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Open Access Publications from the University of California

Optimal Task Allocation for Crowdsourcing Applications

  • Author(s): Tran, John
  • Advisor(s): Pasqualetti, Fabio
  • et al.

Crowdsourcing is an emerging method for efficient task distribution and completion. With multiple tasks at hand, it is considerably faster to divide these tasks to multiple operators than to queue them all onto one person. A single human agent can arguably only finish one problem at any single point in time, assuming the tasks do not overlap in their description. This method is useful for real-world applications as many complex systems with a variety of tasks could be solved more efficiently. Due to pulling agents from a randomized crowd; however, we are faced with the problem of the quality of the work of each agent utilized. Efficiency will always go up the more tasks we distribute to a random crowd, but the performance or success rate may go down depending on the quality of the crowd. We then consider the question, how do we optimize various users from a crowd? In this paper, we focus on a single exogenous human factor, fatigue, and the expertise of the agents to approximate the probability of an erroneous decision from the agents. We study various fatigue models, using the Three-Process Model to compare with our model. We will also present a simplified model to predict fatigue and approximate our agents' performance due to task load and compare with current models. Considering these fatigue models, performance values of agents will be calculated to maximize our success rate depending on their task allocation. The expertise of these agents are also important in analyzing the quality of their work. We model the expertise coefficient of our agents in two fashions: a single constant value throughout the system and a dynamic value based on the drift diffusion model in optimal decision making. The studies regarding a dynamic expertise coefficient are to be completed in detail during future works. We then provide an optimal control approach using dynamic linear systems. Our model is optimized by Pontryagin's Maximum Principle from optimal control theory, allowing for analysis as a continuous function. In this thesis, the models of study are also extended into the discrete time model where we provide a new optimal policy. At the end of our thesis, we will provide an analysis of our performance model and present its success rate due to optimal task allocation.

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