Optimization of Task Pricing Based on Multiple Regression Analysis and Game Theory

Optimization of Task Pricing Based on Multiple Regression Analysis and Game Theory

Tianlong Wang1, Hongyan Bao2, Hairui Zhang2*
1College of Civil Engineering & Architecture, China Three Gorges University, Yichang, 443002, China.
2College of Science, China Three Gorges University, Yichang, 443002, China.


Nowadays, the Internet is developing rapidly, while in the context of the mobile Internet Making photos to make money, a kind of self-service service mode currently has the problem of unreasonable task pricing, which leads to the failure of commodity inspection. Hence, in order to solve the problem, on the basis of a set of completed project task data and the whole registered member information provided by the company, we set up Multiple regression model and Bayesian equilibrium model by means of the mechanism analysis to determine models, big data statistical to determine parameters and considering the various factors that can affect the price of task. Not only can the models established in this paper greatly optimize the task pricing scheme, but also them do improve the task completion degree as much as possible and play a positive role in the efficiency of program operation under the premise that the total cost is basically unchanged.

Keywords: K-means clustering; Multiple regression; The Bayes equilibrium; Game theory

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How to cite this article:
Tianlong Wang, Hongyan Bao, Hairui Zhang. Optimization of Task Pricing Based on Multiple Regression Analysis and Game Theory. Journal of eSciences, 2020; 3:9. DOI: 10.28933/esciences-2020-01-1805


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