The Second-Order Cascade

Use it for

Map the chain of consequences from any development — second-order effects, third-order cascades, probability-weighted impact classification — to surface the insight your audience will recognize as true in 6 months but almost no one is discussing today. From Michael Simmons’ cause-effect chain analysis (Mar 19, 2026).

The prompt

You will map the cascade of consequences from a development, trend, or event — moving beyond the obvious first-order effects to find the second- and third-order consequences where the most valuable (and least crowded) insights live.

The analytical mechanism: first-order effects are what everyone discusses. Second-order effects are what analysts discuss. Third-order effects — and especially the low-probability/high-impact chains — are where genuinely valuable foresight lives. The goal is to find the one insight that is non-obvious today but will be recognized as correct within 6-12 months.

**THE DEVELOPMENT:** $ARGUMENTS

If no development was provided above, ask me to describe the news event, trend, or industry shift to analyze.

**MY AUDIENCE:** [WHO NEEDS THIS ANALYSIS]
**MY DOMAIN:** [YOUR NICHE/FOCUS AREA. Say "you decide" to keep domain-general]

If "you decide," state the inference and proceed.

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**STEP 1 — FIRST-ORDER MAPPING:**
Map the 3-5 first-order consequences everyone is already discussing. Name them explicitly — the value of this analysis is going BEYOND these.

**STEP 2 — SECOND-ORDER EFFECTS:**
Generate 8-10 second-order effects across categories: economic, behavioral, competitive, cultural, regulatory, technological, labor market, attention/narrative shifts. For each: one sentence on the mechanism (what first-order effect causes this, through what channel).

**STEP 3 — THIRD-ORDER CASCADES:**
For the 3 most interesting second-order effects, generate 3 third-order consequences each.

**STEP 4 — PROBABILITY-IMPACT CLASSIFICATION:**
Classify each chain:
- High-probability / High-impact → These are coming. Prepare now.
- High-probability / Low-impact → Will happen but don't matter much.
- Low-probability / High-impact → Worth watching. Flag for monitoring.
- Low-probability / Low-impact → Discard.

Highlight LOW-PROBABILITY / HIGH-IMPACT chains — asymmetric advantage if you're prepared and everyone else isn't.

**STEP 5 — THE NON-OBVIOUS INSIGHT:**
Identify the one insight from this cascade that the audience will recognize as true in 6 months but almost no one is discussing today. Requirements:
- Trace the full chain of reasoning (second-order → third-order → why it matters)
- Explain why it's not being discussed (cognitive bias, information gap, attention asymmetry)
- Make it specific enough to be actionable

NON-MODALITY CHECK: Is this genuinely non-obvious, or already in the top-10 Google results for this topic? Find what the default analysis process misses.

**STEP 6 — VERIFICATION:**
- Are causal chains logically sound, or is there correlation/causation conflation?
- Are probability assessments calibrated, or pattern-matching from dramatic scenarios?
- Is the non-obvious insight genuinely derived from cascade analysis, or a hot take with backfilled chain?

Flag concerns. Adjust confidence levels. Revise if chains don't hold up.

Worked example

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