Efficient crowdsourcing of crowd-generated microtasks
Abigail Hotaling, James P. Bagrow

TL;DR
This paper introduces cost forecasting to efficiently manage a growing set of crowd-generated microtasks, balancing resource use and improving accuracy in crowdsourcing applications.
Contribution
It proposes a novel cost forecasting method enabling efficient crowdsourcing with an expanding task set, addressing limitations of fixed-task algorithms.
Findings
Cost forecasting improves task completion accuracy.
The method balances resource allocation between new and existing tasks.
Experiments demonstrate enhanced efficiency and accuracy.
Abstract
Allowing members of the crowd to propose novel microtasks for one another is an effective way to combine the efficiencies of traditional microtask work with the inventiveness and hypothesis generation potential of human workers. However, microtask proposal leads to a growing set of tasks that may overwhelm limited crowdsourcer resources. Crowdsourcers can employ methods to utilize their resources efficiently, but algorithmic approaches to efficient crowdsourcing generally require a fixed task set of known size. In this paper, we introduce *cost forecasting* as a means for a crowdsourcer to use efficient crowdsourcing algorithms with a growing set of microtasks. Cost forecasting allows the crowdsourcer to decide between eliciting new tasks from the crowd or receiving responses to existing tasks based on whether or not new tasks will cost less to complete than existing tasks, efficiently…
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