# Dynamic Task Allocation for Crowdsourcing Settings

## TL;DR

This paper introduces a dynamic task allocation method for crowdsourcing that maximizes confidence in answers by optimally assigning workers to tasks, improving accuracy and efficiency over existing approaches.

## Contribution

It proposes a mutual information-based stochastic subset selection approach for optimal worker-task assignment in crowdsourcing.

## Key findings

- Higher accuracy achieved with fewer labels.
- Outperforms previous methods sensitive to user-question ratios.
- Effective in boosting crowdsourcing estimation algorithms.

## Abstract

We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers. Such an optimized worker assignment method allows us to boost the efficacy of any popular crowdsourcing estimation algorithm. We consider a mutual information interpretation of the crowdsourcing problem, which leads to a stochastic subset selection problem with a submodular objective function. We present experimental simulation results which demonstrate the effectiveness of our dynamic task allocation method for achieving higher accuracy, possibly requiring fewer labels, as well as improving upon a previous method which is sensitive to the proportion of users to questions.