Progressive Participation

Dirk Bergemann and Philipp Strack ()

No 2189, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: A single seller faces a sequence of buyers with unit demand. The buyers are forward-looking and long-lived but vanish (and are replaced) at a constant rate. The arrival time and the valuation is private information of each buyer and unobservable to the seller. Any incentive-compatible mechanism has to induce truth-telling about the arrival time and the evolution of the valuation. We derive the optimal stationary mechanism, characterize its qualitative structure and derive a closed-form solution. As the arrival time is private information, the agent can choose the time at which he reports his arrival. The truth-telling constraint regarding the arrival time can be represented as an optimal stopping problem. The stopping time determines the time at which the agent decides to participate in the mechanism. The resulting value function of each agent can not be too convex and has to be continuously differentiable everywhere, reflecting the option value of delaying participation. The optimal mechanism thus induces progressive participation by each agent: he participates either immediately or at a future random time.

Keywords: Dynamic Mechanism Design; Observable Arrival; Unobservable Arrival; Repeated Sales; Interim Incentive Constraints; Interim Participation Constraints; Stopping Problem; Option Value; Progressive Participation (search for similar items in EconPapers)
JEL-codes: D44 D82 D83 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2019-08
New Economics Papers: this item is included in nep-des, nep-gth, nep-mic and nep-ore
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Related works:
Journal Article: Progressive participation (2022)
Working Paper: Progressive Participation (2021)
Working Paper: Progressive Participation (2020)
Working Paper: Progressive Participation (2020)
Working Paper: Progressive Participation (2020)
Working Paper: Progressive Participation (2020)
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