# Ranked Prioritization of Groups in Combinatorial Bandit Allocation

arXiv: https://arxiv.org/abs/2205.05659 · HTML: https://mlsanity.com/paper/2205.05659/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 16 of 16)

- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Bandits with concave rewards and convex knapsacks](https://mlsanity.com/paper/1402.5758/) (2014)
- [Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms](https://mlsanity.com/paper/1407.8339/) (2014)
- [Combinatorial Sleeping Bandits with Fairness Constraints](https://mlsanity.com/paper/1901.04891/) (2019)
- [Fairness of Exposure in Stochastic Bandits](https://mlsanity.com/paper/2103.02735/) (2021)
- [Fair Algorithms for Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.06699/) (2020)
- [Dual-Mandate Patrols: Multi-Armed Bandits for Green Security](https://mlsanity.com/paper/2009.06560/) (2020)
- [Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback](https://mlsanity.com/paper/1909.01504/) (2019)
- [Group Fairness in Bandit Arm Selection](https://mlsanity.com/paper/1912.03802/) (2019)
- [Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting](https://mlsanity.com/paper/2106.07677/) (2021)
- [Robust Reinforcement Learning Under Minimax Regret for Green Security](https://mlsanity.com/paper/2106.08413/) (2021)
- [Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations](https://mlsanity.com/paper/1903.06669/) (2019)
- [Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making](https://mlsanity.com/paper/2109.08792/) (2021)
- [Dynamic Ad Allocation: Bandits with Budgets](https://mlsanity.com/paper/1306.0155/) (2013)

## Built on by (most cited first, 3 of 3)

- [Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact](https://mlsanity.com/paper/2412.07880/) (2024)
- [Reflections from the Workshop on AI-Assisted Decision Making for Conservation](https://mlsanity.com/paper/2307.08774/) (2023)
- [Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous Agents](https://mlsanity.com/paper/2408.03405/) (2024)

## Abstract (arXiv)

Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land. Combinatorial bandits have been used to allocate limited patrol resources, but existing approaches overlook the fact that each location is home to multiple species in varying proportions, so a patrol benefits each species to differing degrees. When some species are more vulnerable, we ought to offer more protection to these animals; unfortunately, existing combinatorial bandit approaches do not offer a way to prioritize important species. To bridge this gap, (1) We propose a novel combinatorial bandit objective that trades off between reward maximization and also accounts for prioritization over species, which we call ranked prioritization. We show this objective can be expressed as a weighted linear sum of Lipschitz-continuous reward functions. (2) We provide RankedCUCB, an algorithm to select combinatorial actions that optimize our prioritization-based objective, and prove that it achieves asymptotic no-regret. (3) We demonstrate empirically that RankedCUCB leads to up to 38% improvement in outcomes for endangered species using real-world wildlife conservation data. Along with adapting to other challenges such as preventing illegal logging and overfishing, our no-regret algorithm addresses the general combinatorial bandit problem with a weighted linear objective.