scholarly journals Optimal Auction Design With Common Values: An Informationally Robust Approach

Econometrica ◽  
2021 ◽  
Vol 89 (3) ◽  
pp. 1313-1360
Author(s):  
Benjamin Brooks ◽  
Songzi Du
Keyword(s):  
Mechanism Design ◽  
Information Structure ◽  
Single Unit ◽  
Information Design ◽  
Design Problems ◽  
Auction Design ◽  
One Dimensional ◽  
Common Value ◽  
Optimal Auction ◽  
Joint Mechanism

A profit‐maximizing seller has a single unit of a good to sell. The bidders have a pure common value that is drawn from a distribution that is commonly known. The seller does not know the bidders' beliefs about the value and thinks that beliefs are designed adversarially by Nature to minimize profit. We construct a strong maxmin solution to this joint mechanism design and information design problem, consisting of a mechanism, an information structure, and an equilibrium, such that neither the seller nor Nature can move profit in their respective preferred directions, even if the deviator can select the new equilibrium. The mechanism and information structure solve a family of maxmin mechanism design and minmax information design problems, regardless of how an equilibrium is selected. The maxmin mechanism takes the form of a proportional auction: each bidder submits a one‐dimensional bid, the aggregate allocation and aggregate payment depend on the aggregate bid, and individual allocations and payments are proportional to bids. We report a number of additional properties of the maxmin mechanisms, including what happens as the number of bidders grows large and robustness with respect to the prior over the value.

scholarly journals Selling to Intermediaries: Optimal Auction Design in a Common Value Model

2017 ◽  
Author(s):  
Dirk Bergemann ◽  
Benjamin A. Brooks ◽  
Stephen Edward Morris
Keyword(s):  
Auction Design ◽  
Common Value ◽  
Optimal Auction ◽  
Value Model

scholarly journals Optimal Auction Design in a Common Value Model

2016 ◽  
Author(s):  
Dirk Bergemann ◽  
Benjamin A. Brooks ◽  
Stephen Morris
Keyword(s):  
Auction Design ◽  
Common Value ◽  
Optimal Auction ◽  
Value Model

scholarly journals Countering the winner's curse: Optimal auction design in a common value model

2020 ◽  
Vol 15 (4) ◽  
pp. 1399-1434
Author(s):  
Dirk Bergemann ◽  
Benjamin Brooks ◽  
Stephen Morris
Keyword(s):  
Expected Value ◽  
Winner's Curse ◽  
English Auctions ◽  
Auction Design ◽  
Winner’S Curse ◽  
Common Value ◽  
Optimal Mechanism ◽  
Optimal Auction ◽  
Value Model ◽  
Posted Price

We characterize revenue maximizing mechanisms in a common value environment where the value of the object is equal to the highest of the bidders' independent signals. If the revenue maximizing solution is to sell the object with probability 1, then an optimal mechanism is simply a posted price, namely, the highest price such that every type of every bidder is willing to buy the object. If the object is optimally sold with probability less than 1, then optimal mechanisms skew the allocation toward bidders with lower signals. The resulting allocation induces a “winner's blessing,” whereby the expected value conditional on winning is higher than the unconditional expectation. By contrast, standard auctions that allocate to the bidder with the highest signal (e.g., the first‐price, second‐price, or English auctions) deliver lower revenue because of the winner's curse generated by the allocation. Our qualitative results extend to more general common value environments with a strong winner's curse.

Optimal Auction Design with Deferred Inspection and Reward

2020 ◽  
Author(s):  
Saeed Alaei ◽  
Alexandre Belloni ◽  
Ali Makhdoumi ◽  
Azarakhsh Malekian
Keyword(s):  
Auction Design ◽  
Optimal Auction

Terminal Ranking Games

2021 ◽  
Author(s):  
Erhan Bayraktar ◽  
Yuchong Zhang
Keyword(s):  
Mechanism Design ◽  
Price Of Anarchy ◽  
Mean Field ◽  
Design Problems ◽  
Mean Field Game ◽  
Reward Functions

We analyze a mean field tournament: a mean field game in which the agents receive rewards according to the ranking of the terminal value of their projects and are subject to cost of effort. Using Schrödinger bridges we are able to explicitly calculate the equilibrium. This allows us to identify the reward functions which would yield a desired equilibrium and solve several related mechanism design problems. We are also able to identify the effect of reward inequality on the players’ welfare as well as calculate the price of anarchy.

Issues on Teaching Kinematics and Mechanism Design

1992 ◽  
Author(s):  
Arthur G. Erdman ◽  
Thomas R. Corrigan
Keyword(s):  
Mechanism Design ◽  
Software Package ◽  
Computer Software ◽  
Computer Solution ◽  
Design Problems ◽  
Introductory Course ◽  
Modern Computer ◽  
Computer Software Package ◽  
Historical Examination

Abstract The issues, problems and possible solutions involved in teaching a modern course on mechanisms and kinematics are addressed from the perspective of a professor and a student. A historical examination shows the value of modern (computer) solution of classical dilemmas. The structure of an introductory course is then presented, with comments on its educational attributes. The solution of several design problems with LINCAGES©, a computer software package, demonstrates the prowess of the modem student/computer liaison.

scholarly journals Informationally Robust Optimal Auction Design

2016 ◽  
Author(s):  
Dirk Bergemann ◽  
Benjamin A. Brooks ◽  
Stephen Morris
Keyword(s):  
Auction Design ◽  
Optimal Auction

scholarly journals Information Design

2019 ◽  
Vol 11 (4) ◽  
pp. 151-185 ◽  
Author(s):  
Ina Taneva
Keyword(s):  
Information Structure ◽  
Information Design ◽  
Signal Distribution ◽  
Private Signals ◽  
Optimal Information

A designer commits to a signal distribution that is informative about a payoff-relevant state. Conditional upon the privately observed signals, agents take actions that affect their payoffs as well as those of the designer. We show how to derive the (designer) optimal information structure in static finite environments. We fully characterize it in a symmetric binary setting for a parameterized game. In this environment, conditionally independent private signals are never strictly optimal. (JEL C72, D78, D82, D83)

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