This study aimed to investigate the capacity of a large language model (LLM), specifically DeepSeek, for simulating regional psychological characteristics based solely on demographic information. In particular, it examined whether DeepSeek can preserve culturally distinct psychological patterns without reducing them to oversimplified, flattened profiles, with a focus on personality traits and subjective well-being across different regions of China. Utilizing a sample matched to demographic features from the 2018 China Family Panel Studies (CFPS2018) (N = 2,943), the research generated artificial "virtual participants" with DeepSeek. The simulated dataset was compared to real human responses from CFPS to analyze regional differences in Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and subjective well-being.
Methodologically, the empirical human dataset comprised adult participants from CFPS 2018, covering seven culturally and socioeconomically distinct Chinese regions (North China, Northeast, East China, Central China, South China, Southwest, and Northwest). Each region had an equal number of males and females aged from 18 to 65. Personality was measured using a simplified 15-item Chinese Big Five inventory, while subjective happiness was assessed using a single-item self-rating scale. Correspondingly, a matched virtual dataset of equivalent size and demographic distribution was generated using DeepSeek-V3-0324, with constructed prompts designed to mirror the demographics and cultural context of the actual participants. The virtual participants responded to identical psychological assessments, ensuring comparability.
Results from independent-sample t-tests indicated overall similarity, while significant differences between human and AI-generated data in certain aspects. Specifically, the virtual dataset closely mirrored human data in terms of personality and happiness distributions, but exhibited significant differences in several traits. Simulated participants scored significantly lower in extraversion and openness (with medium to large effect sizes) and higher in agreeableness and neuroticism compared to human data. Happiness levels in the simulated dataset were consistently lower, suggesting limitations in DeepSeek’s capacity to replicate subjective emotional experiences accurately.
Further ANOVA analyses revealed that both datasets reflected significant regional differences in personality traits and happiness. For example, in human responses, the Southwest region demonstrated significantly higher extraversion, while the Northeast region exhibited higher subjective happiness. However, DeepSeek’s simulated data diverged from these patterns, notably underestimating happiness in the Northeast and overestimating certain personality dimensions in economically prosperous East China.
Additionally, regression analyses explored the relationship between personality traits and subjective happiness within both datasets. Human data indicated significant positive predictors of happiness as conscientiousness, extraversion, openness, and the negative predictor, neuroticism. The virtual data, however, showed different structural variations: openness and agreeableness positively predicted happiness, neuroticism negatively predicted happiness significantly more strongly, extraversion negatively predicted happiness, and conscientiousness had no significant predictive effect. Principal Component Analysis (PCA) further highlighted structural difference between the human and simulated datasets, particularly reflecting an overreliance on more linguistically salient and externally expressed traits in the AI-generated responses.
These findings contribute significantly to the understanding of LLM applications in psychological research. Primarily, they demonstrate DeepSeek’s general effectiveness in simulating broad psychological distributions, while also highlighting its limitations in capturing region-specific psychological structures shaped by the interplay of economic conditions, cultural norms, and psychological dispositions—limitations likely stemming from the model’s training data, which insufficiently represents these layered contextual factors.
The practical implications of this research are substantial. The use of DeepSeek as a tool for generating "virtual participants" could significantly reduce costs and logistical burdens associated with large-scale psychological research, enabling preliminary testing and refinement of research designs prior to field deployment. However, caution is recommended due to observed biases, including exaggerated cultural stereotypes and inadequate modeling of subjective emotional states. Future model iterations and methodological advancements should address these issues by incorporating richer, more culturally grounded training data and more precise affective modeling techniques.
Despite these limitations, the research provides important methodological insights and theoretical contributions by introducing an innovative approach using LLM-generated virtual participants for psychological inquiry. It underscores the potential of DeepSeek and similar models for cost-effective large-scale research while highlighting crucial areas that require further refinement.
In conclusion, this study validates the feasibility of employing large language models such as DeepSeek for simulating regional psychological structures, but also emphasizes the necessity for continued development to address culturally grounded and psychologically meaningful variations effectively. As training data and algorithms advance, these models may help reshape methodologies within personality and cross-cultural psychological research.
Key words
large language model /
deepseek /
big five personality /
subjective well-being /
regional psychological structure /
virtual participants
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