# Second And Third Order Effects Mapping

> Anticipates the second- and third-order effects of a strategic decision or AI solution — how it changes customer behavior, the competitive landscape, and your own organization over time, beyond the direct first-order effect.

- Skill: `pilot2service/second-and-third-order-effects-mapping` (Agent Skill)
- Install (CLI): `npx skillmds@latest add pilot2service/second-and-third-order-effects-mapping`
- Raw SKILL.md: https://api.skillmd.com/api/skills/pilot2service/second-and-third-order-effects-mapping/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Pilot2Service (https://skillmd.com/u/pilot2service)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/pilot2service/second-and-third-order-effects-mapping

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# Second- & Third-Order Effects Mapping

## Purpose

Surfaces the effects of a decision or solution that are easy to miss because
they aren't immediate: what the first effect triggers next (second order),
and what that in turn triggers more broadly in the market, competition, or
regulation (third order). Most decisions are evaluated only on their
first-order effect — this skill forces the view further out.

## Anchored in research

- Systems thinking and "second-order thinking" technique in strategic
  decision-making (widely known, e.g. the "consequence scanning" practice in
  consulting).
- Liedtka (1998) — systems perspective and thinking in time; the same roots
  as [`../scenario-and-foresight/SKILL.md`](../scenario-and-foresight/SKILL.md),
  but this skill operationalizes order-of-effect thinking specifically around
  one decision, rather than building broader alternative futures.
- A research report on AI Business Designer skills for the age of AI,
  supplied by the pack owner (2026) — explicitly raises this in the context
  of evaluating an AI solution's business case: how the solution changes
  customer behavior over the long run, and what new competitors it might
  attract into the market.

## Method

1. **Name the decision or solution under review** (e.g. a new AI feature, a
   pricing change, automation, a new business model).
2. **Map the first-order effect**: what's the direct, immediate consequence?
   This is usually the only effect considered in decision-making by default.
3. **Map the second-order effects**: what does the first effect trigger next?
   For example, how does the customer actually *change* their behavior once
   the solution has been in use for a while — not just their first reaction.
4. **Map the third-order effects**: what do the second-order changes trigger
   more broadly — competitor reactions, new market entrants, tighter
   regulation, shifting stakeholder expectations?
5. **For each order, ask separately**: who is affected (customer, competitor,
   your own organization, regulator, the wider ecosystem), and is the effect
   likely positive, negative, or ambivalent?
6. **Identify which second-/third-order effects are likely and significant
   enough to change the original decision** — go back and adjust the
   decision if needed.
7. **Produce a structured effect chain (1st → 2nd → 3rd order)** to support
   the decision; mark clearly what's reasoned inference and what's
   speculation (`[assumption — verify]`).

## What this skill does NOT do

- Doesn't predict the future with certainty — second-/third-order effects are
  plausible hypotheses, not probability calculations.
- Doesn't replace [`../scenario-and-foresight/SKILL.md`](../scenario-and-foresight/SKILL.md)
  — this skill follows one decision's effect chain forward; scenario-and-foresight
  builds alternative futures from broader uncertainty.
- Doesn't make the decision for you — it surfaces effects that would
  otherwise go unnoticed; the decision itself stays with the human.

## Refinement notes

Areas to keep deepening with real practice:

- your own rules of thumb for how far down the effect chain it's worth going
  before it turns too speculative to be useful
- concrete templates (into [`../../references/`](../../references/))
- reference cases / your own examples where a second-/third-order effect
  changed the original decision
- what this skill deliberately does *not* do (guardrails, common mistakes) —
  add to the list above

This is an internal working note, not a claim about the skill's current
usability. Track depth privately via the `maturity` field in
`skills_index.json` (see
[`../../../meta/maturity_levels.md`](../../../meta/maturity_levels.md)).
**Don't add new fields to the frontmatter** — `name` and `description` are
the only ones allowed (see
[`../../../meta/frontmatter_schema.md`](../../../meta/frontmatter_schema.md)).

## Continue from here

- In this pack: [`../scenario-and-foresight/SKILL.md`](../scenario-and-foresight/SKILL.md)
  (complementary, handles broader uncertainty),
  [`../strategic-options-evaluation/SKILL.md`](../strategic-options-evaluation/SKILL.md)
  (carries the effect-chain findings into an options comparison).
- Related skill in another pack:
  [`../../../ai-strategy-and-governance/skills/ai-opportunity-portfolio/SKILL.md`](../../../ai-strategy-and-governance/skills/ai-opportunity-portfolio/SKILL.md),
  [`../../../business-case-and-analysis/skills/risk-matrix-and-mitigation/SKILL.md`](../../../business-case-and-analysis/skills/risk-matrix-and-mitigation/SKILL.md)
- A ready-made skill chain for this situation: see [`../../../playbooks/`](../../../playbooks/)
- This pack's shared guardrails: [`../../CLAUDE.md`](../../CLAUDE.md)

## References

- [`../../references/`](../../references/) — the pack's shared background material
- [`../../CLAUDE.md`](../../CLAUDE.md) — the pack's shared guardrails