A Graphic System for Teaching Physiological Causality and Beyond. Rules and Core Model Collection

Abstract Understanding complex causal and feedback relationships is fundamental across disciplines—from physiology to economics—yet existing diagramming methods struggle to simultaneously represent continuity, causality, and homeostasis in an intuitive way. This paper introduces a rule-based graphical system designed for universal pedagogical application, enabling learners to visualize and construct causal networks with consistent, domain-agnostic rules. The model’s axes represent system parameters (e.g., blood pressure, market demand), while curved sectorial vectors encode directional influences, unifying negative/positive feedback and homeostatic principles into a single scalable framework. By standardizing the visual grammar of causality, this approach enhances clarity in teaching and modeling dynamic systems without relying on discipline-specific conventions. This work is structured in two complementary parts: (1) a theoretical framework introducing the axiomatic foundations of the system, including its scalable architecture, followed by (2) a core model (CM) collection demonstrating its utility through physiological case studies. The model’s dual presentation- from abstract formalism to concrete biological implementation- establishes its versatility as a pedagogical resource for mechanistic reasoning in physiology and beyond. Key words: physiology, education, causal diagrams, feedback loops, homeostasis, teaching tool