We introduce a probability‑wave framework to model the collective behavior of interacting adaptive agents, deriving testable eigenmodes via a generalized behavioral intelligence (GBI) non‑local probability‑wave equation. This framework captures a wide spectrum of human‑level intelligence behaviors with analytical mechanisms and offers an indirect way to examine the Liu‑Chen‑Ao (LCA) hypothesis of non‑local entangled nerve fibers in the brain through collective trader actions.\
Empirical analysis of Chinese intraday stock market data shows that adaptive entangled game modes explain 82%–94% (89% overall) of observed decision patterns, sharply contrasting with neoclassical finance predictions based on independent rational agents. Additionally, 2%–12% of behaviors adapt 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 provide empirical support for the LCA hypothesis, as observable trading behaviors reflect underlying brain mechanisms and internal decision‑making processes in behavioral psychology.\
The results highlight the necessity of incorporating adaptive entangled game modules into artificial general intelligence (AGI) architectures to address the limitations of conventional ANN‑based AI, which relies on trillions of opaque parameters. By integrating ANN 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 yield more compact, efficient, and robust AGI systems, especially for embodied intelligence and robotics.\
Review