Call for Nominations for the SIGecom Doctoral Dissertation Award

The SIGecom Doctoral Dissertation Award recognizes an outstanding dissertation in the field of economics and computer science. The award is conferred annually at the ACM Conference on Economics and Computation and includes a plaque, complimentary conference registration, and an honorarium of $1,500. A plaque may further be given to up to two runners-up. No award may be conferred if the nominations are judged not to meet the standards for the award.

To be eligible, a dissertation must be on a topic related to the field of economics and computer science and must have been defended successfully during the calendar year preceding the year of the award presentation.

The next SIGecom Doctoral Dissertation Award will be given for dissertations defended in 2017. Nominations are due by the March 31, 2018, and must be submitted by email with the subject "SIGecom Doctoral Dissertation Award" to the awards committee at sigecom-awards-diss@acm.org. A dissertation may be nominated simultaneously for both the SIGecom Doctoral Dissertation Award and the ACM Doctoral Dissertation Award.

Nominations may be made by any member of SIGecom, and will typically come from the dissertation supervisor. Self-nomination is not allowed. Nominations for the award must include the following, preferably in a single PDF file:

1. A two-page summary of the dissertation, written by the nominee, including bibliographic data and links to publicly accessible versions of published papers based primarily on the dissertation.
2. An English-language version of the dissertation.
3. An endorsement letter of no more than two pages by the nominator, arguing the merit of the dissertation, potential impact, and justification of the nomination. This document should also certify the dissertation defense date.
4. The names, email addresses, and affiliations of at least two additional endorsers.

The additional endorsement letters themselves should be emailed directly to sigecom-awards-diss@acm.org, by the same deadline. These endorsements should be no longer than 500 words, and should specify the relationship of the endorser to the nominee, contributions of the dissertation, and its potential impact on the field.

It is expected that a nominated candidate, if selected for the award, will attend the next ACM Conference on Economics and Computation to accept the award and give a presentation on the dissertation work. The cost of attending the conference is not covered by the award, but complimentary registration is provided.

Submit your papers to WWW 2018

Jennifer Wortman Vaughan and I are the track chairs for the the "Web Economics, Monetization, and Online Markets" track of WWW 2018. The track name is a little unwieldy -- we tried to change it to "Economics and Markets" -- but the focus should be of interest to many in the AGT, theory, and machine learning communities. See the call for papers here: https://www2018.thewebconf.org/call-for-papers/research-tracks-cfp/web-economics/ We have a great PC, and the topics of interests include (amongst many other things), "Economics of Privacy" and "Fairness in Economic Environments".

January Fairness Workshop at Penn

At the beginning of the semester, I mentioned that after the law school's semester of events (click for videos) on fairness, machine learning, and the law, we would host a technical workshop on recent work on fairness in machine learning.

We have now finished putting together the program, which will be terrific. The workshop will take place here at Penn from January 19-20th. Take a look at our great line-up of speakers here: https://sites.google.com/view/fairnessconfererencepenn

The event is open to the public, but registration is required.

Designing the Digital Economy

I'm on a train to New Haven, where I'll be giving a guest lecture (together with Solon Barocas) in Glen Weyl's class, "Designing the Digital Economy" (n.b. I need to get advice from Glen about how to get as good publicity for my classes...)

Solon and I will be sharing the 3 hour class, talking about fairness in machine learning, starting at 2:30. Pop by if you are around -- otherwise, here are my slides.

Semester on Fairness and Algorithms at Penn

This year, the "Fels Policy Research Initiative" is funding two exciting events, both related to fairness and machine learning. The first, joint between the law school and statistics, is called "Optimizing Government", and will host a series of 4 seminars over the course of this semester touching on technical and legal aspects of fairness.

I will be speaking at the first one, introducing the basics of Machine Learning and scenarios in which its use can lead to inadvertent discrimination. My inimitable colleague Richard Berk (who actually builds models used to predict criminal recidivism used by the state of Pennsylvania) will be offering his comments following my talk.

The second seminar will provide a panel discussion on what the law demands in terms of "fair and equal treatment", and how it relates to the use of machine learning. The panelists will come from Philosophy, Political Science, and Law.

The third seminar will be given by our excellent Warren Center postdoc Jamie Morgenstern, and will focus on technical solutions to the problem of unfairness in machine learning, and how it can be squared with learning the optimal policy in online decision making settings.

Finally, the fourth seminar will be an exciting keynote delivered by the current Deputy U.S. CTO Ed Felten on uses of machine learning in government.

I believe the talks will be recorded.


We will begin next semester with the second Fels sponsored workshop, organized between computer science and economics -- a 2 day intensive workshop exploring current research on technical and economic solutions to addressing unfairness in decision making. More details to come.

Below is the schedule for this semester:

The “Optimizing Government” interdisciplinary research collaboration, supported by the Fels Policy Research Initiative, will hold the following workshops this fall:

Thursday, 9/22/16: What is Machine Learning (and Why Might it be Unfair)?Fundamentals of machine learning with a focus on what makes it different from traditional statistical analysis and why it might lead to unfair outcomes.Speakers: Aaron Roth (Penn Computer Science), with comments from Richard Berk (Wharton Statistics; Chair of SAS Criminology)

Thursday, 10/6/16: What Does Fair and Equal Treatment Demand?

