Yes, the extensive “How-To” and process materials provided in the sources can be translated into Mermaid schematics.
The sources describe three distinct “flavors” of investigation depending on the nature of the problem:
1. The Iterative Learning Cycle (Checkland, OU, MoM) – for “messes” and social complexity.
2. The Complexity Navigation Flow (Snowden, Harish Jose) – for “complex” domains requiring experimentation.
3. The Biological Design Process (Alan Kay, Nelson) – for engineering and architecture.
Here are the schematics for each approach.
1. The Systemic Inquiry Cycle (The “Soft” Approach)
This schematic synthesizes the Soft Systems Methodology (SSM) and Critical Systems Practice approaches (Checkland, Jackson, OU Course). It visualizes the critical movement across the “Epistemic Cut” between the Real World and the Abstract World.
graph TD subgraph Real_World ["The Real World (The Mess)"] A[Enter the Situation] --> B(Create 'Rich Pictures'<br/>Capture structure, process, & conflict) B --> C{Define the Problematic Situation} G[Compare Models to Reality<br/>'Does this happen?'] --> H{Is Change Desirable & Feasible?} H -- No --> B H -- Yes --> I[Take Action<br/>'The Intervention'] I --> A end subgraph Systems_Thinking_World ["The Abstract World (Logic)"] C --> D[Formulate Root Definitions<br/>Use CATWOE] D --> E[Build Conceptual Models<br/>'What must the system DO?'] E --> F[Derive Questions from Models] F --> G end style Real_World fill:#f9f9f9,stroke:#333,stroke-width:2px style Systems_Thinking_World fill:#e1f5fe,stroke:#333,stroke-width:2px
2. The Complexity Navigation Flow (The “Cynefin” Approach)
This schematic visualizes the decision logic found in the works of Dave Snowden, Harish Jose, and Max Boisot. It focuses on determining the domain and applying the correct heuristic (Probe vs. Analyze).
graph TD Start([Start Investigation]) --> Q1{Is causality visible?} Q1 -- Yes --> Domain_Ordered[Ordered Domain<br/>Complicated/Simple] Q1 -- No --> Domain_Complex[Complex Domain<br/>Unordered/Emergent] Q1 -- Chaos --> Domain_Chaos[Chaotic Domain<br/>Crisis] subgraph Ordered_Strategy ["Sense-Analyze-Respond"] Domain_Ordered --> O1[Gather Data] O1 --> O2[Apply Expert Analysis] O2 --> O3[Implement Best/Good Practice] end subgraph Complex_Strategy ["Probe-Sense-Respond"] Domain_Complex --> C1[Design Safe-to-Fail Probes<br/>Parallel Experiments] C1 --> C2[Sense Patterns<br/>'Genchi Genbutsu'] C2 --> C3{Is the pattern positive?} C3 -- Yes --> C4[Amplify<br/>Add Energy/Resources] C3 -- No --> C5[Dampen<br/>Kill/Disrupt] C4 --> C6[Stabilize into Practice] end subgraph Chaos_Strategy ["Act-Sense-Respond"] Domain_Chaos --> X1[Act to Establish Order<br/>Impose Constraints] X1 --> X2[Sense Reaction] X2 --> Domain_Complex end
3. The Biological Design Process (The “Kay/Nelson” Approach)
This schematic captures Alan Kay’s engineering philosophy and Harold Nelson’s Systemic Design. It focuses on shifting context and architecture before attempting implementation.
graph TD Start(The Problem / Need) --> Step1[Shift Context<br/>'Point of View is worth 80 IQ points'] Step1 --> Step2{Q: Is it Complex or Complicated?} Step2 -- Complicated/Messy --> Step2a[Simplify<br/>Remove 'Noise' & Bad Design] Step2 -- Intrinsic Complexity --> Step3[Problem Finding] Step3 --> Step4[Ask: What Is Actually Needed?<br/>'WIAN'] Step4 --> Step5[Biological Decomposition] subgraph Architecture ["The Design Phase"] Step5 --> D1[Encapsulate Sub-systems<br/>'Cells'] D1 --> D2[Define Message Passing<br/>No rigid dependencies] D2 --> D3[Design for Late Binding<br/>Delay commitment to details] end Architecture --> Step6[Simulation<br/>'Debug the Ideas'] Step6 --> Step7[Fabrication / Optimization]
Key Differences in Application
• Use Schematic 1 (Systemic Inquiry) when dealing with people, culture, and conflicting definitions of the problem (e.g., organizational politics).
• Use Schematic 2 (Complexity Navigation) when dealing with market uncertainty, innovation, or crisis where the answer cannot be known in advance.
• Use Schematic 3 (Biological Design) when building new systems (software, products, or institutions) that need to scale and survive over time.
