Modeling Digital Personality: A Fuzzy-Logic-Based Myers–Briggs Type Indicator for Fine-Grained Analytics of Digital Human

Abstract

Digital human in cyberspace can help provide humanized services in specific applications, such as question & answer systems, recommender systems, chatter robots, and intelligent assistants. While most researches focus on behavior analytics, few of them integrate the personality that is also a closely related factor. As a classic indicator for personality representation, Myers–Briggs type indicator (MBTI) categorizes an individual into mutually exclusive types from four dichotomous axes (extraversion versus introversion, sensing versus intuition, thinking versus feeling, judging versus perceiving). Traditional recognition method using MBTI simply measures the user’s preference frequency in each axis through questionnaires, treating the dominant value as the identified result. Such a paradigm, however, represents all the people with only 16 types and cannot distinguish heterogeneous users clearly. This article proposes a novel personality recognition method using fuzzy logic. Different from previous classifications, our new method categorizes the individual in a continuous space and represents one’s personality in a more fine-grained level. We have designed comparative psychological tests for 77 people. The validation experiments on such tests indicate that the fuzzy-logic-based method is not only consistent with the classic MBTI tests (in the sense of defuzzification) but also provides the uncertainty for each personality type. Therefore, it can be viewed as a generalization of the classic MBTI tests and promotes the representation of individual’s heterogeneity for fine-grained analytics of digital human.