← Dynamic Systems Engineering Foundations
System Analysis Template
A universal framework for understanding dynamic systems — usable by human researchers, students, engineers, AI systems, and research teams. Every analysis follows the same reasoning process: define the system, identify important variables, understand causal relationships, model interactions, predict behavior, validate conclusions, improve understanding.
Section 1 — System Identification
System Name: What is the system being analyzed?
System Purpose: What function does the system perform? Why does it exist?
System Boundary: What is part of the system? What is external? Why is this boundary appropriate?
Environment: What external factors influence the system — physical environment, users, market conditions, other systems, external constraints?
Subsystems: What smaller systems make up the larger one? For each: purpose, inputs, outputs, interactions.
Section 2 — State Representation
A dynamic system is understood through its state.
Observable Variables: Directly measurable (temperature, speed, revenue, test scores).
Hidden Variables: Influence behavior but can't be directly observed (intent, motivation, internal conditions, future demand).
Essential Variables: Must remain within acceptable limits for the system to succeed. Why is each important? What threatens it? How is it maintained?
State Estimate: Current best understanding of system condition — including evidence, confidence, uncertainty, and unknowns.
Section 3 — Causal Structure
Inputs: What enters the system? Outputs: What does it produce?
Causes: What mechanisms produce change? Effects: What consequences result?
Dependencies: Which variables depend on others? Constraints: What limits system behavior?
Section 4 — Feedback Analysis
Feedback Loops: Positive feedback (processes that amplify change), negative feedback (processes that stabilize change).
Controllers: What regulates the system? Sensors: How does the system receive information? Actuators: How does it respond?
Delays: Where does time delay influence behavior?
Section 5 — Mathematical Representation
Identify: variables, parameters, relationships, rates of change, probabilities, optimization goals.
Questions: What can be measured? What can be predicted? What relationships require testing?
Section 6 — Knowledge Structure
Create a knowledge model including: definitions, concept relationships, prerequisites, assumptions, evidence, unknowns, and conflicts.
Section 7 — Prediction
A useful model must generate predictions. Document: the prediction itself, expected outcome, required conditions, confidence, and measurement method.
Section 8 — Validation
Testing Strategy: How will the model be tested? Metrics: What determines success?
Failure Modes: How could the model fail?
Revision Plan: If the model fails — what assumptions get examined, what variables might be missing, how the model will improve.
Section 9 — Knowledge Artifact Creation
After completing analysis, determine whether knowledge should be preserved. Ask: Does this create a reusable concept? Does this improve understanding? Does this modify an existing model? Does this represent an important discovery?
If yes, create an artifact containing: title, summary, definitions, relationships, evidence, uncertainty, examples, and revision history.
Final DSE Analysis Question
"Does this model help us understand, predict, and improve the behavior of the system while remaining consistent with reality?"
If not, identify what must be improved.