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Over the past decade, crowdsourcing has emerged as a cheap and efficientmethod of obtaining solutions to simple tasks that are difficult for computersto solve but possible for humans. The popularity and promise of crowdsourcingmarkets has led to both empirical and theoretical research on the design ofalgorithms to optimize various aspects of these markets, such as the pricingand assignment of tasks. Much of the existing theoretical work on crowdsourcingmarkets has focused on problems that fall into the broad category of onlinedecision making; task requesters or the crowdsourcing platform itself makerepeated decisions about prices to set, workers to filter out, problems toassign to specific workers, or other things. Often these decisions are complex,requiring algorithms that learn about the distribution of available tasks orworkers over time and take into account the strategic (or sometimes irrational)behavior of workers.  As human computation grows into its own field, the time is ripe to addressthese challenges in a principled way. However, it appears very difficult tocapture all pertinent aspects of crowdsourcing markets in a single coherentmodel. In this paper, we reflect on the modeling issues that inhibittheoretical research on online decision making for crowdsourcing, and identifysome steps forward. This paper grew out of the authors' own frustration withthese issues, and we hope it will encourage the community to attempt tounderstand, debate, and ultimately address them.  The authors welcome feedback for future revisions of this paper.

Online Decision Making in Crowdsourcing Markets: Theoretical Challenges (Position Paper)