Current legal and moral norms about fairness and equal protection as they relate to the use of machine learning in government.

Speakers: Panel featuring Samuel Freeman (Penn Philosophy), Nancy Hirschmann (Penn Political Science), and Seth Kreimer (Penn Law)

Thursday, 11/3/16: Fairness and Performance Trade-Offs in Machine Learning

Technical solutions to fairness challenges raised by machine learning and their impacts on algorithm effectiveness.

Speaker: Jamie Morgenstern (Penn Computer Science)

Thursday, 11/17/16: Keynote on Machine Learning and Government

How to use machine learning for a variety of administrative and policy functions, and findings from a White House initiative on artificial intelligence in government.

Speaker: Ed Felten, Deputy U.S. CTO (Invited)

Each workshop will take place from 4:30-6:00 pm in Gittis 213 (Penn Law). You can enter the Law School through its main entrance at 3501 Sansom Street.

EDIT: The Optimizing Government Project now has a website: https://www.law.upenn.edu/institutes/ppr/optimizing-government-project/ and the talks will be livestreamed here: https://www.law.upenn.edu/institutes/ppr/optimizing-government-project/media.php

Call for Papers: Second Workshop on Adaptive Data Analysis

As part of NIPS 2016, we will be running the second annual workshop on adaptive data analysis. Last year's workshop was a big hit. As a new addition this year, we are soliciting submitted contributions in addition to invited speakers. The call for papers is below. If you are working on relevant work, definitely submit it to our workshop! More information at: http://wadapt.org/

Call for Papers


The overall goal of WADAPT is to stimulate the discussion on theoretical analysis and practical aspects of adaptive data analysis. We seek contributions from different research areas of machine learning, statistics and computer science. Submissions focused on a particular area of application are also welcome.

Submissions will undergo a lightweight review process and will be judged on originality, relevance, clarity, and the extent to which their presentation can stimulate the discussion between different communities at the workshop. Submissions may describe either novel work (completed or in progress), or work already published or submitted elsewhere provided that it first appeared after September 1, 2015.

Authors are invited to submit either a short abstract (2-4 pages) or a complete paper by Oct 15, 2016. Information about previous publication, if applicable, should appear prominently on the first page of the submission. Abstracts must be written in English and be submitted as a single PDF file at EasyChair.

All accepted abstracts will be presented at the workshop as posters and some will be selected for an oral presentation. The workshop will not have formal proceedings, and presentation at the workshop is not intended to preclude later publication at another venue.

Those who need to receive a notification before the NIPS early registration deadline (Oct 6, 2016) should submit their work by the early submission deadline of Sept 23, 2016.

Important Dates:


Submission deadlines. Early: Sep 23, 2016; Regular: Oct 25, 2016. Submit at EasyChair.
Notification of acceptance. Early: Oct 3, 2016, Regular: Nov 7, 2016.
Workshop: December 9, 2016



Specific topics of interest for the workshop include (but are not limited to):


Selective/post-selection inference
Sequential/online false discovery rate control
Algorithms for answering adaptively chosen data queries
Computational and statistical barriers to adaptive data analysis
Stability measures and their applications to generalization
Information-theoretic approaches to generalization

Machine Learning Postdoc

My brand new colleague Shivani Agarwal is in the market for a postdoc; the announcement is below. One of the targeted areas is machine learning and economics. Whoever takes this position will join a growing group of exceptional postdocs in this area at Penn, including Jamie Morgenstern and Bo Waggoner.

Postdoctoral Position in Machine Learning at UPenn

Applications are invited for a postdoctoral position in machine learning  in the Department of Computer and Information Science at the University  of Pennsylvania. The position is expected to begin in Fall 2016, and is  for a period of up to two years (with renewal in the second year  contingent on performance in the first year). Applications in all areas  of machine learning will be considered, with special emphasis on the  following areas: ranking and choice modeling; connections between machine learning and economics; and learning of complex structures.

The ideal candidate will demonstrate both ability for independent  thinking and interest in co-mentoring of graduate students. The candidate will work primarily with Shivani Agarwal (joining UPenn faculty in July 2016), but will also have opportunities to collaborate with other faculty in machine learning and related areas at UPenn, including Michael Kearns, Daniel Lee, Sasha Rakhlin, Aaron Roth, Lyle Ungar, and other faculty.

UPenn is located in the vibrant city of Philadelphia, which is known for its rich culture, history, museums, parks, and restaurants. It is less than 1.5 hrs by train to NYC, 1.5 hrs by flight to Boston, 2 hrs by train to Washington DC, and 40 mins by train to Princeton. For more details about the CIS department at UPenn, see:

http://www.cis.upenn.edu/

To apply, send the following materials in an email titled “Application for Postdoctoral Position” to  by June 17, 2016:

- curriculum vitae
- 2-page statement of research interests and goals
- 3 representative publications or working papers
- 3 letters of recommendation (to be sent separately by the same date)

Shortlisted candidates will be invited for a short meeting/interview at ICML/COLT in NYC during June 23-26 (in your email, please indicate your availability for this).

