Experimental study on EV purchases assisted by multi-agents representing a set of questionnaires

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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


An experimental economics (EE) method is used to analyze the influences of subjective willingness on the development of the electric vehicle (EV) industry. It is difficult to run large-scale EE-based simulations and to support decision optimizations due to the limited number of qualified human participants and the incomparability among repeated trials. Taking the customers’ willingness to buy EVs as an example, this paper extracts multi-layer correlation information from a limited number of questionnaires and builds a multi-agent model to match the probabilistic distributions of multi-responder behaviors, for the purpose of reflecting the truly statistic information embedded from the questionnaires. The vraisemblance of both the model and the algorithm is validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results. Based on the work presented in this paper, the influence of a key factor on the EV development can therefore be analyzed by using a simulation platform with mixed inputs from agents modelled in this paper and human participants.
Original languageEnglish
Title of host publicationIntelligent Computing in Smart Grid and Electrical Vehicles
EditorsKang Li, Yusheng Xue, Shunmei Cui, Qun Niu
Number of pages11
Publication date2014
ISBN (Print)978-3-662-45285-1
Publication statusPublished - 2014
EventInternational Conference on Life System Modeling and Simulation - Shanghai, China
Duration: 20 Sep 201423 Sep 2014


ConferenceInternational Conference on Life System Modeling and Simulation
SeriesCommunications in Computer and Information Science


  • Experimental economics
  • Human experimenter
  • Multi-agents, behavior analysis
  • Knowledge extraction
  • Willingness to buy EVs


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