Activate when: user says 'and then what?', 'what are the second-order effects?', 'what could go wrong downstream?', 'what happens once everyone does this?',...
4 次安装2 星标
编程
thinking-second-order
试用When a change has effects past the immediate fix—incentives, scale, feedback—trace consequence chains with timing and probability before committing.
它能做什么
When a change has effects past the immediate fix—incentives, scale, feedback—trace consequence chains with timing and probability before committing.
技能文档
Second-Order Consequence Chains
Do not stop at the intended first effect. Trace what happens next across actors, time, and feedback until the chain stops changing the decision.
When to Use
- Strategic, policy, incentive, or architecture choices with lasting coupling.
- The obvious fix feels too easy or has known backfire patterns.
- Success or scale would create new problems (load, gaming, debt).
- Need to compare options by delayed effects, not only day-one benefit.
When NOT to Use
- Local reversible edit with no incentive or cross-component coupling—just ship and observe.
- Full system structure (stocks, many loops, leverage ranking) is the goal—use systems.
- Pre-mortem of failure modes for a plan already chosen—use pre-mortem.
- Pure mechanical changes (rename, format) with no behavioral effect.
Procedure
- State decision and first-order effect. One sentence each: action and intended immediate result.
- Chain "and then what?" At least two further orders. For each link record: effect, who responds, rough probability (high/med/low), timing (immediate / next cycle / at scale), and whether it feeds back into the original problem (reinforce or counteract).
- Expand affected parties. Who else reacts (users, operators, other teams, attackers, markets)? What incentives does the change create or destroy?
- Scale test. Ask what happens if everyone does this or usage grows 10x. Mark paths that only appear under scale or repetition.
- Prune and decide. Drop speculative links that do not change the choice. Keep only effects that alter go/no-go, design, or mitigations. Revise the action or add guards where second-order harm exceeds first-order gain.
Stop when further "and then what?" no longer changes the decision, or the remaining chain is pure speculation without mechanism.
Output
decision:
first_order:
chain:
- order: 2
effect:
actors:
p: high|med|low
when: immediate|next_cycle|at_scale
feedback: none|reinforce|balance
- order: 3
...
scale_if_universal:
revised_decision:
mitigations:
Verification
- Falsify: If no credible second-order path changes the choice, first-order is enough—stop inventing cascades. If the core issue is multi-loop structure rather than one decision's trail, switch to systems.
- Stop: End at the first order that no longer affects the decision; do not pad to a fixed depth.
- Over-application guard: No low-probability sci-fi chains. No treating parameter tweaks as deep strategy. Probability and timing required on kept links; omit decoration without mechanism.
相关技能
Use when forecasting, estimating, or sizing risk — anchor on base rates, give ranges, update prior→likelihood→posterior on evidence, and factor unmeasured quantities into order-of-magnitude bounds.
1 次安装
When the right response mode is unclear, classify the cause-effect domain first; decompose disorder.
1 次安装
Before heavy deliberation, classify the decision as cheap or costly to undo; decide two-way doors fast and stage one-way doors to preserve options.
1 次安装
Use under time pressure when the situation is still changing and you must act before certainty — cycle Observe→Orient→Decide→Act on ~70% confidence, then re-observe.
1 次安装
Before committing to a plan or launch, assume it already failed and reason backward through concrete causes — convert failure paths into mitigations, gates, and stop checks.
1 次安装