Entropy, Uncertainty, and Directionality in Transforming Systems / A Constraint-Based Perspective on Biological and Artificial Cognition

This paper proposes a unifying interpretation of biological and artificial cognitive systems as instances of a broader class of transforming systems. These systems exhibit directional behavior not as a consequence of intention or goal-directed agency, but as a result of structural constraints and the presence of gradients in quantities such as entropy [1] or uncertainty. Biological systems maintain internal structure by locally resisting entropy increase [1][2], while artificial systems - particularly predictive models - reduce uncertainty through probabilistic inference. Both processes can be understood as asymmetric responses to disequilibrium. We argue that this behavior reflects a more general principle: transforming systems respond to gradients in the quantities they process. This perspective provides a conceptual foundation for understanding response generation, stability, and the apparent “activity” of both natural and artificial systems without invoking teleological assumptions.