Exactly Measured Psychological Information and Consilient Decision Neuroscience

Abstract

This letter discusses a mathematical construct for neuroeconomics. It re-axiomatizes the measure of numeric utility in Theory of Games and Economic Behavior (von Neumann and Morgenstern, (1944) ) for mathematical partial order under co-identification of absolute value and intensity. The conceptual measure maps a topological process in projective geometry, and is therefore suitable for computational and inductive analysis of how objective incentives neuro-psychologically compare.The derivations proceed towards a classic preliminary device for dynamic measurement, using assumptions that have been successful in theoretical physics, i.e. they build on the proposition of mutually translating sets. The axiomatized measure can be operationalized to characterize the existing economic utility construct from a concordant mapping of temporal difference correlation and how incentives trace to absolute worth, which in context may be called attractivity. Attractivity can be modeled algorithmically to modern theory of reinforcement learning at the micro-level of conceptual signal accumulation from the energetic interaction of incentive comparison processes. Beginning necessarily from fewest details, I operationalize the axiom to a general filter, which traces through the family of mathematical means. Conveniently, the filter also marks the necessary and sufficient conditions for a scientific ideal of local linearity and perfectly assigned prediction of a hypothetical correlation measure for mapping the well-ordering of a hidden common cause. Since, the filter represents a construct that is exact, containing only mutually identified analytical terms from an axiomatically identified core, it is a consilient theoretical term for purposes of analytical induction using decision-neuroscience data.I make neither claim to originality nor finality, however my derivations elevate neuroeconomic utility to a valid and consilient psychological process measure from which a reinvigorated attempt to describe the analytical mechanism by which the economic utility construct maps to neural information may be inter-disciplinarily challenged towards an applied mathematical theory for the 21st century.