 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 .
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