Describable but not Predictable

Mathematical Modeling and Non-Naturalistic Causation

Abstract

Our notions of causation in science are often unintentionally constrained by the mathematics we use.  Typically, scientific investigations use algebraic or calculus-based mathematics to model causes and effects.  In these types of models, there is a predictive relationship between the cause and the effect.  This predictive pattern is what most people use to classify events as materialistic, leaving events that are not so classified as non-materialistic.  Mathematics over the last century has introduced new formalisms that cover functions that do not conform to the materialistic pattern.  While these functions cannot always predict outcomes for typical cases, they can be studied and analyzed in other ways, and therefore can be used for knowledge-building.  Therefore, by expanding the mathematical toolset, investigators can better identify and model non-materialistic causes.