Description
This monograph develops a unified theory of decision, cognition, and learning based on the transition from functional representations to field-based geometry. It begins from the empiricaland theoretical limitations of classical models - Expected Utility, Rank-Dependent Utility, andCumulative Prospect Theory - which rely on separable functional mappings and fail to account for contextuality, order effects, and configural preference reversals documented in phenomena such asthe Allais and Birnbaum paradoxes. As an alternative computational paradigm, the work analyzes Extreme Quantum Cognition Machines (EQCMs), in which decision-making is not learned as a function but emerges as a projection from a fixed dynamical system. In EQCM, inputs are encoded into maximum-entropy states, evolved under input-dependent Hamiltonians, and mapped to outputs via a trained linear readout. This architecture demonstrates that robust decision behavior under ambiguity, contradiction, and noise can arise from non-commutative, context-sensitive dynamics, rather thanfrom parametric optimization of utility functions.