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.
Highlights
China is one of the world pioneering countries in promoting the development and acceptance of electric vehicles (EVs) due to the unprecedent challenges of severe air pollutions and significant amount of CO2 emissions countrywide
For a certain subset containing m answer sheets, samples are randomly selected from the total for building the statistical multi-agent model
The hybrid experimental economics (EE)-based simulation techniques combining multi-agents and human participants strike a balance among different computation considerations involving strong subjective willingness of participants and a large number of simulated individuals
Summary
China is one of the world pioneering countries in promoting the development and acceptance of electric vehicles (EVs) due to the unprecedent challenges of severe air pollutions and significant amount of CO2 emissions countrywide. To avoid the blindness in this process, it is imperative to quantitatively assess customers’ response to these policies beforehand, and to adequately handle any challenges that may arise when the power systems are integrated with a huge number of EVs [3]. This requires the collection of a massive number of data, which are used for modelling and analysis in a bid to apprehend the whole picture, including how the EV-involved systems are operated, how the policy can implemented and their consequences, what are the customers’ preferences in purchasing and using EVs, and how the EV’s prospect and participants’ willingness are interrelated, etc. The influences of customers’ preferences on the purchasing ratio are discussed
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