Fairness in Learning

The very real problem of (un)fairness in algorithmic decision making in general, and machine learning in particular seems to have finally reached the forefront of public attention. Every day there is a new popular article about the topic. Just in the last few weeks, we have seen articles in the Times about built in bias in facebook, and an in-depth ProPublica study about racial bias in statistical models for predicting criminal recidivism. Earlier this month, the White House released a report on the challenges in promoting fairness in Big Data.

The tricky thing is saying something concrete and technical about this problem -- even defining what "fairness" is is delicate. There has been some good technical work in this area that I have long admired from afar -- see e.g. the "FATML" (Fairness and Transparency in Machine Learning) Workshop to get an idea of the range of work being done, and the folks doing it. People like Cynthia Dwork, Moritz Hardt, Solon Barocas, Suresh Venkatasubramanian, Sorelle Friedler, Cathy O'Neil, and others have been doing important work thinking about these problems for quite some time. A particularly nice early paper that I recommend everyone interested in the area read is Fairness Through Awareness, by Dwork, Hardt, Pitassi, Reingold, and Zemel. It was first posted online in 2011(!), and in retrospect is quite prescient in its discussion of algorithmic fairness.

So I'm happy to finally have something interesting to say about the topic! My student Matthew Joseph, Michael Kearns, Jamie Morgenstern, and I just posted a new paper online that I'm excited about: Fairness in Learning: Classic and Contextual Bandits. I'll mostly let the paper speak for itself, but briefly, we write down a simple but (I think) compelling definition of fairness in a stylized general model of sequential decision making called the "contextual bandit setting". To keep a canonical problem in your mind, imagine the following: There are a bunch of different populations (say racial or socioeconomic groups), and you are a loan officer. Every day, an individual from each population applies for a loan. You get to see the loan application for each person (this is the "context"), and have to decide who to give the loan to. When you give out the loan, you observe some reward (e.g. you see if they paid back the loan), but you don't see what reward you -would- have gotten had you given the loan to someone else. Our fairness condition says roughly that an algorithm is "fair" if it never preferentially gives a loan to a less qualified applicant over a more qualified applicant -- where the quality of an applicant in our setting is precisely the probability that they pay back the loan. (It prohibits discriminating against qualified applicants on an individual basis -- even if  they happen to come from a population that is less credit-worthy on average, or from a population that the bank doesn't understand as well).

It might seem like this definition of fairness is entirely consistent with the profit motivation of a bank -- why would a bank ever want to give a loan to an applicant less likely to pay it back? Indeed, this would be true if the bank had nothing to learn -- i.e. if it already knew the optimal rule mapping loan applications to credit-worthiness. Said another way, implementing the optimal policy is entirely consistent with our fairness definition. Our main conceptual message is that fairness can nevertheless be an obstruction to learning the optimal policy.

What our results say is that "fairness" always has a cost in terms of the optimal learning rate achievable by algorithms in this setting. For some kinds of problems, the cost is mild in that the cost of fairness on the learning rate is only polynomial (e.g. when credit-worthiness is determined by a simple linear regression model on the features of a loan application). On the other hand, for other kinds of problems, the cost of fairness on the learning rate is severe, in that it can slow learning by an exponential factor (e.g. when credit-worthiness is determined by an AND of features in the loan application). Put another way, for the problems in which the cost of fairness is severe, if the bank were to use a fast learning algorithm (absent a fairness constraint), the algorithm might be "unfair" for a very long time, even if in the limit, once it learned the truly optimal policy, it would eventually be fair. One friction to fairness is that we don't live in the limit -- we are always in a state of learning.

Penn is Hiring in Computer Science

Penn Engineering is embarking on a period of substantial growth, and the computer science department plans to hire lots of people over the next several years. (You can see a draft of the Penn Engineering plan for growth here. "Data Science and Computation" and Security are both priority areas).

This year, we are planning to hire for multiple positions, both junior and senior, with a focus on (among other things), machine learning. 

So if you are on the market (or thinking about it), send us your application -- apply here: https://facultysearches.provost.upenn.edu/postings/663 

(Unless you want to apply for an endowed chair in computer graphics -- then apply here: https://facultysearches.provost.upenn.edu/postings/664 )

Edit: I forgot to mention! Interest in machine learning is university wide -- both statistics and ESE also plan to hire in machine learning.
Here is the link for applying to the statistics position: https://statistics.wharton.upenn.edu/recruiting/facultypositions/
And here is the link for ESE: http://www.ese.upenn.edu/faculty-staff/index.php

An action packed Monday at FCRC

FCRC begins next week, and due to the wonders of co-location will be action packed. (Monday will have among other things EC workshops, as well as the first full day of STOC talks). I've found myself on the organizing committee of two of the three EC workshops, and Monday is also when our STOC paper is scheduled.

So here are three (partially) conflicting events you might be interested in:

NetEcon 2015: Highlights include excellent keynote speakers: Rakesh Vohra at  9:00 am, Eva Tardos at 2:00 pm, and R. Srikant at 4:00 pm

The Workshop on Algorithmic Game Theory and Data Science: This workshop is highlighting work in a very exciting area -- the intersection between "data science" and mechanism design -- which is at its very beginning, and I think ripe for lots of important work to be done. Its also attracted a great lineup of speakers (my student Steven will be giving the talk on our paper).

