Adaptive Entangled Game Modules in Artificial General Intelligence
Adaptive Entangled Game Modules in Artificial General Intelligence
通用人工智能中的自适应纠缠博弈模块
Abstract: We introduce a probability-wave framework for modeling the collective behavior of interacting adaptive agents, deriving testable eigenmodes through a generalized behavioral intelligence (GBI) nonlocal probability-wave equation.
摘要： 我们引入了一种概率波框架，用于模拟交互式自适应智能体的集体行为，并通过广义行为智能（GBI）非局部概率波方程推导出可测试的本征模。
This framework captures a broad range of human intelligence behaviors with analytical mechanisms and offers an indirect method to examine the Liu-Chen-Ao (LCA) hypothesis of nonlocal entangled nerve fibers in the brain through collective trader behaviors.
该框架通过分析机制捕捉了广泛的人类智能行为，并提供了一种间接方法，通过集体交易者的行为来检验大脑中非局部纠缠神经纤维的刘-陈-敖（LCA）假说。
Our empirical analysis of Chinese intraday stock market data demonstrates that adaptive entangled game modes explain 82-94% (89% overall) of observed decision patterns, a sharp contrast to the predictions of neoclassical finance based on independent rational agents.
我们对中国股市日内数据的实证分析表明，自适应纠缠博弈模式解释了 82-94%（总体为 89%）的观测决策模式，这与基于独立理性智能体的新古典金融学预测形成了鲜明对比。
Moreover, 2-12% of behaviors show adaption to intraday news, events, and environments, characterized by dual equilibrium states and abrupt reference point shifts, while purely independent modes occur in less than 5% of cases.
此外，2-12% 的行为表现出对日内新闻、事件和环境的适应性，其特征是双重平衡状态和参考点的突然转变，而纯粹的独立模式出现的情况不到 5%。
These findings empirically support the LCA hypothesis, as observable trading behaviors reflect underlying brain mechanisms and internal intelligence decision-making in behavioral psychology.
这些发现从实证上支持了 LCA 假说，因为可观测的交易行为反映了行为心理学中潜在的大脑机制和内部智能决策。
Our results highlight the necessity of incorporating adaptive entangled game modules into artificial general intelligence (AGI) architectures, addressing the limitations of conventional artificial neural network (ANN)-based AI, which relies on trillions of opaque parameters.
我们的研究结果强调了将自适应纠缠博弈模块纳入通用人工智能（AGI）架构的必要性，这解决了传统基于人工神经网络（ANN）的 AI 的局限性，后者依赖于数万亿个不透明的参数。
By integrating ANN-based AI with probability-wave-based entangled-brain simulations, machine learning can enrich AGI foundation models (FMs) and facilitate the development of human-like processing units (HPUs) that leverage brain-inspired mechanisms.
通过将基于 ANN 的 AI 与基于概率波的纠缠大脑模拟相结合，机器学习可以丰富 AGI 基础模型（FMs），并促进利用类脑机制的类人处理单元（HPUs）的发展。
Such HPUs may ultimately create more compact, efficient, and robust AGI systems, particularly for embodied intelligence and robotics.
此类 HPUs 最终可能会创造出更紧凑、高效且稳健的 AGI 系统，特别是在具身智能和机器人领域。