- Jacob LaRiviere, Microsoft
- Vijay Vazirani, University of California Irvine
- Adam Wierman, California Institute of Technology
Keynote 1: How do Developer Networks Impact Open Source Project Success? Jacob laRiviere, Microsoft
Abstract: We use a novel panel dataset combining StackOverflow data with Github data to explain how open source projects become more or less successful. We circumvent historical difficulties in measuring open source project popularity by leveraging Stack Overflow pageview data. We also leverage Github telemetry data to build two developer networks and use features of the networks, among many other features, to predict subsequent success. We find network characteristics explain a large amount of variation in open source project success. We then add structure to the network formation model of developers to identify externalities associated with specific developer decisions and offer data driven insights for how the open source community can increase the amount of useful open source projects through improved labor supply decisions. Lastly we discuss ongoing research which leverages quasi-experimental variation in the size of the developer pool to infer causal relationships of developer decisions on open source project success.
Jacob laRiviere is a senior research economist at Microsoft working in
Michael Schwarz's group. His main research interests are
Industrial Organization, Environmental & Public Economics, and Behavioral
Economics. He uses applied theory to inform microeconometric and experimental
empirical techniques. Recently he has been doing research in
cloud computing and markets and economic decisions characterized by
externalities like electricity markets, public good provision and
open source software development. He is an affiliate faculty in the
Economics Department and Evans School of Public Policy at University of
Washington and adjunct assistant professor of Economics at University
of Tennessee, where he is also a Fellow for Energy and Environmental Policy
at the Baker Center for Public Policy.
Keynote 2: Google's Adwords Market: How Theory Influenced Practice. Vijay V. Vazirani, CS Department, University of California, Irvine
Prof. Vazirani will talk about an optimal online algorithm
for Google's Adwords market, obtained over a decade ago when
the algorithmic and economic/game-theoretic issues of this
marketplace were just being understood.
Their result addresses a central algorithmic issue underlying
this marketplace: how to match query keywords
to advertisers so as to maximize Google's revenue.
He will give an overview of the novel LP-based techniques that led to this result and the simple heuristic, of bid scaling, that is suggested by their algorithm. He will also give a formal framework for thinking about budgeted auctions more generally, and these ideas have been widely adopted by Google and other search engine companies.
Purely theoretical work on the online bipartite matching problem greatly benefitted our work. The latter problem appears to have no applications whatsoever! On the other hand, the multi-billion dollar online ad industry has become the key source of revenues for several Internet companies. For algorithms designers, this a very satisfying story of practical impact from rich theory.
Vijay Vazirani received his BS from MIT,
his Ph.D. from the University of California, Berkeley, and he is currently
Distinguished Professor at the University of California, Irvine.
He has made seminal contributions to the theory of algorithms, in particular to the classical maximum matching problem,
approximation algorithms, and complexity theory.
Over the last decade and a half, he has contributed widely
to an algorithmic study of economics and game theory. The latter includes an online algorithm for Google's Adwords market
which has had massive impact in online ad allocation.
Vijay Vazirani is also the author of a definitive book on Approximation Algorithms, published in 2001, and translated into Japanese, Polish, French and Chinese.
He was McKay Fellow at U. C. Berkeley in Spring 2002, and Distinguished SISL Visitor at Caltech during 2011-12.
He is a Guggenheim Fellow and an ACM Fellow.
Keynote 3: Transparency and Control in Platforms & Networked Markets, Adam Wierman, Caltech
Abstract: Platforms have emerged as a powerful economic force, driving both traditional markets, like the electricity market, and emerging markets, like the sharing economy. The power of platforms comes from their ability to tame the complexities of networked marketplaces -- marketplaces where there is not a single centralized market, but instead a network of interconnected markets loosely defined by a graph of feasible exchanges. Despite the power and prominence of platforms, the workings of platforms are often guarded secrets. Further, many competing platforms make very different design choices, but little is understood about the impact of these differing choices. In this talk, he will overview recent work from his group that focuses on reverse engineering the design of platforms and understanding the consequences of design choices underlying transparency in modern platforms. Adam will use electricity markets and ridesharing services as motivating examples throughout.
Biography: Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences at the California Institute of Technology, where he currently serves as Executive Officer. His research interests center around resource allocation and scheduling decisions in computer systems and services. He received the 2011 ACM SIGMETRICS Rising Star award, the 2014 IEEE Communications Society William R. Bennett Prize, and has been coauthor on papers that received of best paper awards at ACM SIGMETRICS, IEEE INFOCOM, IFIP Performance, IEEE Green Computing Conference, IEEE Power & Energy Society General Meeting, and ACM GREENMETRICS.