Piensalo strategy

piensalo-strategy

A structured reasoning program for strategy and decision-making, distilled from curated expert reasoning traces. Work the numbered steps in order; each step's output feeds the next. Do not skip steps silently - if one does not apply, say so in one line.

Primary workflow

1. Recover real objective - Per intent-clarity: decode what the requester actually needs before optimizing anything. Distinguish the literal words from the mission; if the request was corrected or rephrased, the delta between versions IS the intent.
2. Separate objective from proxy - Per intent-clarity's symptom-vs-mission rule and product-thinking: split the measurable proxy (the metric, the named fix, the requested feature) from the underlying goal it stands for.
3. Extract constraints - Enumerate hard constraints (correctness, budget, deadline, interfaces, irreversibility), soft preferences, and available resources.
4. Identify missing info - List the unknowns that would materially change the work.
5. Choose representation - Per structured-reasoning step 1: classify the problem and pick the frame that makes it inspectable — first principles, tradeoff, decision matrix, causal chain, evidence grading, risk/reward, or sequencing — or a domain formalism (equations, state machine, claim tree, scenario tree).
6. Determine verifiables - List which outputs can be checked mechanically (run it, compute it, diff it, grep it) versus only by judgment. Per live-state-truth and adversarial-verify: if a check is executable, execute it — attacking a claim in your head is weaker than running it.
7. Objective hierarchy - Build the objective hierarchy: terminal goal at the root, instrumental objectives beneath, current initiatives as leaves.
8. Causal model - Sketch the causal graph from actions to the terminal goal: what drives what, with sign, rough strength, and lag. Per structured-reasoning's first-principles and causal-chain frames: state the mechanism, not the correlation.
9. Find bottleneck - Identify the binding constraint: the single node in the causal graph where added effort most changes the terminal goal. Per leverage-first: effort on non-bottlenecks is motion, not progress — improving an unconstrained step buys nothing.
10. Scenario branches - Branch the future on the biggest unknowns: 3-5 scenarios with explicit trigger conditions and rough probabilities.
11. Generate candidates - Per divergent-ideation: diverge with judgment OFF — generate 8-15 raw candidates; the first ~3 are the obvious ones everyone has, the different-mechanism ideas live past them.
12. Compress to capsules - Compress each surviving candidate to a fixed-format capsule — claim, mechanism, key evidence, cost, dominant risk — so comparison is like-for-like.
13. Expected value comparison - Per structured-reasoning's risk/reward frame: score each strategy option across the scenario branches — upside x probability vs downside x probability, PLUS reversibility as a first-class criterion (a cheap reversible bet beats a slightly better irreversible one under uncertainty).
14. Cheapest discriminating test - Before committing real resources, design the cheapest real-world test that discriminates between the top options or resolves the unknown the sensitivity note says the decision hinges on.
15. Counterexample search - Per adversarial-verify: switch from author to attacker and assume there is a flaw.
16. Distill losers - Before discarding rejected candidates, extract their salvageable components — a mechanism, a constraint they handled better, a test they suggested.
17. Check assumptions - Per verification-discipline: classify each load-bearing claim as fact (verified this session), inference (derived — show the reasoning), assumption (stated, with breaks-if-false), or guess (flagged).
18. Calibrated answer - Per output-structuring: lead with the outcome — the first two sentences carry the answer — in the format most usable for the reader's next action.

Conditional moves

• When attack changed an option's scenario scores: return to Expected value comparison and rework from there.
• When no option's worst case is tolerable: return to Generate candidates and rework from there.
• When test design revealed an unmodeled scenario: return to Scenario branches and rework from there.

Output contract

A decision memo: objective hierarchy, causal model with the argued bottleneck, scenario-weighted EV table with reversibility and a sensitivity note, the recommended option, the cheapest discriminating test with its pre-registered decision rule and kill criteria, the discard log, and labeled assumptions.

Delivery notes (small-model packets)

If delegating steps to a smaller model, follow the small-model packet rules: one bounded objective per packet, all inputs named explicitly, facts separated from instructions, uncertainty marked 'UNCERTAIN: ...' rather than invented away.

Before answering

Check the draft against references/failure-checks.md (the failure-mode catalog) and references/verification.md (domain verification criteria). Repair procedures live in references/contracts.md.