Exercise 2: AI vs. Human Systems Analysis
Layers Used: Layer 2 (Reasoning Receipt), Layer 5 (Divergence Test)
You will use the Error Taxonomy from Chapter 2, Exercise 1 to annotate errors in AI's systems analysis, not just factual claims.
What You Do
Now prompt both Claude and ChatGPT with the same scenario and ask each for a comprehensive analysis of all consequences. Compare both AI outputs against your own cascade map. Typically, AI produces a broader but shallower analysis — more categories, fewer connections between them. Create a merged map (Draft 2) that combines the best of human and AI analysis with clear attribution for each insight.
A comparison document with three columns: "Effects only I found," "Effects only AI found," and "Effects we both found." The merged cascade map (Draft 2) with every insight color-coded or labeled by source: Human (H), Claude (C), ChatGPT (G), or Synthesis (S) for new insights that emerged from combining perspectives. A brief note explaining which category had the most valuable additions.
I am comparing my systems analysis with AI-generated analyses of the
same scenario. I have created a merged cascade map that combines
insights from my own thinking, Claude's analysis, and ChatGPT's
analysis, with each insight attributed to its source.
Please:
(1) Evaluate my merged map -- is it genuinely better than any single
source alone?
(2) Are there insights I attributed to myself that are actually
standard AI outputs? Be honest.
(3) Are there synthesis insights (S) that are genuinely novel --
combinations that none of the three sources produced independently?
(4) Rate the quality of my attribution -- am I being honest about
where each idea came from?
(5) What important systemic effects are STILL missing from the merged
map?
Scenario: [paste].
My original map: [paste].
Claude's analysis: [paste].
ChatGPT's analysis: [paste].
Merged map with attribution: [paste].
Finally, complete the Thinking Score Card for this exercise:
Independent Thinking (1-10), Critical Evaluation (1-10),
Reasoning Depth (1-10), Originality (1-10), Self-Awareness (1-10).
For each score, give a one-sentence justification.
What This Teaches You
You learn exactly where human systems thinking adds value that AI misses (usually in feedback loops and cultural/political dynamics) and where AI adds value humans miss (usually in breadth of categories). The attribution exercise forces intellectual honesty — you cannot claim AI's insights as your own when the source is documented.