A Computational Model of Human Personalities (Dataset)

In this paper we present a method for programming human personalities. We first describe a plausible biological mechanism that provides a logical explanation of the model in terms of power flow in the brain. We then show how the model qualitatively predicts the personality characteristics described by Lowry Colors, Enneagram, Merrill's C.A.P.S., the five factor model, and the clinical personality disorders. Regardless of the truth of the biological model, the predicted configuration allows us to create a feedback-loop computational model of human personalities, using the Enneagram to describe self-images and Myers-Briggs to describe behavioural techniques. The model successfully predicts the distribution of Myers-Briggs personality types in the human population within 14 percent error. The model directly results in the ability to program personalities on robots and AI systems. Moreover, the model suggests where to look for the source of personalities in the operation of biological brains. Run the file main.m.本研究提出一种人类人格编程方法。首先，我们阐述了一种合理的生物学机制，从大脑能量流动的角度为该模型提供逻辑层面的解释。随后阐明该模型如何定性预测各类人格特质，涵盖罗瑞色彩人格（Lowry Colors）、九型人格（Enneagram）、梅里尔CAPS量表（Merrill's C.A.P.S.）、大五人格模型（five factor model）以及临床人格障碍相关特质。无论该生物学模型的正确性如何，基于其预测的结构，我们可构建人类人格的反馈环路计算模型：以九型人格描述自我意象，以迈尔斯-布里格斯（Myers-Briggs）描述行为模式。该模型可成功预测人群中迈尔斯-布里格斯人格类型的分布，误差率不超过14%。该模型直接实现了在机器人与人工智能系统上进行人格编程的能力。此外，该模型还指明了在生物大脑运作中探寻人格来源的研究方向。运行main.m文件。