The research introduces a probability-wave framework for modeling collective agent behavior. This framework uses a generalized behavioral intelligence (GBI) nonlocal probability-wave equation. The analysis examined Chinese intraday stock market data, revealing that adaptive entangled game modes explain 82-94% of observed decision patterns. This contrasts with neoclassical finance models relying on independent rational agents.
Specifically, 2-12% of behaviors exhibited adaption to intraday news and environments, characterized by dual equilibrium states and abrupt reference point shifts. Purely independent modes occurred in less than 5% of cases. These findings empirically support the Liu-Chen-Ao (LCA) hypothesis regarding nonlocal entangled nerve fibers.
The research suggests incorporating adaptive entangled game modules into artificial general intelligence (AGI) architectures. This approach addresses limitations of conventional artificial neural network (ANN)-based AI, which relies on trillions of opaque parameters. The integration of probability-wave-based simulations with ANN-based AI could enrich foundation models and facilitate the development of human-like processing units.
These human-like processing units may create more compact, efficient, and robust AGI systems, particularly for embodied intelligence and robotics. Source: https://arxiv.org/abs/2609.09226