发表机构
Laboratory for Artificial Intelligence in Design; Royal College of Art; University of Science and Technology of China; Red Horse Investments Group; Beijing Shangdafei Science & Technology Co., Ltd（设计人工智能实验室; 皇家艺术学院; 中国科学技术大学; 红马投资集团; 北京尚大飞科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出概率波框架建模自适应智能体，实证发现纠缠博弈模式解释89%决策，支持LCA假说，并主张将自适应纠缠博弈模块融入AGI架构以克服传统ANN局限。
AI 中文摘要
我们引入了一个概率波框架，用于对相互作用的自适应智能体的集体行为进行建模，并通过广义行为智能（GBI）非局域概率波方程推导出可检验的本征模。该框架通过分析机制捕获了广泛的人类智能行为，并提供了一种间接方法来检验刘-陈-敖（LCA）关于大脑中非局域纠缠神经纤维的假说，该方法通过集体交易者行为实现。我们对中国日内股票市场数据的实证分析表明，自适应纠缠博弈模式解释了观察到的决策模式的82-94%（总体89%），这与基于独立理性主体的新古典金融学的预测形成鲜明对比。此外，2-12%的行为表现出对日内新闻、事件和环境的适应性，其特征是双平衡态和参考点的突然转移，而纯独立模式在不到5%的情况下出现。这些发现实证支持了LCA假说，因为可观察的交易行为反映了行为心理学中的潜在大脑机制和内部智能决策。我们的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，解决了基于人工神经网络（ANN）的传统人工智能的局限性，后者依赖于数万亿个不透明参数。通过将基于ANN的人工智能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），并促进利用大脑启发机制的人类样处理单元（HPUs）的开发。这样的HPUs最终可能创造出更紧凑、高效和稳健的AGI系统，特别是对于具身智能和机器人技术。
英文摘要
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. 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. 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. 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. These findings empirically support the LCA hypothesis, as observable trading behaviors reflect underlying brain mechanisms and internal intelligence decision-making in behavioral psychology. 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. 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. Such HPUs may ultimately create more compact, efficient, and robust AGI systems, particularly for embodied intelligence and robotics.
Comments22 pages, 13 figures, and 3 tables
