Sensitivity Analysis in a Bayesian Network for Modeling an Agent
Agent-based social simulation (ABSS) has become a popular method for simulating and visualizing a phenomenon while making it possible to decipher the system’s dynamism. When a large amount of data is used for an agent’s behavior, such as a questionnaire survey, a Bayesian network is often the preferred method for modeling an agent. Based on the data, a Bayesian network is used in ABSS. However, it is very difficult to learn the accurate structure of a Bayesian network from the raw data because there exist many variables and the search space is too wide. This study proposes a new method for obtaining an appropriate structure for a Bayesian network by using sensitivity analysis in a stepwise fashion. This method enables us to find a feature subset, which is good to explain objective variables without reducing the accuracy. A simple Bayesian network structure that maintains accuracy while indicating an agent’s behavior provides ABSS users with an intuitive understanding of the behavioral principle of an agent. To illustrate the effectiveness of the proposed method, data from a questionnaire survey about healthcare electronics was used.
This work was supported by JSPS KAKENHI Grant Numbers JP26560121 and JP26282087.
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