Optimal Design for Social Learning
Yeon-Koo Che,Johannes Hörner +1 more
TLDR
This paper studies the design of a recommender system for organizing social learning on a product and finds that fully transparent recommendations may become optimal if a (socially-benevolent) designer does not observe the agents’ costs of experimentation.Abstract:
This paper studies the design of a recommender system for organizing social learning on a product. To improve incentives for early experimentation, the optimal design trades off fully transparent social learning by over-recommending a product (or “spamming”) to a fraction of agents in the early phase of the product cycle. Under the optimal scheme, the designer spams very little about a product right after its release but gradually increases the frequency of spamming and stops it altogether when the product is deemed sufficiently unworthy of recommendation. The optimal recommender system involves randomly triggered spamming when recommendations are public—as is often the case for product ratings—and an information “blackout” followed by a burst of spamming when agents can choose when to check in for a recommendation. Fully transparent recommendations may become optimal if a (socially-benevolent) designer does not observe the agents’ costs of experimentation.read more
Citations
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Proceedings ArticleDOI
Social Learning and the Innkeeper's Challenge
TL;DR: Inspired by the classical multi-armed bandit model, this work provides the central planner with a recommendation algorithm that is (almost) incentive compatible and facilitates social learning for the setting where each agent observes his predecessor.
Posted Content
Competing Bandits: The Perils of Exploration under Competition.
TL;DR: It is found that stark competition induces firms to commit to a "greedy" bandit algorithm that leads to low consumer welfare, but it is also found that weakening competition by providing firms with some "free" consumers incentivizes better exploration strategies and increases consumer welfare.
Proceedings ArticleDOI
Optimal Algorithm for Bayesian Incentive-Compatible Exploration
Lee Cohen,Yishay Mansour +1 more
TL;DR: In this paper, the authors consider a social planner faced with a stream of myopic selfish agents and present an optimal algorithm for the planner, in the case that the actions realizations are deterministic and have limited support.
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Greedy Algorithm almost Dominates in Smoothed Contextual Bandits.
TL;DR: This work improves on prior results to show that a greedy approach almost matches the best possible Bayesian regret rate of any other algorithm on the same problem instance whenever the diversity conditions hold, and that this regret is at most $\tilde O(T^{1/3})$.
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Bayesian Exploration with Heterogeneous Agents
TL;DR: This work considers Bayesian Exploration: a simple model in which the recommendation system (the “principal”) controls the information flow to the users and strives to incentivize exploration via information asymmetry, and allows heterogeneous users.
References
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