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 \emph{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 policies: independent focus groups, group aggregators, and interlaced information structures.
Bio
Nicole's research lies broadly within the field of economics and computation. 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. Her areas of specialty include social networks and mechanism design. Nicole received her Ph.D. from MIT in Cambridge, MA in 2005 and then completed three years of postdocs at both Microsoft Research in Redmond, WA and CWI in Amsterdam, Netherlands before accepting a job as an assistant professor at Northwestern University in Chicago, IL in 2008. She joined Microsoft Research in 2012.
