Incentivizing exploration

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Human Interaction with Recommendation Systems: On Bias and Exploration (2017, background): “Therefore, there has recently been an increased interest in understanding this trade-off in the presence of myopic agents, who are naturally interested in doing well for themselves, rather than helping the server learn Frazier et al. [2014], Mansour et al. [2015, 2016], Papanastasiou et al. [2014]. ”
Human Interaction with Recommendation Systems (2017, methodology): “, 2013] by using payments [Frazier et al., 2014] or by the way the system disseminates information [Papanastasiou et al.”
Introduction to Multi-Armed Bandits (2019, background): “ical models: time-discounted rewards (Bimpikis et al., 2018); continuous information ﬂow and a continuum of agents (Che and Horner, 2018); incentivizing exploration using money (the fundamental paper Frazier et al. (2014)¨ and an extension to heterogenous agents Chen et al. (2018)); the agents must ”
Fairness Incentives for Myopic Agents (2017, background): “[6] explore the achievable set between the monetary rewards the principal must pay to incentivize exploration and the time discounted expected reward to the principal.”