RAIN schedule for Spring Quarter 2014-15
The RAIN seminar is held on Wednesdays from 12:00-1:00pm in Y2E2 101. And yes, lunch is provided!
|April 8||Robert Kleinberg||Incentivizing Exploration|
|April 15||Alex Dimakis||TBA|
|May 20||Ozan Candogan||Optimal Contracts for Intermediaries in Online Advertising|
|May 27||Anindya Ghose||Randomized Field Experiments in Mobile Marketing|
Google Calendar for RAIN
Previous year's talks
Archived talks can be accessed here.
Talk AbstractsIncentivizing Exploration
Bobby Kleinberg, Cornell
An important recent theme in the development of on-line social systems is the potential of crowdsourced effort to solve large problems — defining tasks in which many people can each contribute a small amount of time to the overall goal. In some cases the arrangement is based on a direct compensation scheme, in which a (low) rate is paid for each unit of work performed. But in many settings one only has access to a crowd "in the wild", as they go about their everyday activities. Examples include product recommendations, social news readers, and scientific activities ranging from crowdsourced "citizen science" to the funding of research by national agencies.
In all of these domains, there is a problem of misaligned incentives: the designer's goal is to carry out exploration (of the space of news stories, products, bird habitats, etc.) as efficiently as possible, but for reasons of scale they must implement the exploration via a crowd composed of members who each derive their own utility from participating in the exploration. We model this as a multi-armed bandit problem in which selfish, myopic agents pull arms with publicly observable outcomes, and a principal seeking to maximize the cumulative time-discounted reward may influence the agents by offering monetary (or other) rewards contingent on choosing particular actions. Our main result is a full characterization of the trade-off between the expected payments the principal must make and the total reward that can be achieved.
Joint work with Peter Frazier, David Kempe, and Jon Kleinberg.
Bio: Bobby Kleinberg is an Associate Professor of Computer Science at Cornell University. His research studies the design and analysis of algorithms, and their relations to economics, learning theory, and networks. Prior to receiving his doctorate from MIT in 2005, he spent three years at Akamai Technologies, where he assisted in designing the world's largest Internet Content Delivery Network. He is the recipient of a Microsoft Research Faculty Fellowship, an Alfred P. Sloan Foundation Fellowship, and an NSF CAREER Award.
Alex Dimakis, UT Austin
Bio: Alex Dimakis is an Assistant Professor at the Electrical and Computer Engineering department, University of Texas at Austin. From 2009 until 2012 he was with the Viterbi School of Engineering, University of Southern California. He received his Ph.D. in 2008 and M.S. degree in 2005 in electrical engineering and computer sciences from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. During 2009 he was a CMI postdoctoral scholar at Caltech. He received an NSF Career award in 2011, a Google faculty research award in 2012 and the Eli Jury dissertation award in 2008. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012. He is currently serving as an associate editor for IEEE Signal Processing letters. His research interests include information theory, coding theory, signal processing, and networking, with a current focus on distributed storage and machine learning.
Optimal Contracts for Intermediaries in Online Advertising
Ozan Candogan, Duke Fuqua Business School
In online display advertising, the prevalent method advertisers employ to acquire impressions is to contract with an intermediary. These contracts involve upfront payments made by the advertisers to the intermediary, in exchange for running campaigns on their behalf. This paper studies the optimal contract offered by the intermediary in a setting where advertisers’ budgets and targeting criteria are private. This problem can naturally be formulated as a multi-dimensional dynamic mechanism design problem, which in general is hard to solve. We tackle this problem by employing a novel performance space characterization technique, which relies on delineating the expected cost and value achievable by any feasible (dynamic) bidding policy. This technique provides a convex optimization formulation of the optimal contract design problem. Using this formulation, we obtain a closed-form characterization of the optimal contract, when advertisers have identical value distributions. Conversely, when advertisers are heterogeneous, we provide a duality-based approach, which reduces the optimal contract design problem to a simpler convex optimization problem. The intermediary’s bid in the optimal contract is obtained by first using the optimal dual solution to compute a weighted average of the values associated with different types (to guarantee that the advertiser reports her type truthfully), and then shading this quantity (to account for budget constraints). Our results indicate that an intermediary can profitably provide bidding service to a budget-constrained advertiser, and at the same time increase the overall market efficiency.
