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

Free Full-text PDF

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


1. Xiaoyun Chen. Research on the Business Model of Retail Enterprises in the Era of “Mobile Internet +” Sharing Economy- Retail Crowdsourcing Shopping Model [J] .Journal of Baoji University of Arts and Sciences (Social Science Edition), 2016,36(06):76-81.
2. Shu Wang. Research on Collaborative Innovation of Crowdsourcing Model of Online Business Platform [D] .Zhejiang University, 2012.
3. Yixin Cui, Ziwei Shen, Xu Zhang. Research on the new business model of “taking pictures to make money” [J] .Modern Business, 2019(36):1 3-16.
4. Yajie Zhu. Research on Crowdsourcing Business Model Element Model and Operation Mechanism [D] .Shandong University, 2011.
5. Xing Wang. Analysis of electricity consumption behavior of residents in Jingzhou City based on K-means clustering algorithm [J] .Communications World, 2019(08):276-277.
6. Jie Fang, Zhonglin Wen, Dongmei Liang, Nini Li. Analysis of Moderating Effects Based on Multiple Regression [J]. Psychological Science, 2015,38(03):715-720.
7. Qiyuan Jiang, Jinxing Ye. Mathematical Model (Fifth Edition), Beijing: Higher Education Press, 1986.6.
8. Jianqiang Fan, Haicheng Xu. Bidding Model Based on Bayesian Game Equilibrium at Reasonable Low Price [J] .Statistics and Decision, 2008(18):65-67.
9. Frank R. Giordano, William P. Fox, Steven B. Horton. Mathematical Modeling, Beijing: Mechanical Industry Press, 2014.6.

Terms of Use/Privacy Policy/ Disclaimer/ Other Policies:
You agree that by using our site, you have read, understood, and agreed to be bound by all of our terms of use/privacy policy/ disclaimer/ other policies (click here for details).

This work and its PDF file(s) are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.