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CrowdManager - Combinatorial allocation and pricing of crowdsourcing tasks with time constraints


Minder, Patrick; Seuken, Sven; Bernstein, Abraham; Zollinger, Mengia (2012). CrowdManager - Combinatorial allocation and pricing of crowdsourcing tasks with time constraints. In: Workshop on Social Computing and User Generated Content in conjunction with ACM Conference on Electronic Commerce (ACM-EC 2012) , Valencia, Spain, 7 June 2012 - 7 June 2012, 1-18.

Abstract

Crowdsourcing markets like Amazon’s Mechanical Turk or Crowdflower are quickly growing in size and popularity. The allocation of workers and compensation approaches in these markets are, however, still very simple. In particular, given a set of tasks that need to be solved within a specific time constraint, no mechanism exists for the requestor to (a) find a suitable set of crowd workers that can solve all of the tasks within the time constraint, and (b) find the “right” price to pay these workers. In this paper, we provide a solution to this problem by introducing CrowdManager – a framework for the combinatorial allocation and pricing of crowdsourcing tasks under budget, completion time, and quality constraints. Our main contribution is a mechanism that allocates tasks to workers such that social welfare is maximized, while obeying the requestor’s time and quality constraints. Workers’ payments are computed using a VCG payment rule. Thus, the resulting mechanism is efficient, truthful, and individually rational. To support our approach we present simulation results that benchmark our mechanism against two baseline approaches employing fixed-priced mechanisms. The simulation results illustrate that our mechanism (i) significantly reduces the requestor’s costs in the majority of settings and (ii) finds solutions in many cases where the baseline approaches either fail or significantly overpay. Furthermore, we show that the allocation as well as VCG payments can be computed in a few seconds, even with hundreds of workers and thousands of tasks.

Abstract

Crowdsourcing markets like Amazon’s Mechanical Turk or Crowdflower are quickly growing in size and popularity. The allocation of workers and compensation approaches in these markets are, however, still very simple. In particular, given a set of tasks that need to be solved within a specific time constraint, no mechanism exists for the requestor to (a) find a suitable set of crowd workers that can solve all of the tasks within the time constraint, and (b) find the “right” price to pay these workers. In this paper, we provide a solution to this problem by introducing CrowdManager – a framework for the combinatorial allocation and pricing of crowdsourcing tasks under budget, completion time, and quality constraints. Our main contribution is a mechanism that allocates tasks to workers such that social welfare is maximized, while obeying the requestor’s time and quality constraints. Workers’ payments are computed using a VCG payment rule. Thus, the resulting mechanism is efficient, truthful, and individually rational. To support our approach we present simulation results that benchmark our mechanism against two baseline approaches employing fixed-priced mechanisms. The simulation results illustrate that our mechanism (i) significantly reduces the requestor’s costs in the majority of settings and (ii) finds solutions in many cases where the baseline approaches either fail or significantly overpay. Furthermore, we show that the allocation as well as VCG payments can be computed in a few seconds, even with hundreds of workers and thousands of tasks.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:7 June 2012
Deposited On:10 Jul 2012 08:50
Last Modified:19 Aug 2017 21:03
Free access at:Official URL. An embargo period may apply.
Official URL:http://yiling.seas.harvard.edu/sc2012/Minder_et_al_CrowdManager_SCUGC_2012.pdf
Related URLs:http://www.sigecom.org/ec12/
Other Identification Number:merlin-id:7030

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