Multi-agents modelling of EV purchase willingness based on questionnaires

Yusheng Xue, Juai Wu, Dongliang Xie, Kang Li, Yu Zhang, Fushuan Wen, Bin Cai, Qiuwei Wu, Guangya Yang

Research output: Contribution to journalArticle

24 Citations (Scopus)
266 Downloads (Pure)

Abstract

Traditional experimental economics methods often consume enormous resources of qualified human participants, and the inconsistence of a participant’s decisions among repeated trials prevents investigation from sensitivity analyses. The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments. An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants. Taking the customers’ willingness to purchase electric vehicles (EVs) as an example, multi-layer correlation information is extracted from a limited number of questionnaires. Multi-agents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires. The authenticity of both the model and the algorithm is validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results. With the aid of agent models, the effects of minority agents with specific preferences on the results are also discussed.
Original languageEnglish
Pages (from-to)149-159
Number of pages11
JournalJournal of Modern Power Systems and Clean Energy
Volume3
Issue number2
DOIs
Publication statusPublished - 2015

Bibliographical note

Special Issue On Electric Vehicles And Their Integration With Power Grid

Fingerprint Dive into the research topics of 'Multi-agents modelling of EV purchase willingness based on questionnaires'. Together they form a unique fingerprint.

  • Cite this

    Xue, Y., Wu, J., Xie, D., Li, K., Zhang, Y., Wen, F., Cai, B., Wu, Q., & Yang, G. (2015). Multi-agents modelling of EV purchase willingness based on questionnaires. Journal of Modern Power Systems and Clean Energy , 3(2), 149-159. https://doi.org/10.1007/s40565-015-0112-4