Incentivizing Exploration with Selective Data Disclosure

• URL: http://arxiv.org/abs/1811.06026v7
• Date: Wed, 13 Nov 2024 01:28:11 GMT
• Title: Incentivizing Exploration with Selective Data Disclosure
• Authors: Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu,
• Abstract summary: We propose and design recommendation systems that incentivize efficient exploration. Agents arrive sequentially, choose actions and receive rewards, drawn from fixed but unknown action-specific distributions. We attain optimal regret rate for exploration using a flexible frequentist behavioral model.
• Score: 70.11902902106014
• License:
• Abstract: We propose and design recommendation systems that incentivize efficient exploration. Agents arrive sequentially, choose actions and receive rewards, drawn from fixed but unknown action-specific distributions. The recommendation system presents each agent with actions and rewards from a subsequence of past agents, chosen ex ante. Thus, the agents engage in sequential social learning, moderated by these subsequences. We asymptotically attain optimal regret rate for exploration, using a flexible frequentist behavioral model and mitigating rationality and commitment assumptions inherent in prior work. We suggest three components of effective recommendation systems: independent focus groups, group aggregators, and interlaced information structures.

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