Summary of Influencing Bandits: Arm Selection For Preference Shaping, by Viraj Nadkarni and D. Manjunath and Sharayu Moharir
Influencing Bandits: Arm Selection for Preference Shaping
by Viraj Nadkarni, D. Manjunath, Sharayu Moharir
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents research on non-stationary multi-armed bandits, which involve shaping population preferences to maximize the favorability of a predetermined arm. The study focuses on binary opinions and two types of opinion dynamics: decreasing elasticity (modeled as a Polya urn with increasing balls) and constant elasticity (using the voter model). It introduces Explore-then-commit and Thompson sampling policies for decreasing elasticity, analyzing regret for each. The analysis is shown to carry over to the constant elasticity case, and a Thompson sampling-based algorithm is proposed for multiple opinion types. Additionally, the abstract discusses a trade-off between popularity and opinion shaping objectives when multiple recommendation systems are present. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to shape people’s opinions to favor a specific choice or option. It looks at how people’s preferences change over time based on what they see happening around them. The researchers propose different strategies for achieving this, such as exploring new ideas and then committing to the best one. They test these approaches and show that they work well in different situations. The study also considers how multiple sources of information can influence opinions and highlights a trade-off between making recommendations popular and shaping people’s preferences. |