The STOC presentation of our paper, Preserving Statistical Validity in Adaptive Data Analysis: This is at 1:55pm, unfortunately conflicting with NetEcon -- but the attendees of the AGT + Data Science workshop will get a break in order to attend. Vitaly will be giving the talk, which I expect will be very good.

If you want to hear a longer (but probably less good) version of the talk, you can tune in tomorrow at 1pm eastern when I talk about this paper for TCS+

EDIT: The video of the talk is here. It went off with only one technical hitch.

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/

---------------------------

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

Netecon deadline in two weeks

A reminder that the NetEcon workshop deadline is coming up in two weeks. If you plan to be at FCRC (for e.g. STOC, or EC, or Sigmetrics), this will be a great place to present your work and get it seen by both the EC and the SIGmetrics community. There's also a great lineup of invited talks (abstracts here: http://netecon.eurecom.fr/NetEcon2015/keynotes.html ) by R. Srikant, Rakesh Vohra, and Eva Tardos.

Here is the call: http://netecon.eurecom.fr/NetEcon2015/index.html

The submission deadline is April 22.

STOC 2015 Call for Workshops and Tutorials

Sanjeev Khanna and Chandra Chekuri are co-chairing the STOC 2015 workshops and tutorials, and are looking for submissions. The call is below:

Call for STOC 2015 Workshop and Tutorial Proposals

• Workshop and Tutorial Day: Sunday, June 14, 2015
• Workshop and Tutorial Co-Chairs: Chandra Chekuri and Sanjeev Khanna
• Submission deadline: March 20, 2015
• Notification: March 30, 2015

On Sunday, June 14, immediately preceding the main conference, STOC 2015 will hold a workshop-and-tutorials day. We invite groups of interested researchers to submit workshop or tutorial proposals. The goal of a workshop is to provide an informal forum for researchers to discuss important research questions, directions, and challenges. Connections between theoretical computer science and other areas, topics that are not well represented at STOC, and open problems are encouraged as workshop topics. Organizers are completely free to choose their workshop formats (invited speakers, panel discussions, etc.). The program for June 14th may also involve tutorials, each consisting of 1-2 survey talks on a particular area, and we welcome tutorial proposals as well.
STOC does not have funds to pay travel expenses or honoraria to invited workshop and tutorial speakers. Workshop and tutorials attendance will be free, and there is no separate registration for attending them. STOC will support coffee breaks for the workshops/tutorials attendees but no lunch will be provided. Note that STOC registration neither includes FCRC registration for Sunday, June 14 nor covers lunch for Sunday, June 14. Workshop/tutorial attendees who wish to attend another FCRC conference on June 14, would need to register for that conference.

Proposal submission

Workshop and tutorial proposals should be no longer than 2 pages. Please include a list of names and email addresses of the organizers, a description of the topic and the goals of the workshop or tutorial, the proposed workshop format (invited talks, contributed talks, panel, etc.), and proposed or tentatively confirmed speakers if known. Please also indicate the preferred length of time for your workshop or tutorial, along with the minimum acceptable time. We anticipate a 4-5 hour block for each workshop and a 2-4 hour block for each tutorial. Please feel free to contact the Workshop and Tutorial Co-Chairs at the email addresses below if you have questions about workshop or tutorial proposals.

Submission deadline

Proposals should be submitted by March 20, 2015, via email to chekuri@illinois.edu and sanjeev@cis.upenn.edu. Proposers will be notified by March 30, 2015, about whether their proposals have been accepted.

http://acm-stoc.org/stoc2015/callforworkshops.html

Bringing Differential Privacy to the Masses <3

This Valentines Day, we'll be bringing differential privacy to the (scientific) masses, with a session at the AAAS annual meeting in San Jose.

If you happen to be attending, you should stop by: https://aaas.confex.com/aaas/2015/webprogram/Session9556.html

NetEcon 2015

Patrick Loiseau, Adam Wierman, and I are co-chairing the 2015 NetEcon workshop, to be held in conjunction with EC and Sigmetrics at this year's FCRC in Portland. You should stop by if you are attending any of the FCRC conferences, (and submit a paper). If you haven't been to a previous iteration, this workshop brings together people interested in game theory from both the networking and theory/AI communities, so is a great place to present work that might be of interest to both communities, or to pick up problems that are interesting from a different community than yours.

We've got a great set of invited speakers: R. Srikant, Ricky Vohra, and Eva Tardos.

So start thinking about what you want to submit -- you've only got about 3 months.

*******************************************************************
Call For Papers:

         NetEcon 2015: The 10th Workshop on the Economics of Networks, 
Systems and Computation
         At FCRC 2015, in conjunction with ACM EC and ACM SIGMETRICS
         Monday, June 15, 2015 (Portland, Oregon, USA)

http://netecon.eurecom.fr/NetEcon2015/


*******************************************************************
INVITED SPEAKERS

     * *R. Srikant*, University of Illinois at Urbana-Champaign
     * *Rakesh V. Vohra*, University of Pennsylvania
     * *Eva Tardos*, Cornell University


*******************************************************************
CALL FOR PAPERS

Today's communication networks and networked systems are highly complex 
and heterogeneous, and are often owned by multiple profit-making 
entities. For new technologies or infrastructure designs to be adopted, 
they must not be only based on sound engineering performance 
considerations but also present the right economic incentives. Recent 
changes in regulations of the telecommunication industry make such 
economic considerations even more urgent. For instance, concerns such as 
network neutrality have a significant impact on the evolution of 
communication networks.