Joint work with Santiago Balseiro (Duke University).
Bio: Ozan Candogan is an assistant professor at the Fuqua School of Business, where he is a member of the Decision Sciences area. Prior to joining Fuqua, he received his Ph.D. and M.S. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. His research interests are in mechanism design, decision making in social and economic networks, and analysis of dynamic strategic interactions. His most recent work focuses on the design of novel iterative auction mechanisms for efficient allocation of resources, and he was a finalist in the 2013 George Nicholson Student Paper Competition. Potential applications of his research include novel auction formats that can be employed for procurement, pricing and advertising mechanisms that optimally utilize available social network information, and pricing mechanisms for selling cloud computing service. His research has appeared in journals such as Management Science, Operations Research, Mathematics of Operations Research, and Games and Economic Behavior. He is also a recipient of the 2012 Microsoft Research Ph.D. fellowship, and 2009 Siebel Scholarship.
Randomized Field Experiments in Mobile Marketing
Anindya Ghose, NYU Stern
The explosive growth of smartphones and location-based services (LBS) has contributed to the rise of mobile advertising. In this talk, we present results from multiple randomized field experiments that are designed to measure the effectiveness of mobile marketing and promotions. In the first experiment, using data from a location based app for smartphones, we measure the effectiveness of mobile coupons. The aim is to analyze the causal impact of geographical distance between a user and retail store, the display rank, and coupon discounts on consumers’ response to mobile coupons. In a second large scale field study we examine the role of contextual crowdedness on the redemption rates of mobile coupons. We find that people become increasingly engaged with their mobile devices as trains get more crowded, and in turn become more likely to respond to targeted mobile messages. The change in behavior can be accounted for by the phenomenon of “mobile immersion”: to psychologically cope with the loss of personal space in crowded trains and to avoid accidental gazes, commuters can escape into their personal mobile space. In turn, they become more involved with targeted mobile messages they receive, and, consequently, are more likely to make a purchase in crowded trains. These studies causally show that mobile advertisements based on real-time static geographical location and contextual information can significantly increase consumers’ likelihood of redeeming a geo-targeted mobile coupon. However, beyond the location and contextual information, the overall mobile trajectory of each individual consumer can provide even richer information about consumer preferences. In a third study, we propose a new mobile advertising strategy that leverages full information on consumers’ offline moving trajectories. To examine the effectiveness of this new mobile trajectory-based advertising strategy, we designed a large-scale randomized field experiment in one of the largest shopping malls in the world. We find that mobile trajectory-based advertising can lead to highest redemption probability, fastest redemption behavior, and highest satisfaction rate from customers at the focal advertising store. Various practical implications for mobile marketing are discussed.
Bio: Anindya Ghose is a Professor of IT and a Professor of Marketing at New York University's Leonard N. Stern School of Business. He is the co-Director of the Center for Business Analytics at NYU Stern. He is the Robert L.& Dale Atkins Rosen Faculty Fellow and a Daniel P. Paduano Fellow of Business Ethics at NYU Stern. He has been a Visiting Associate Professor at the Wharton School of Business. He also serves as the Scientific Advisor to 3TI China. He was recently named by Business Week as one of the "Top 40 Professors Under 40 Worldwide" and by Analytics Week as one the "Top 200 Thought Leaders in Big Data and Business Analytics". His research has been recognized by 10 best paper awards and nominations. He has published more than 75 papers in premier scientific journals and peer reviewed conferences, and has given more than 200 talks internationally. His research has been profiled and he has been interviewed numerous times in the BBC, Bloomberg TV, CNBC, China Daily, Financial Times, Fox News, Forbes, Knowledge@Wharton, Korean Broadcasting News Company, Los Angeles Times, MSNBC, NBC, New York Times, New York Daily, National Public Radio, NHK Japan Broadcasting, Reuters, Time Magazine, Washington Post, Wall Street Journal, Xinhua, and elsewhere.