Implementing the “Wisdom of the Crowd”

By Ilan Kremer, Yishay Mansour, Motty Perry. Journal of Political Economy, 2013

418 citations recorded by the source corpus.

We study a novel mechanism design model in which agents each arrive sequentially and choose one action from a set of actions with unknown rewards. The information revealed by the principal affects the incentives of the agents to explore and generate new information. We characterize the optimal disclosure policy of a planner whose goal is to maximize social welfare. One interpretation of our result is the implementation of what is known as the “wisdom of the crowd.” This topic has become increasingly relevant with the rapid spread of the Internet over the past decade.

Subfields: Management Science and Operations Research, Safety Research

View Paper: https://www.semanticscholar.org/paper/864e31c51ab7aa409442b74a0350209a9e19f48b

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Citations in the corpus

Introduction to Multi-Armed Bandits (2019, methodology): “t surprisingly rich. Various aspects of this model have been studied: Deterministic rewards: The special case of deterministic rewards and two arms has been optimally solved in the original paper of Kremer et al. (2014). That is, they design a BIC algorithm which exactly optimizes the expected rewar”
Fairness Incentives for Myopic Agents (2017, background): “[9], a more general exploration in Mansour et al.”
Incentivizing exploration (2014, methodology): “A similar motivation to ours was considered in [Kremer et al. 2013].”
Human Interaction with Recommendation Systems: On Bias and Exploration (2017, positive): “eck and Cesa-Bianchi, 2012]. In particular, there has been prior work on human interaction with multi-armed bandit algorithms: for example, how a system can optimally induce myopic agents to explore [Kremer et al., 2013] by using payments [Frazier et al., 2014] or by the way the system disseminates ”
Human Interaction with Recommendation Systems (2017, methodology): “In particular, there has been prior work on human interaction with multi-armed bandit algorithms: for example, how a system can optimally induce myopic agents to explore [Kremer et al., 2013] by using payments [Frazier et al.”
Markets for Information: An Introduction (2018, background): “Levin and Milgrom (2010) discuss this issue in terms of splits and conflation of product categories.”