At the same time, communication networks and networked systems support 
increasing economic activity based on applications and services such as 
cloud computing, social networks, and peer-to-peer networks. These 
applications pose new challenges such as the development of good pricing 
and incentive mechanisms to promote effective system-wide behavior. In 
relation to these applications, security and privacy also require 
consideration of economic aspects to be fully understood.

The aim of NetEcon is to foster discussions on the application of 
economic and game-theoretic models and principles to address challenges 
in the development of networks and network-based applications and 
services. NetEcon was established in 2006 (succeeding to the P2PECON, 
IBC and PINS workshops) and merged with the W-PIN workshop in 2013. We 
invite submission of extended abstracts describing original research on 
theoretical/methodological contributions or on applications to cases of 
interest. It is our hope that NetEcon will serve as a feeder workshop, 
i.e., that expanded, polished versions of extended abstracts will appear 
later in major conference proceedings and refereed journals of relevant 
research communities.

Topics of interest include (but are not limited to):

     * Pricing of resources in communication networks, grids, and cloud 
computing
     * Pricing of information goods and services; copyright issues, 
effect of network externalities (e.g., in social network)
     * Economic issues in universal broadband access; economics of 
interconnection and peering
     * Effects of market structure and regulations (e.g., network 
neutrality)
     * Economics of network security and privacy; valuation of personal data
     * Auctions with applications to networks: spectrum auctions, 
auction-based marketplaces for network and cloud resources
     * Incentive mechanisms for networks: peer-to-peer systems, clouds, 
wireless networks, spam prevention, security
     * Methods for engineering incentives and disincentives (e.g., 
reputation, trust, control, accountability, anonymity)
     * Empirical studies of strategic behavior (or the lack thereof) in 
existing, deployed systems
     * Design of incentive-aware network architectures and protocols
     * Game-theoretic models and techniques for network economics: large 
games, learning, mechanism design, interaction of game theory and 
information theory or queuing theory, information exchange, diffusion, 
dynamics of cooperation and network formation, trades in social and 
economic networks
     * Algorithmic mechanism design for network systems
     * Critiques of existing models and solution concepts, as well as 
proposals of better models and solution concepts
     * Studies of open collaboration, peer production, crowdsourcing, 
and human computation.


*******************************************************************
SUBMISSION FORMATTING GUIDELINES AND PROCEEDINGS

Submissions must be in the form of extended abstracts of 3-4 pages, 
including all figures, tables, references, appendices, etc. They must be 
formatted according to the standard alternate ACM PER double column 
format using letter paper. You are encouraged to use the ACM 
sig-alternate-per latex template 
(http://www.sigmetrics.org/sig-alternate-per.cls).

Accepted extended abstracts will be published in a special issue of ACM 
Performance Evaluation Review (PER) and will be available online through 
ACM portal digital library. Authors of accepted abstracts grant ACM 
permission to publish them in print and digital formats.

Note that authors retain the copyright of their work published in ACM 
PER, with freedom to submit it elsewhere. Yet, authors for whom 
publication of a 3-4 pages extended abstract in the NetEcon 2015 
proceedings would preclude later publication of an expanded version in 
the relevant venue may elect to contribute only a one-page abstract of 
their submitted extended abstract to the NetEcon 2015 proceedings. Such 
an abstract should include the URL of a working paper or preprint that 
contains the main results presented at the NetEcon workshop. Authors 
will make this decision after receiving a notice of acceptance.

If the number of excellent submissions is larger than we have space to 
allot presentations for, some authors will be offered the opportunity to 
present their work during a poster session.


*******************************************************************
COMMITTES

PC CHAIRS
     Patrick Loiseau (EURECOM, France)
     Aaron Roth (UPenn, USA)
     Adam Wierman (Caltech, USA)

WEBMASTER
     Michela Chessa (EURECOM, France)

