Procedural and Computational Framework for a Non-Decomposable Collective Monetary Cognitive Model using Quantum Dynamic

The classical cognitive models are often formulated in terms of dynamics with the assumption of decomposability. In this formulation, the mind is considered to be a simple machine processing discrete pieces of information, and the decisions are considered to be linear combinations of these pieces. However, these models are not able to represent the complex nature of human thought. This paper proposes a new collective level quantum dynamics model. To reduce the complexity involved
in massive subject cognition, this paper attempts to develop and simulate a new quantum dynamics model for the non-decomposable collective cognitive decision-making process in a financial setting, where we are concerned with the interaction between two or more agents. This procedure involves specifying the collective state space, specifying a collective Hamiltonian that represents the social interaction, simulating the joint cognitive process, and computing the probabilities of the outcomes. The method specifies the joint decision space as a finite dimensional Hilbert space. The Collective Hamiltonian is comprised of personal preference, intrinsic elements, and macroscopic non-decomposable social interactions. The time-evolution of the collective mental state is modeled by numerically solving the Time Dependent Schr¨odinger Equation. The primary result is the final collective decision probability, which can be determined by applying the Born Rule to calculate the square length of the projection of the time-evolved state vector onto the corresponding basis states for each of the four possible outcomes. This calculation yields the time-dependent probability of the group reaching a particular end-point financial decision. These results are then used in an AI-integrated research protocol with tensor networks and other advanced quantum cognitive AI processes for visualization to produce a Dynamic Probability Profile. Validation is performed using a cross-validation technique by setting aside 30% of the sample space data for validation purposes only after completing the research with the remaining 70%