# Decision Architect > Structure a decision under uncertainty: enumerate options, model outcomes, assign probabilities, clarify values, and recommend a decision framework. Use when the user faces a complex choice with multiple options, uncertain outcomes, or unclear trade-offs and needs the decision laid out clearly before choosing. - Skill: `npbuilds/decision-architect` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add npbuilds/decision-architect` - Raw SKILL.md: https://api.skillmd.com/api/skills/npbuilds/decision-architect/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: npbuilds (https://skillmd.com/u/npbuilds) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/npbuilds/decision-architect --- # Decision Architect — The Cartographer Map the decision landscape before the user walks into it. Most bad decisions aren't caused by picking the wrong option — they're caused by not seeing the options clearly, not understanding the trade-offs, or not knowing what you actually value. The architect makes the invisible structure of a decision visible. ## Input From the decision-theory director or directly: - The decision to structure (what needs to be decided) - Context: timeline, constraints, who's affected, reversibility - Known options (if any — the architect may find more) - The user's stated objectives (what they're trying to achieve) ## Process ### Step 1 — Frame the Decision Before structuring, ensure the right question is being asked: - **Scope**: What exactly is being decided? (Not "what should I do with my career" but "should I take offer A or stay at current job?") - **Decision-maker**: Who has the authority to decide? - **Timeline**: When must a decision be made? Is delay an option? - **Reversibility**: Can this be undone? At what cost? - **Dependencies**: Does this decision depend on or affect other decisions? A well-framed decision is half-solved. Many "impossible" decisions become clear once framed precisely. ### Step 2 — Enumerate Options List all available options, including: - **The obvious options**: what the user is already considering - **The null option**: what happens if they do nothing / maintain status quo - **The creative options**: novel alternatives not yet considered (combine elements, sequence decisions, find a third way) - **The delay option**: is "decide later with more information" viable? For each option, note: - What it commits to and what it preserves - Whether it's reversible - What information it reveals (some options are informative even if suboptimal) ### Step 3 — Model Outcomes For each option, identify the plausible outcomes: ``` Option A: Outcome A1 (best case): [description] — Probability: [estimate] Outcome A2 (base case): [description] — Probability: [estimate] Outcome A3 (worst case): [description] — Probability: [estimate] ``` **Probability estimation principles:** - Use reference classes when available (what happened in similar situations?) - Distinguish uncertainty (unknown probabilities) from risk (known probabilities) - Acknowledge when you're estimating vs. when you have data - Watch for anchoring — the first number you think of is often wrong **Outcome assessment principles:** - Consider second-order effects, not just immediate results - Consider who is affected besides the decision-maker - Consider both tangible and intangible outcomes - Consider the outcome of the option *conditional on the world state*, not in isolation ### Step 4 — Clarify Values What does the decision-maker actually value? Often this is the hardest and most illuminating step. **Elicit value dimensions:** - What are you optimizing for? (return, safety, growth, freedom, relationships, time) - What's your risk tolerance? (risk-averse, risk-neutral, risk-seeking) - What would you regret most? (minimizing maximum regret is often more intuitive than maximizing expected value) - What's non-negotiable? (constraints that eliminate options regardless of payoff) **Common value conflicts in decisions:** - Security vs. opportunity - Short-term vs. long-term - Personal vs. collective benefit - Financial vs. non-financial value - Known-good vs. unknown-possibly-better If values are unclear, route to `ethics/values-excavator` for deeper excavation. ### Step 5 — Select Decision Framework Match the decision to the appropriate analytical framework: | Situation | Framework | How It Works | |-----------|-----------|-------------| | Probabilities estimable, outcomes quantifiable | **Expected Value** | Probability × payoff for each outcome; choose highest EV | | Outcomes quantifiable but probabilities unknown | **Minimax Regret** | For each option, calculate worst-case regret; minimize the maximum regret | | High uncertainty, catastrophic downside possible | **Maximin** | Choose the option whose worst-case outcome is least bad | | Decision is sequential / information will arrive | **Decision Tree / Real Options** | Map the decision sequence; value the option to learn and adapt | | Multiple competing objectives | **Multi-Criteria Decision Analysis** | Score each option against each criterion; weight criteria by importance | | Risk of ruin / non-ergodic situation | **Kelly Criterion / Ruin Avoidance** | Never bet enough to face ruin, even if EV is positive | Read `references/decision-frameworks.md` for detailed framework descriptions. ### Step 6 — Sensitivity Analysis Test how robust the recommendation is: - **What would change the answer?** Identify the assumption whose falsity would flip the decision - **How sensitive to probabilities?** If the key probability shifted by 20%, would the choice change? - **How sensitive to values?** If the user valued X slightly more than Y, would the choice change? - **What information would be most valuable?** What could the user learn that would most change the analysis? A robust decision survives sensitivity analysis. A fragile one depends on precise estimates — flag this. ## Output ``` DECISION ARCHITECTURE ───────────────────── Decision: [precisely framed question] Decision-maker: [who] | Timeline: [when] | Reversibility: [high/medium/low] Options: A. [option] — Reversible: [yes/no] | Commits to: [what] B. [option] — Reversible: [yes/no] | Commits to: [what] C. [null option / status quo] D. [creative option, if found] Outcome Model: Option A: [best/base/worst case with probabilities] Option B: [best/base/worst case with probabilities] ... Value Dimensions: Primary: [what matters most] Secondary: [what also matters] Constraints: [non-negotiables] Key trade-off: [the central tension] Recommended Framework: [name] — Because: [why this framework fits] Analysis: [Framework-specific analysis — EV calculation, regret matrix, decision tree, etc.] Sensitivity: The answer changes if: [key assumption] Most valuable information: [what to learn before deciding] Decision Map: If you value [X] most → [option] If you value [Y] most → [option] If uncertainty is the main concern → [option] ``` ## Error Handling **Decision is trivial:** If analysis reveals one option dominates on all dimensions, say so. Not every decision needs a framework — sometimes the answer is obvious once structured. **Too many options to analyze:** Group into categories, eliminate dominated options first (any option that's worse than another on all dimensions), then analyze the remaining contenders. **User can't articulate values:** Use comparison-based elicitation. "Would you prefer guaranteed $50K or a 50% chance of $120K?" reveals risk preferences. "If you had to sacrifice X or Y, which goes?" reveals value ranking. **Decision involves other agents' responses:** Flag that this is a strategic interaction and may benefit from game-theoretic analysis (cross-domain to game-theory). Decision theory handles individual choice; game theory handles interactive choice. ## Related Skills - **behavioral-game-theory** — Empirical findings from behavioral-game-theory (non-rational play, framing, reference points) feed directly into decision-architect nudge design and choice architecture.