TECHNICAL PROGRAM COMMITTEE
     Matthew Andrews (Alcatel-Lucent Bell Labs, USA)
     Itai Ashlagi (MIT, USA)
     Moshe Babaioff (Microsoft Research, Israel)
     Tamer BaÅŸar (University of Illinois, Urbana-Champaign, USA)
     Bobby Bhattarcharjee (University of Maryland, USA)
     Rainer Böhme (WWU Münster, Germany)
     Kostas Bimpikis (Stanford University, USA)
     Eilyan Bitar (Cornell University, USA)
     Augustin Chaintreau (Columbia University, USA)
     Michela Chessa (EURECOM, France)
     kc claffy (CAIDA / UC San Diego, USA)
     Costas Courcoubetis (SUTD, Singapore and AUEB, Greece)
     Amogh Dhamdhere (CAIDA / UC San Diego, USA)
     Constantine Dovrolis (GeorgiaTech, USA)
     Rachid Elazouzi (University of Avignon, France)
     Sergey Gorinsky (Institute IMDEA Networks, Spain)
     Jens Grossklags (The Pennsylvania State University, USA)
     Roch Guerin (Washington University in St. Louis, USA)
     Nidhi Hegde (Alcatel-Lucent Bell Labs, France)
     Ekram Hossain (University of Manitoba, Canada)
     Stratis Ioannidis (Yahoo! labs, USA)
     Krisnamurthy Iyer (Cornell University, USA)
     Rahul Jain (USC, USA)
     Ian Kash (Microsoft Research, UK)
     David Kempe (USC, USA)
     Peter Key (Microsoft Research, UK)
     Nikolaos Laoutaris (Telefonica Research, Spain)
     Dave Levin (University of Maryland, USA)
     Patrick Loiseau (EURECOM, France) -- co-chair
     Brendan Lucier (Microsoft Research, USA)
     John C. S. Lui (The Chinese University of Hong Kong, Hong Kong)
     Patrick Maillé (Institut Mines-Telecom / Telecom Bretagne, France)
     Jason Marden (University of Colorado, Boulder, USA)
     Ravi Mazumdar (University of Waterloo, Canada)
     Jeonghoon Mo (Yonsei University, South Korea)
     John Musacchio (UC Santa Cruz, USA)
     Andrew Odlyzko (University of Minnesota, Minneapolis, USA)
     Aaron Roth (UPenn, USA) -- co-chair
     Galina Schwartz (UC Berkeley, USA)
     Paul G. Spirakis (University of Liverpool, UK and CTI, Greece)
     Nicolás Stier Moses (Facebook Data Science, USA)
     Vijay Subramanian (University of Michigan, Ann Arbor, USA)
     John N. Tsitsiklis (MIT, USA)
     Bruno Tuffin (Inria, France)
     Adrian Vetta (McGill University, Canada)
     Steven Weber (Drexel University, USA)
     Adam Wierman (Caltech, USA) -- co-chair


*******************************************************************
IMPORTANT DATES

     * Wednesday April 22, 2015, 11:59pm PST: Submission deadline (firm)
     * Wednesday May 13, 2015: Notification to authors
     * Monday June 8, 2015: Final version for the workshop's website due
     * Monday June 15, 2015: Workshop in Portland
     * Monday July 13, 2015: Final version for the ACM PER proceedings due


*******************************************************************
ADDITIONAL INFORMATION

For more information, please contact the organizers or visit the 
workshop website: http://netecon.eurecom.fr/NetEcon2015/.

Differential Privacy Workshop in London

There will be a differential privacy workshop in London in April, accepting submissions soon. If you have something you are working on, consider submitting here. Some highlights:

-- Although the workshop will not have proceedings (so you can as usual submit a talk and still submit it to a conference of your choosing), exceptional submissions will be invited to a special issue of the Journal of Privacy and Confidentiality.

-- The PC is pretty inter-disciplinary. This is certainly not just a Theory-A workshop. Work on privacy in all areas is on topic.

-- You get to hear Jon Ullman give a (presumably excellent) talk. 

CALL FOR PAPERS
TPDP 2015
First workshop on the Theory and Practice of Differential Privacy
18th April 2015, London, UK
Affiliated to ETAPS
http://tpdp.computing.dundee.ac.uk

Differential privacy is a promising approach to the privacy-preserving
release of data: it offers a strong guaranteed bound on the increase
in harm that a user incurs as a result of participating in a
differentially private data analysis.

Researchers in differential privacy come from several area of computer
science as algorithms, programming languages, security, databases,
machine learning, as well as from several areas of statistics and data
analysis. The workshop is intended to be an occasion for researchers
from these different research areas to discuss the recent developments
in the theory and practice of differential privacy.

**Submissions**

The overall goal of TPDP is to stimulate the discussion on the
relevance of differentially private data analyses in practice. For
this reason, we seek contributions from different research areas of
computer science and statistics.

Authors are invited to submit a short abstract (4-5 pages maximum) of
their work by January 23, 2015. Abstracts must be written in English
and be submitted as a single PDF file at the EasyChair page for TPDP:
https://easychair.org/conferences/?conf=3Dtpdp2015

Submissions will be judged on originality, relevance, interest and
clarity. Submission should describe novel works or works that have
already appeared elsewhere but that can stimulate the discussion
between the different communities. Accepted abstracts will
be presented at the workshop.

The workshop will not have formal proceedings, but we plan to have a
special issue of the Journal of Privacy and Confidentiality devoted to
TPDP. Authors presenting valuable contributions at the workshop will
be invited to submit a journal version of their work right after the
workshop.


**Important Dates**

-January 23, 2015 - Abstract Submission
-February 10, 2015 - Notification
-February 14, 2015 - Deadline early registration ETAPS
-April 18, 2015 - Workshop

-May 15, 2015 Deadline for journal special issue


**Topics**

Specific topics of interest for the workshop include (but are not limited t=
o):
theory of differential privacy,
verification techniques for differential privacy,
programming languages for differential privacy,
models for differential privacy,
trade-offs between privacy protection and analytic utility,
differential privacy and surveys,
relaxations of the differential privacy definition,
differential privacy vs other privacy notions and methods,
differential privacy and accuracy,
practical differential privacy,
implementations for differential privacy,
differential privacy and security,
applications of differential privacy.


