Mechanism design for data science

The promise of data science is that if data from a system can be recorded and understood then this understanding can potentially be utilized to improve the system. Behavioral and economic data, however, is different from scientific data in that it is subjective to the system. Behavior changes when the system changes, and to predict behavior for any given system change or to optimize over system changes, the behavioral model that generates the data must be inferred from the data. The ease with which this inference can be performed generally also depends on the system. Trivially, a system that ignores behavior does not admit any inference of a behavior generating model that can be used to predict behavior in a system that is responsive to behavior. To realize the promise of data science in economic systems, a theory for the design of such systems must also incorporate the desired inference properties. Consider as an example the revenue-maximizing auctioneer. If the auctioneer has knowledge of the distribution of bidder values then she can run the first-price auction with a reserve price that is tuned to the distribution. Under some mild distributional assumptions, with the appropriate reserve price the first-price auction is revenue optimal [Myerson 1981]. Notice that the historical bid data for the first-price auction with a reserve price will in most cases not have bids for bidders whose values are below the reserve. Therefore, there is no data analysis that the auctioneer can perform that will enable properties of the distribution of bidder values below the reserve price to be inferred. It could be, nonetheless, that over time the population of potential bidders evolves and the optimal reserve price lowers. This change could go completely unnoticed in the auctioneer's data. The two main tools for optimizing revenue in an auction are reserve prices (as above) and ironing. Both of these tools cause pooling behavior (i.e., bidders with distinct values take the same action) and economic inference cannot thereafter differentiate these pooled bidders. In order to maintain the distributional knowledge necessary to be able to run a good auction in the long term, the auctioneer must sacrifice the short-term revenue by running a non-revenue-optimal auction.

[1]  P. Révész,et al.  Strong Approximations of the Quantile Process , 1978 .

[2]  C. J. Stone,et al.  Optimal Rates of Convergence for Nonparametric Estimators , 1980 .

[3]  Roger B. Myerson,et al.  Optimal Auction Design , 1981, Math. Oper. Res..

[4]  M. Csorgo.  Quantile processes with statistical applications , 1987 .

[5]  W. Newey,et al.  16 Efficient estimation of models with conditional moment restrictions , 1993 .

[6]  W. Newey,et al.  The asymptotic variance of semiparametric estimators , 1994 .

[7]  E. Parzen,et al.  Unified estimators of smooth quantile and quantile density functions , 1997 .

[8]  Q. Vuong,et al.  Optimal Nonparametric Estimation of First-price Auctions , 2000 .

[9]  David Lucking-Reiley.  Field experiments on the effects of reserve prices in auctions: More magic on the internet , 2000 .

[10]  Ariel Pakes,et al.  A Framework for Applied Dynamic Analysis in I.O , 2000 .

[11]  Xiaohong Chen,et al.  Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions , 2003 .

[12]  Lawrence M. Ausubel,et al.  The Lovely but Lonely Vickrey Auction , 2004 .

[13]  J. Powell,et al.  c ○ 2004 The Review of Economic Studies Limited Endogeneity in Semiparametric Binary Response Models , 2001 .

[14]  David H. Reiley.  Field experiments on the effects of reserve prices in auctions: more Magic on the Internet , 2006 .

[15]  Daniel C. Fain,et al.  Sponsored search: A brief history , 2006 .

[16]  Harry J. Paarsch,et al.  An Introduction to the Structural Econometrics of Auction Data , 2006 .

[17]  Philip A. Haile,et al.  Nonparametric Approaches to Auctions , 2007 .

[18]  Glenn Ellison,et al.  Position Auctions with Consumer Search , 2007 .

[19]  Moshe Babaioff,et al.  Characterizing truthful multi-armed bandit mechanisms: extended abstract , 2009, EC '09.

[20]  J. Morgan,et al.  How Much Is a Dollar Worth? Tipping versus Equilibrium Coexistence on Competing Online Auction Sites , 2006, Journal of Political Economy.

[21]  V. Chernozhukov,et al.  Inference on Counterfactual Distributions , 2009, 0904.0951.

[22]  Nikhil R. Devanur,et al.  The price of truthfulness for pay-per-click auctions , 2009, EC '09.

[23]  Xiaohong Chen,et al.  Estimation of Nonparametric Conditional Moment Models with Possibly Nonsmooth Generalized Residuals , 2009 .

[24]  Jason D. Hartline,et al.  Envy, truth, and profit , 2011, EC '11.

[25]  Michael Ostrovsky,et al.  Reserve Prices in Internet Advertising Auctions: A Field Experiment , 2009, Journal of Political Economy.

[26]  Alessandro Lazaric,et al.  A truthful learning mechanism for contextual multi-slot sponsored search auctions with externalities , 2012, EC '12.

[27]  Nima Haghpanah,et al.  Bayesian optimal auctions via multi- to single-agent reduction , 2012, EC '12.

[28]  Denis Nekipelov,et al.  Game Theory and Econometrics: A Survey of Some Recent Research , 2013 .

[29]  Nikhil R. Devanur,et al.  Prior-free auctions for budgeted agents , 2012, EC '13.

[30]  Jason D. Hartline,et al.  Auctions with unique equilibria , 2013, EC '13.

[31]  Moshe Babaioff,et al.  Dynamic Pricing with Limited Supply , 2011, ACM Trans. Economics and Comput..