The evaluation of voting rules based on voting networks: Weber problem with support domain

Abstract

This study focuses on improving the Weber model to obtain a more optimised solution, by which we can measure effectively the quality of the voting rules. By introducing the voter’s supporting domain and the candidate’s supported degree to depict the emotional factors of voters and the real voting network, we propose the concept of validity for a candidate. Upon the traditional Weber model, two improved models are presented and the corresponding global optimal candidate is used as the evaluation benchmark for the voting rules. The experiments show that the optimal solution of the two models has better robustness in complex voting networks, can be used as a standard to evaluate the voting rules. When the support degree of voter is the main factor, the Condorcet rule is optimal in most cases, and when the validity of the candidate is taken as the main factor, the Approval rule must be the best.

KEYWORDS:

• Voting rules
• evaluation benchmark
• extended Weber model
• multi-objective Weber model
• voting networks

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (Project No. 71871150; 71471123) .

Notes on contributors

Chunxiang Guo

Chunxiang Guo was born in 1971. She graduated from Southwest Normal University in 1994 with a bachelor of science degree; in 2002 she obtained a master’s degree in applied mathematics from Sichuan University; in 2005 she received a doctorate in management. Now she is a professor at Sichuan University Business School, a doctoral tutor and a postdoctoral fellow in transportation engineering. At present, she is mainly engaged in research work in the fields of data-driven behavioural decision-making, logistics and supply chain management and behavioural operation management. E-mail: [email protected]

Yinjie Zhang

Yinjie Zhang was born in 1997. He is a postgraduate student in Business School, Sichuan University with the major Industrial engineering. Now, his major research interests are data-driven behavioural decision-making and complex network. E-mail: [email protected]

Ruili Shi

Ruili Shi was born in 1988. She is a Ph.D. student in Business School, Sichuan University. Her major research interests are behavioural decision-making and group decision-making. E-mail: [email protected]

Dongzhi Wang

Dongzhi Wang was born in 1993. He obtained a master’s degree in Computer Science from Southwest University of Science and Technology. His major research interests are data mining and complex networks. E-mail: [email protected]