**Invited Speakers**

Jonathan Ullman - Simons Fellow at Columbia University,

Another invited speaker joint with HotSpot'15 to be confirmed.


**Program Committee**

Gilles Barthe - IMDEA Software
Konstantinos Chatzikokolakis - CNRS and LIX, Ecole Polytechnique
Kamalika Chaudhuri - UC San Diego
Graham Cormode - University of Warwick
George Danezis - University College London
Marco Gaboardi - University of Dundee
Matteo Maffei - CISPA, Saarland University
Catuscia Palamidessi - INRIA and LIX, Ecole Polytechnique
Benjamin C. Pierce - University of Pennsylvania
Aaron Roth - University of Pennsylvania
David Sands - Chalmers University of Technology
Chris Skinner - London School of Economics
Adam Smith - Pennsylvania State University
Carmela Troncoso - Gradiant
Salil Vadhan - Harvard University

Differential Privacy Book is Here

After a much longer time than either of us thought it would take, my book with Cynthia Dwork, "The Algorithmic Foundations of Differential Privacy" is finally available.

You have 3 options for obtaining a copy! (I must admit to not quite understanding the pricing model of our publisher).

1. Hard Copy: You can buy a hard copy directly from NOW for $99 (http://www.nowpublishers.com/articles/foundations-and-trends-in-theoretical-computer-science/TCS-042/book-details) or from Amazon for $101 and free shipping (http://www.amazon.com/Algorithmic-Foundations-Differential-Privacy/dp/1601988184/ )
2. "ebook" format (which I believe is just a downloadable pdf): If you don't have room on your book shelf, and are happy with a PDF, the book can be yours from NOW for only $240. This is a bargain -- coming in at 281 pages, this is less than 86 cents per digital "page".   (http://www.nowpublishers.com/articles/foundations-and-trends-in-theoretical-computer-science/TCS-042/book-details)
3. Free download: The PDF is also available for free on my web page: http://www.cis.upenn.edu/~aaroth/privacybook.html

Lecture 12 -- Privacy Yields an Anti-Folk Theorem in Repeated Games

Last week, Kobbi Nissim gave us an excellent guest lecture on differential privacy and machine learning. The semester has gone by fast -- this week is our last lecture in the privacy and mechanism design class. (But stop by next week to hear the students present their research projects!)

Today we'll talk about infinitely repeated games. In an infinitely repeated game, n players repeatedly, in an infinite number of stages, play actions and obtain payoffs based on some commonly known stage game. Since the game is infinitely repeated, in order to make sense of players total payoff, we employ a discount factor delta that specifies how much less valuable a dollar is tomorrow compared to a dollar today. (delta is some number in [0, 1) ). In games of perfect monitoring, players perfectly observe what actions each of their opponents have played in past rounds, but in large n player games, it is much more natural to think about games of imperfect monitoring, in which agents see only some noisy signal of what their opponents have played.

For example, one natural signal players might observe in an anonymous game is a noisy histogram estimating what fraction of the population has played each type of action. (This is the kind of signal you might get if you see a random subsample of what people play -- for example, you have an estimate of how many people drove on each road on the way to work today by looking at traffic reports). Alternately, there may be some low dimensional signal (like the market price of some good) that everyone observes that is computed as a randomized function of everyone's actions today (e.g. how much of the good each person produced).

A common theme in repeated games of all sorts are folk theorems. Informally, these theorems state that in repeated games, we should expect a huge multiplicity of equilibria, well beyond the equilibria we would see in the corresponding one-shot stage game. This is because players observe each other's past behavior, and so can threaten each other to behave in prescribed ways or else face punishment. Whether or not a folk theorem is a positive result or a negative result depends on whether you want to design behavior, or predict behavior. If you are a mechanism designer, a folk theorem might be good news -- you can try and encourage equilibrium behavior that has higher welfare than any equilibrium of the stage game. However, if you want to predict behavior, it is bad news -- there are now generically a huge multiplicity of very different equilibria, and some of them have much worse welfare than any equilibrium of the stage game.

In this lecture (following a paper joint with Mallesh Pai and Jon Ullman) we argue that:

1. In large games, many natural signaling structures produce signal distributions that are differentially private in the actions of the players, where the privacy parameters tends to 0 as the size of the game gets large, and
2. In any such game, for any discount factor delta, as the size of the game gets large, the set of equilibria of the repeated game collapse to the set of equilibria of the stage game. In other words, there are no "folk theorem equilibria" -- only the equilibria that already existed in the one shot game. 

This could be interpreted in a couple of ways. On the one hand, this means that in large games, it might be harder to sustain cooperation (which is a negative result). On the other hand, since it shrinks the set of equilibria, it means that adding noise to the signaling structure in a large game generically improves the price of anarchy over equilibria of the repeated game, which is a positive result. 

Lecture 10 -- Running Ascending Price Auctions that Make Sincere Bidding an Ex-Post Dominant Strategy

In the 10th lecture in our privacy and mechanism design class, we consider the problem of running an ascending price auction. An ascending price auction is just a generalization of what you normally see as an "auction" on TV -- rather than submitting your valuation in some kind of one-shot protocol, the prices of the goods gradually rise, and you take turns with other bidders making bids on the goods as a function of the current prices.

