Virtual Grounding for Facsimile Model Construction Where Real Data Is not Available

Recently, a need has arisen for facsimile model construction with close correspondence to the real world for use in evaluating the effectiveness of specific policies. When constructing facsimile models requiring a large amount of data, however, parameter estimation using data fitting becomes problematic. Furthermore, there are often problems where, owing to the limitations of field research, it becomes impossible for analysts to obtain the data necessary for model construction. The paper proposes a solution to this data collection problem by using “virtual grounding” as a method for creating valid agent models. The proposed method constructs an agent model by isolating qualitative features of the real world situation that are targets for modeling at a stage where the complete dataset is not yet available, and uses standard models for which utility has been previously demonstrated. Following this construction, a number of sample participants modeled as agents repeatedly make hypothetical decision-making actions within the model environment, and the model parameters are estimated based on the results of these decisions. This paper demonstrates the utility of the virtual grounding method by using an example of modeling visitor agents to Tokyo Disney Sea.

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