Bobak Pakzad-Hurson
ABSTRACT
Mechanisms used to derive optimal allocations are generally designed under the premise that agents fully know their preferences. It is often impossible to duplicate these optimal allocations when agents imperfectly observe object characteristics. I present a crowdsourcing mechanism to approximate optimal allocations under imperfect observations. To ensure truth-telling, agents are punished when their reports differ from the "wisdom of the crowd." Under mild conditions, this crowdsourcing-with-punishment mechanism replicates the full-information optimal allocation with probability exponentially converging to one in the size of the market, with aggregate worst-case waste (punishment) converging exponentially to zero. I show that no other mechanism can meaningfully improve upon the proposed mechanism. The approach I take is general, and can be applied in many settings, including two-sided matching markets.
Link to full version of paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2618837
Index Terms
- Crowdsourcing and Optimal Market Design
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