  Good economic mechanisms depend on the preferences of participants in the
mechanism. For example, the revenue-optimal auction for selling an item is
parameterized by a reserve price, and the appropriate reserve price depends on
how much the bidders are willing to pay. A mechanism designer can potentially
learn about the participants' preferences by observing historical data from the
mechanism; the designer could then update the mechanism in response to learned
preferences to improve its performance. The challenge of such an approach is
that the data corresponds to the actions of the participants and not their
preferences. Preferences can potentially be inferred from actions but the
degree of inference possible depends on the mechanism. In the optimal auction
example, it is impossible to learn anything about preferences of bidders who
are not willing to pay the reserve price. These bidders will not cast bids in
the auction and, from historical bid data, the auctioneer could never learn
that lowering the reserve price would give a higher revenue (even if it would).
To address this impossibility, the auctioneer could sacrifice revenue
optimality in the initial auction to obtain better inference properties so that
the auction's parameters can be adapted to changing preferences in the future.
This paper develops the theory for optimal mechanism design subject to good
inferability.
