CS Talk
Nicole Immorlica
Host: Yang Cai
Title: Incentivizing Exploration with Selective Data Disclosure
Abstract:
We study the design of rating systems that incentivize efficient social learning. Agents arrive sequentially and choose actions, each of which yields a reward drawn from an unknown distribution. A policy maps the rewards of previously-chosen actions to messages for arriving agents. The regret of a policy is the difference, over all rounds, between the expected reward of the best action and the reward induced by the policy. Prior work proposes policies that recommend a single action to each agent, obtaining optimal regret under standard rationality assumptions. We instead assume a frequentist behavioral model and, accordingly, restrict attention to disclosure policies that use messages consisting of the actions and rewards from a subsequence of past agents, chosen ex ante. We design a policy with optimal regret in the worst case over reward distributions. Our research suggests three components of effective polices: independent focus groups, group aggregators, and interlaced information structures.
Joint work with Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu.
Bio:
Nicole Immorlica is a senior principal researcher at Microsoft Research New England (MSR NE) where she leads the economics and computation group. She is also chair of SIGecom, the ACM Special Interest Group on Economics and Computation, which fosters world-class research in this interdisciplinary field through conferences, awards, and mentorship programs. She received her BS in 2000, MEng in 2001 and PhD in 2005 in theoretical computer science from MIT in Cambridge, MA. She joined MSR NE in 2012 after completing postdocs at Microsoft in Redmond, WA and Centruum vor Wiskunde en Informatics (CWI) in Amsterdam, Netherlands, and a professorship in computer science at Northwestern University. Nicole’s research interest is in the design and operation of sociotechnical systems. Using tools and modeling concepts from both theoretical computer science and economics, Nicole hopes to explain, predict, and shape behavioral patterns in various online and offline systems, markets, and games. She is known for her work on social networks, matching markets, and mechanism design. She is the recipient of a number of fellowships and awards including the Sloan Fellowship, the Microsoft Faculty Fellowship and the NSF CAREER Award. She has been on several boards including SIGACT, the Game Theory Society, and OneChronos; is an associate editor of Operations Research and Transactions on Economics and Computation, and was program committee member and chair for several ACM, IEEE and INFORMS conferences in her area.