# DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks

arXiv: https://arxiv.org/abs/2608.11873 · HTML: https://mlsanity.com/paper/2608.11873/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 12 Aug 2026.

## Builds on (most cited first, 23 of 23)

- [Cascading Bandits: Learning to Rank in the Cascade Model](https://mlsanity.com/paper/1502.02763/) (2015)
- [Delay and Cooperation in Nonstochastic Bandits](https://mlsanity.com/paper/1602.04741/) (2016)
- [Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback](https://mlsanity.com/paper/1409.8428/) (2014)
- [Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithms](https://mlsanity.com/paper/1606.00911/) (2016)
- [Cascading Bandits for Large-Scale Recommendation Problems](https://mlsanity.com/paper/1603.05359/) (2016)
- [Combinatorial Cascading Bandits](https://mlsanity.com/paper/1507.04208/) (2015)
- [Online Clustering of Contextual Cascading Bandits](https://mlsanity.com/paper/1711.08594/) (2017)
- [Multiplayer bandits without observing collision information](https://mlsanity.com/paper/1808.08416/) (2018)
- [DCM Bandits: Learning to Rank with Multiple Clicks](https://mlsanity.com/paper/1602.03146/) (2016)
- [Thompson Sampling Algorithms for Cascading Bandits](https://mlsanity.com/paper/1810.01187/) (2018)
- [Multiplayer Bandit Learning, from Competition to Cooperation](https://mlsanity.com/paper/1908.01135/) (2019)
- [Online Learning for Cooperative Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2109.03818/) (2021)
- [Multi-Player Bandits: The Adversarial Case](https://mlsanity.com/paper/1902.08036/) (2019)
- [Federated Combinatorial Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2405.05950/) (2024)
- [Fatigue-aware Bandits for Dependent Click Models](https://mlsanity.com/paper/2008.09733/) (2020)
- [Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communication](https://mlsanity.com/paper/2311.06210/) (2023)
- [A Survey of Real-World Recommender Systems: Challenges, Constraints, and Industrial Perspectives](https://mlsanity.com/paper/2509.06002/) (2025)
- [Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits](https://mlsanity.com/paper/2408.04332/) (2024)
- [Cascading Reinforcement Learning](https://mlsanity.com/paper/2401.08961/) (2024)
- [Multiplayer Information Asymmetric Bandits in Metric Spaces](https://mlsanity.com/paper/2503.08004/) (2025)
- [Multiplayer Information Asymmetric Contextual Bandits](https://mlsanity.com/paper/2503.08961/) (2025)
- [Measurement and applications of position bias in a marketplace search engine](https://mlsanity.com/paper/2206.11720/) (2022)
- [Cascading Bandits Robust to Adversarial Corruptions](https://mlsanity.com/paper/2502.08077/) (2025)

## Abstract (arXiv)

In this work, we extend the Dependent Click Model (DCM) Bandits to a multiplayer information-asymmetric setting, where multiple agents interact with a shared ranked list and may observe multiple clicks per session, introducing new challenges for selection strategies. We study asymmetry in (1) actions and (2) rewards, providing sublinear regret guarantees for three settings where at least one asymmetry is present. Establishing matching information-theoretic lower bounds for these settings is left as an open problem. We further show that for small termination probabilities, the termination ranking need not be known, improving on prior single-agent results. Experiments confirm that our algorithms perform well across asymmetric environments and highlight the critical role of feedback structure, specifically the distinction between full versus first-click feedback, in coordinating exploration and minimizing regret.