A Data-Driven Game Theoretic Strategy for Developers in Software Crowdsourcing: A Case Study

Zhifang Liao, Zhi Zeng, Yan Zhang, Xiaoping Fan

Research output: Contribution to journalArticle

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Abstract

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward.
Original languageEnglish
Article number721
Number of pages16
JournalApplied Sciences
Volume9
Issue number4
DOIs
Publication statusPublished - 19 Feb 2019

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Keywords

  • software crowdsourcing
  • reward
  • game
  • Topcoder.com

Cite this

Liao, Zhifang ; Zeng, Zhi ; Zhang, Yan ; Fan, Xiaoping. / A Data-Driven Game Theoretic Strategy for Developers in Software Crowdsourcing: A Case Study. In: Applied Sciences. 2019 ; Vol. 9, No. 4.
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A Data-Driven Game Theoretic Strategy for Developers in Software Crowdsourcing: A Case Study. / Liao, Zhifang; Zeng, Zhi; Zhang, Yan; Fan, Xiaoping.

In: Applied Sciences, Vol. 9, No. 4, 721, 19.02.2019.

Research output: Contribution to journalArticle

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