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Algorithmic Management for Improving Collective Productivity in Crowdsourcing.

Han Yu1, Chunyan Miao2,3, Yiqiang Chen4,5

  • 1Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University, Singapore, 639798, Singapore. han.yu@ntu.edu.sg.

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Summary
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The surprise-minimization-value-maximization (SMVM) approach enhances crowdsourcing efficiency by optimizing worker assignments. This novel method, using a worker desirability index (WDI), significantly boosts collective productivity and outperforms human decisions.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Operations Research

Background:

  • Crowdsourcing systems face challenges in worker assignment due to numerous strategies and dynamic worker behaviors.
  • Maximizing social welfare in these complex systems is an NP-hard problem.

Purpose of the Study:

  • To propose a novel approach, surprise-minimization-value-maximization (SMVM), for optimizing worker assignment in crowdsourcing.
  • To enhance collective productivity and social welfare in dynamic crowdsourcing environments.

Main Methods:

  • Developed a worker desirability index (WDI) considering worker reputation, workload, and motivation.
  • Implemented the SMVM approach to provide real-time guidance for workers, balancing individual benefits with collective productivity.
  • Utilized polynomial-time algorithms with proven asymptotic bounds to a theoretical optimum.

Main Results:

  • High-resolution simulations on real-world data showed SMVM significantly outperformed existing state-of-the-art methods.
  • A 3-year empirical study with 1,144 participants demonstrated SMVM exceeded human task delegation decisions in over 80% of cases under typical workloads.
  • The approach proved effective in improving collective productivity and worker outcomes.

Conclusions:

  • The SMVM approach offers an efficient and effective solution for complex crowdsourcing task delegation.
  • Results support the development of scalable, data-driven algorithmic management decision support systems for crowdsourcing.
  • This research provides a practical framework for improving crowdsourcing system performance and worker engagement.