Why would you want to run such an auction when the VCG mechanism already can provide welfare optimal outcomes for every social choice function, while making truthful reporting a dominant strategy? People quote a couple of reasons:

1. It might be hard to actually report your full valuation: in principle, you need to figure out exactly your value for every bundle you might receive, and its difficult to pin down a number. In an ascending price auction, all you need to do is be able to point to your favorite good (or bundle of goods) that you would buy if the current prices were the final prices, which is often an easier task. 
2. An ascending price auction can end without you having to reveal your full type. For example, in a single item second price auction, the highest bidder never has to reveal (even to the auctioneer) his value for the good -- only that it is higher than that of the second highest bidder. Hence, people might prefer such auctions for "privacy" reasons. 

In an ascending price auction, "truthful" reporting doesn't make sense, since nobody ever asks you to report your type. But we can ask for "sincere bidding", in which bidders truthfully bid on the item at each round that is their favorite, given the current prices. But there is a problem: we typically can't implement sincere bidding as a dominant strategy, because of the problem of threats. Consider the following simple example:

Suppose we have two unit demand bidders 1 and 2, and two goods for sale a and b. We have v_{1,a} = 1, v_{1,b} = epsilon and v_{2,a} = 1/2, v_{2, b} = 1/2 - \epsilon. Suppose moreover that bidder 2 takes the following strategy: "Bid on good a. If bidder 1 bids on good a, then outbid him on whatever he bids on until the price is > 1.'' Against this strategy, bidder 1 cannot obtain non-negative utility if he bids on his favorite good (a), and so his best response is to place an insincere bid on good 2. Moreover, bidder 2 has a clear motivation to take this threatening position -- he obtains substantially higher payoff than if players followed sincere bidding, since he gets his most preferred good without any competition. As a result of instances like these, typically ascending price auctions can implement sincere bidding at best as an (ex-post) Nash equilibirum. 

In this lecture, we talk about how to implement an ascending auction such that the prices are differentially private in the bidding strategies of the players (and the allocation in the end is jointly differentially private). This fixes two of the problems above:

1. The privacy guaranteed by the ascending price auction is no longer hand-wavy and qualitative, but rather precise and quantitative. 
2. We get sincere bidding as an asymptotic ex-post dominant strategy for all players.

To get this result, we need only a mild large-market assumption: that the "supply" of each good is modestly large compared to the number of different types of goods -- but crucially we need to assume nothing about how bidder preferences are generated. 

The intuition, which we will appeal to again later, is that by running the auction privately, we have eliminated the possibility that players can distort incentives by threatening each other.

Lecture 9 -- Purchasing Private Data from Privacy Sensitive Individuals

Yesterday in our privacy and mechanism design course, we were fortunate to have a guest lecture by David Xiao. David told us his exciting recent paper, with Kobbi Nissim and Salil Vadhan, Redrawing the Boundaries on Purchasing Private Data from Privacy-Sensitive Individuals.

Consider the following scenario: An analyst wishes to conduct some medical study about an underlying population, but needs to obtain permission from each individual whose data he uses. On the one hand, he needs to buy data from a representative sample of the population so that his study is accurate. On the other hand, he needs to compensate individuals for their privacy costs, and would like to come up with a payment scheme that incentivizes them to report their true privacy costs, rather than inflating them for selfish gain. Finally, he wants the mechanism to be individually rational: that no rational agent should obtain negative utility by interacting with the analyst.

Because individual's costs for privacy are a function of the method by which their reports are used to compute the outcome of the mechanism, rather than just a function of the outcome itself, this takes us outside of a standard mechanism design setting. What makes the problem tricky is that individual's costs for privacy could quite plausibly be correlated with their private data. Suppose the analyst wishes to estimate the fraction of people in some population who have syphilis. It is reasonable to expect that syphilitics will on the whole want to be compensated more than healthy individuals for a loss of privacy. But this means that even computations on agents reported costs for privacy (and independent of agent's supposedly private data) can lead to privacy loss for those agents, and so must be compensated.

Some years ago Arpita Ghosh and I studied this problem, and showed an impossibility result when making some (unreasonably) strong assumptions. One might have hoped that our result could be circumvented with one of several tweaks to the model. But no. David and his coauthors extend this impossibility result to have much wider applicability, making fewer assumptions on what the analyst is able to observe, and far fewer assumptions about the form of the privacy loss function of the agents. Their result is quite robust: Under extremely general circumstances, no truthful individually rational mechanism which makes finite payments can distinguish between two populations, in one of which everyone has syphilis, and in the other of which nobody does. This result says that no mechanism can simultaneously enjoy truthfulness, individual rationality, and non-trivial accuracy properties, and so without drastically relaxing the model of how people might value privacy, you must always give up on one of these.

They do propose one such relaxation, which seems to reduce to something like the assumption that contracting syphilis can only ever cause your costs for privacy to increase, never to decrease. But this is probably not the last word. I think that convincing answers for what to do in the face of their impressive impossibility result are still to be proposed, and is a really interesting question.