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Identifying Opponent's Neuroticism Based on Behavior in Wargame.

Sihui Ge^1,2, Sihua Lyu^1,2, Yazheng Di^1,2

¹Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.

Behavioral Sciences (Basel, Switzerland)

|August 28, 2025

Summary

This study uses machine learning and behavioral data to predict neuroticism in competitive settings, overcoming limitations of traditional self-report questionnaires. The developed approach shows promise for objective psychological assessment in real-world scenarios.

Keywords:

game behavior machine learning neuroticism wargame

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Area of Science:

Psychology
Machine Learning
Behavioral Science

Background:

Traditional neuroticism assessments rely on self-report questionnaires, which are limited in confrontational settings and prone to bias.
There is a need for objective and practical methods to assess neuroticism, especially in competitive environments.

Purpose of the Study:

To develop and validate a machine learning-based approach for predicting neuroticism using behavioral data.
To overcome the limitations of subjective self-report measures in assessing neuroticism.

Main Methods:

Analyzed behavioral records from 167 participants on the MiaoSuan Wargame platform.
Identified key behavioral features associated with neuroticism and developed predictive models.
Utilized the 8-item neuroticism subscale of the Big Five Inventory for neuroticism assessment.

Main Results:

The best-performing model, LinearSVR, effectively inferred neuroticism levels from behavioral data.
Achieved a correlation of 0.606 between predicted and self-reported neuroticism scores.
Demonstrated test-retest reliability of 0.516, indicating model stability.

Conclusions:

Behavioral data can be effectively used to predict neuroticism, offering a viable alternative to self-report measures.
The developed method has practical applications in psychological assessment within competitive environments.
Future research should focus on refining feature selection and expanding application scenarios for behavior-based assessment.