# Incentivizing an Unknown Crowd

arXiv: https://arxiv.org/abs/2109.04226 · HTML: https://mlsanity.com/paper/2109.04226/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Crowdsourced Judgement Elicitation with Endogenous Proficiency](https://mlsanity.com/paper/1303.0799/) (2013)
- [Informed Truthfulness in Multi-Task Peer Prediction](https://mlsanity.com/paper/1603.03151/) (2016)
- [Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing](https://mlsanity.com/paper/1408.1387/) (2014)
- [Learning to Incentivize: Eliciting Effort via Output Agreement](https://mlsanity.com/paper/1604.04928/) (2016)
- [Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing](https://mlsanity.com/paper/1806.00206/) (2018)
- [Sequential Peer Prediction: Learning to Elicit Effort using Posted Prices](https://mlsanity.com/paper/1611.09219/) (2016)
- [Approval Voting and Incentives in Crowdsourcing](https://mlsanity.com/paper/1502.05696/) (2015)
- [Truthful Information Elicitation from Hybrid Crowds](https://mlsanity.com/paper/2107.10119/) (2021)

## Abstract (arXiv)

Motivated by the common strategic activities in crowdsourcing labeling, we study the problem of sequential eliciting information without verification (EIWV) for workers with a heterogeneous and unknown crowd. We propose a reinforcement learning-based approach that is effective against a wide range of settings including potential irrationality and collusion among workers. With the aid of a costly oracle and the inference method, our approach dynamically decides the oracle calls and gains robustness even under the presence of frequent collusion activities. Extensive experiments show the advantage of our approach. Our results also present the first comprehensive experiments of EIWV on large-scale real datasets and the first thorough study of the effects of environmental variables.