# MADDM: Multi-Advisor Dynamic Binary Decision-Making by Maximizing the Utility

arXiv: https://arxiv.org/abs/2305.08664 · HTML: https://mlsanity.com/paper/2305.08664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 6 of 6)

- [AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers](https://mlsanity.com/paper/1909.04121/) (2019)
- [Thompson Sampling for Budgeted Multi-armed Bandits](https://mlsanity.com/paper/1505.00146/) (2015)
- [Budget-Constrained Bandits over General Cost and Reward Distributions](https://mlsanity.com/paper/2003.00365/) (2020)
- [Budget-Constrained Multi-Armed Bandits with Multiple Plays](https://mlsanity.com/paper/1711.05928/) (2017)
- [Stochastic Collection and Replenishment (SCAR) Optimisation for Persistent Autonomy](https://mlsanity.com/paper/1603.01932/) (2016)
- [Multi-trainer Interactive Reinforcement Learning System](https://mlsanity.com/paper/2210.08050/) (2022)

## Abstract (arXiv)

Being able to infer ground truth from the responses of multiple imperfect advisors is a problem of crucial importance in many decision-making applications, such as lending, trading, investment, and crowd-sourcing. In practice, however, gathering answers from a set of advisors has a cost. Therefore, finding an advisor selection strategy that retrieves a reliable answer and maximizes the overall utility is a challenging problem. To address this problem, we propose a novel strategy for optimally selecting a set of advisers in a sequential binary decision-making setting, where multiple decisions need to be made over time. Crucially, we assume no access to ground truth and no prior knowledge about the reliability of advisers. Specifically, our approach considers how to simultaneously (1) select advisors by balancing the advisors' costs and the value of making correct decisions, (2) learn the trustworthiness of advisers dynamically without prior information by asking multiple advisers, and (3) make optimal decisions without access to the ground truth, improving this over time. We evaluate our algorithm through several numerical experiments. The results show that our approach outperforms two other methods that combine state-of-the-art models.