Modeling Framework
Cognitive System Dynamics
What is Cognitive System Dynamics?
Cognitive System Dynamics (CSD) is a new formal modeling framework that structurally represents how biases, mental feedback loops, and decision distortions evolve in complex systems. Grounded in systems theory and epistemic modeling, CSD introduces a minimal yet layered architecture for modeling cognitive breakdown, feedback amplification, and resistance in human-centered environments.
Significance
In decision science, cognitive modeling often remains informal, ad hoc, or overly statistical. CSD challenges this status quo by treating cognition as a dynamic system that can be structurally modeled, feedback-mapped, and behaviorally explained through minimal yet formal architecture. It centers not just on how decisions are made, but how they are shaped, distorted, and sustained through mental structures, bias loops, and environmental misalignment.
Foundations
CSD is grounded in Feedback Realism, a philosophical view that treats belief not as a response to truth, but as a dynamic structure stabilized by feedback. In this framing, beliefs persist not despite contradiction, but because they are upheld by recursive loops across cognition, emotion, and environment. CSD provides one structural pathway for modeling this logic by formalizing how bias is amplified, resisted, or restructured through interaction. It serves as a methodological anchor for researchers in dynamics modeling of cognition.
Architecture
CSD introduces a layered modeling architecture: Causal Loop Diagrams (CLDs) capture external environmental structure; Bias Interaction Archetypes (BIAs) formalize internal bias reinforcement; and Stock-Flow Models (SFMs) simulate the evolution of belief, attention, and resistance over time. To avoid noise and preserve clarity each layer is added with purpose. This strict layering enforces cognitive minimalism while enabling precision. The architecture is designed not just to model cognition, but to expose the points where feedback governs the failure of rationality.
Applications
CSD is built for use in high-stakes cognitive environments, where belief change matters, but often fails. In misinformation studies, it helps identify why corrections don’t work. In AI trust design, it models how user confidence becomes structurally locked. In organizational change, it simulates how resistance loops form despite rational incentives. CSD is already being positioned for use in strategy labs, behavioral diagnostics, and systems learning platforms. Its goal is pragmatic: turn structural insight into design leverage.