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In the distributed implementation of SMVM, the time complexity can be as low as O(1).Through analysis, we show that the performance of SMVM asymptotically approaches that of a theoretical optimal task allocation strategy with perfect foresight.The approach and results can help engineer highly scalable data-driven algorithmic management decision support systems for crowdsourcing..Over the years, crowdsourcing technologies have spawned the “sharing economy”, an economic paradigm in which individuals can borrow or rent assets owned by someone else.This makes the solutions unable to keep up with changes in situational factor in real time in crowdsourcing systems.In an effort to induce high collective productivity, some crowdsourcing systems (e.g., m Turk) are incorporating algorithmic management in the form of high-level policies (e.g., reputation-based sanctioning mechanisms to induce desirable worker behaviours) into their software systems.In crowdsourcing systems, crowdsourcers propose tasks for workers to complete. For example, in Amazon’s Mechanical Turk (m Turk) (https:// workers outnumber crowdsourcers almost 20 to 1.
Thus, in this paper, we adopt the social welfare maximization to collectively optimise system performance.
By analysing typical crowdsourcing system dynamics, we establish a simple and novel worker desirability index (WDI) (equation (10)) which uses information on workers’ reputation, SMVM does not require additional infrastructure support apart from what currently exists in crowdsourcing systems in order to operate.
It can operate in crowdsourcing systems to allocate tasks to workers with a time complexity of O(N log (N)), where N is the total number of workers in a given crowdsourcing system.
At the typical scale of commercial crowdsourcing systems, it has been shown that optimizing social welfare is a Non-deterministic Polynomial-time hard (NP-hard) problem.
The same time complexity has also been proven in alternative formulations treating crowdsourcing task delegation as a joint optimization problem, which has resulted in the need for part of the solutions to be computed offline.