Algorithmic Game Theory and Data Science

With FOCS submissions sent off and EC rejections in hand, its time to think about presenting your work at a workshop, and chat with your colleagues doing similar things. 

If you are working on something at the intersection of algorithmic game theory and machine learning (this includes e.g. the sample complexity of auction design, or learning from revealed preferences, or learning from censored feedback), then you should consider the "Algorithmic Game Theory and Data Science" workshop that we'll be running during EC 2015. Conveniently, this is at FCRC, so if you were planning on attending EC or STOC (or SIGmetrics, or CCC, or...) you'll already be there in Portland. 

Deadline is in 10 days!

https://sites.google.com/site/agtanddatascienceworkshop2015/

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Call for Papers

In conjunction with the Sixteenth ACM Conference on Economics and Computation (EC'15), we solicit submissions for the First Workshop on Algorithmic Game Theory and Data Science, to be held on June 15, 2015 in Portland, Oregon, USA. Computer systems have become the primary mediator of social and economic interactions, enabling transactions at ever-increasing scale.  Mechanism design when done on a large scale needs to be a data-driven enterprise.  It seeks to optimize some objective with respect to a huge underlying population that the mechanism designer does not have direct access to.  Instead, the mechanism designer typically will have access to sampled behavior from that population (e.g. bid histories, or purchase decisions).  This means that, on the one hand, mechanism designers will need to bring to bear data-driven methodology from statistical learning theory, econometrics, and revealed preference theory.  On the other hand, strategic settings pose new challenges in data science, and approaches for learning and inference need to be adapted to account for strategization.   The goal of this workshop is to frame the agenda for research at the interface of algorithms, game theory, and data science.  Papers from a rich set of experimental, empirical, and theoretical perspectives are invited.  Topics of interest include but are not limited to: Can good mechanisms be learned by observing agent behavior in response to other mechanisms?  How hard is it to "learn'' a revenue maximizing auction given a sampled bid history?  How hard is it to learn a predictive model of customer purchase decisions, or better yet, a set of prices that will accurately maximize profit under these behavioral decisions?  What is the sample complexity of mechanism design?  How much data is necessary to enable good mechanism design? How does mechanism design affect inference?  Are outcomes of some mechanisms more informative than those of others from the viewpoint of inference? How does inference affect mechanism design?  If participants know that their data is to be used for inference, how does this knowledge affect their behavior in a mechanism? Can tools from computer science and game theory be used to contribute rigorous guarantees to interactive data analysis?  Strategic interactions between a mechanism and a user base are often interactive (e.g. in the case of an ascending price auction, or repeated interaction with a customer and an online retailer), which is a setting in which traditional methods for preventing data over-fitting are weak. Is data an economic model? Can data be used to evaluate or replace existing economic models?  What is the consequence for game theory and economics for replacing the model with data. Submission Instructions Any submission format between abstracts and full papers will be considered.  Abstracts may be rejected if we cannot sufficiently evaluate their contribution.  Full papers will be evaluated after page 10 only at the discretion of the committee. We solicit both new work and work recently published or soon to be published in another venue.  For submissions of the latter kind, authors must clearly state the venue of publication.  This workshop will have no published proceedings.  Papers appearing in published conference proceedings or journals subsequent to EC 2014 will be considered, though preference may be given to papers that have not yet appeared.  Papers that have appeared or are to appear at EC or affiliated workshops will not be considered. Authors are encouraged to provide a link to an online version of the paper (such as on arXiv).  If accepted, such papers will be linked via an index to give an informal record of the workshop. All submissions should be sent electronically to AGTDataScienceWorkshop15@gmail.com on or before April 26th, 2015.  Notification of acceptance will be on May 11, 2015. Organizing Committee Shuchi Chawla, U. of Wisconsin  Hu Fu, Microsoft Research Jason Hartline, Northwestern U. Denis Nekipelov, U. of Virginia Aaron Roth, U. of Pennsylvania Kane Sweeney, eBay and Stubhub