The content discusses the application of bandit algorithms in shaping population preferences by influencing user opinions through rewards. It covers different opinion dynamics models, explores optimal policies for preference shaping, and introduces algorithms like Explore-then-Commit and Thompson Sampling. The analysis extends to contextual bandits, non-stationary rewards, and the influence of multiple recommendation systems on popularity and opinion shaping objectives.
The authors present theoretical results, including regret bounds and optimal policy formulations, along with simulations to demonstrate the performance of these algorithms in various scenarios. They also discuss extensions to N-arm bandits and competing recommendation systems with opposing objectives.
Overall, the content provides a comprehensive overview of bandit algorithms for preference shaping in dynamic environments.
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by Viraj Nadkar... a las arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00036.pdfConsultas más profundas