  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 balancing resources as
crowdsourcing occurs. Experiments with real and synthetic crowdsourcing data
show that cost forecasting leads to improved accuracy. Accuracy and efficiency
gains for crowd-generated microtasks hold the promise to further leverage the
creativity and wisdom of the crowd, with applications such as generating more
informative and diverse training data for machine learning applications and
improving the performance of user-generated content and question-answering
platforms.
