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 di

SKILL.md

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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, 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 — 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/)
  • 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). Don't add new fields to the frontmatter — name and description are the only ones allowed (see ../../../meta/frontmatter_schema.md).

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References