Overview

The Decision Architecture Analysis Framework (DAA) handles the case where the user faces a high-stakes decision they hold authority over and wants the integrated architecture across four analytical lenses rather than four separate reads to mentally combine. Constraints alone, probabilities alone, stakeholders alone, and failure pathways alone each produce useful but partial views; DAA composes them into a single document where each alternative carries all four lenses and the synthesis stages surface tensions where probability-weighted outcomes clash with binding constraints, where stakeholder impacts contradict the leading alternative, where pre-mortem failure modes invalidate the top-ranked option. The recommendation that emerges is one no single component could have produced — the dialectical product of probability-weighted outcomes × constraints × stakeholder impact × failure pathways.

The framework runs four full component modes and four synthesis stages. Stage 1 (decision-under-uncertainty) produces probability-weighted outcomes per alternative under the relevant uncertainty regime (risk vs. uncertainty vs. deep uncertainty), surfaces defer/sequence/hedge/buy-information alternatives the framing might exclude, and assesses value of information against cost of delay. Stage 2 (constraint-mapping) maps the deterministic tradeoff structure — hard constraints that invalidate alternatives, soft constraints with cost-of-violation, and no-lose elements valuable regardless of choice. Stage 3 (stakeholder-mapping) inventories the parties affected by each alternative, classifies salience along Mitchell-Agle-Wood dimensions (power × legitimacy × urgency), and surfaces absent or marginalized parties. Stage 4 (pre-mortem-action) stress-tests the leading alternative and one runner-up using past-tense prospective hindsight — imagine it’s twelve months from now and the chosen alternative has failed; produce the failure narrative, name plan-specific failure modes, identify leading indicators, distinguish pre-commitment mitigations from post-hoc remediations.

The four synthesis stages — decision-frame integration, stakeholder-impact overlay, failure-mode stress test, integrated decision architecture — do the actual integrative work. Synthesis Stage 1 integrates probability-weighted outcomes with binding constraints and surfaces tensions where attractive alternatives are invalidated by hard constraints or where constraint-clean alternatives are weak on outcomes. Synthesis Stage 2 overlays per-alternative stakeholder impact (beneficiaries with values honored; cost-bearers with values subordinated; power asymmetries) and revises the leading alternative if stakeholder analysis flips it. Synthesis Stage 3 stress-tests the leading alternative against the pre-mortem failure pathways — would a failure mode change which alternative leads? would a leading indicator firing make the runner-up superior? — and produces a revised ranking incorporating failure-pathway resilience. Synthesis Stage 4 produces the final integrated Decision Architecture Document with executive summary, decision frame, alternatives with probability-weighted outcomes, binding constraints, stakeholder impact per alternative, failure-mode stress-test findings, recommended alternative with residual risks named explicitly, decision-conditions-to-monitor as concrete observable signals, and confidence map per finding.

The framework’s load-bearing intellectual content is the integrate-don’t-concatenate discipline, the per-alternative stakeholder mapping rule, the plan-specific failure mode requirement, and the monitoring-vagueness defense. The integrate-don’t-concatenate discipline counters the most common failure mode (silo-aggregation) where a Decision Architecture Document reads as four reports stapled together rather than four lenses applied to each alternative. The per-alternative stakeholder mapping rule counters the stakeholder-disconnection failure mode where a generic stakeholder list disconnected from choice substitutes for impact-per-alternative. The plan-specific failure mode requirement counters the generic-trope failure mode where pre-mortems produce “scope creep” and “stakeholder misalignment” as failure modes rather than mechanisms specific to the actual plan. The monitoring-vagueness defense counters the failure mode where decision-conditions-to-monitor read as “watch how things develop” rather than concrete observable signals with thresholds.

DAA differs from Decision Clarity Analysis (DCA) in a load-bearing way: DAA recommends, DCA does not. DAA is for decisions the user holds; DCA is for decisions the user is producing analysis about for someone else’s hand. The two share component modes (constraint-mapping, stakeholder-mapping) but differ on synthesis stance — DAA produces an integrated recommendation with residual risks named; DCA produces a Decision Clarity Document for the third-party decision-maker without recommending. If during DAA execution it emerges the user is producing the document for a third party, the framework halts and routes to DCA.

The framework answers questions like: I’m trying to decide whether to accept a senior role at a startup or stay at my current job — I want the full architecture across stakeholders, constraints, what could go wrong, and probability-weighted outcomes integrated into one document. We’re choosing between rebuilding the system or refactoring — give me the integrated decision architecture rather than four separate analyses. I have a high-stakes decision and the stakes warrant 10+ minutes of analysis; produce one document I can act on with eyes open.

Systemic context

Decision Architecture Analysis is the depth-molecular operation in T3 (Decision-Making Under Uncertainty), sitting above constraint-mapping (T3-light, atomic, deterministic-tradeoffs) and decision-under-uncertainty (T3-thorough, atomic, probability-weighted) in the depth ladder. It is the heaviest analytical mode in T3. The framework consumes four component modes — decision-under-uncertainty (Stage 1), constraint-mapping (Stage 2), stakeholder-mapping (Stage 3, pulled from T8), and pre-mortem-action (Stage 4, pulled from T6) — and integrates them via four synthesis stages. The territory framework is Framework — Decision-Making Under Uncertainty.md. The sibling Wave 4 mode is Framework — Decision Clarity Analysis.md, which shares component modes but produces a Decision Clarity Document for a third-party decision-maker rather than an integrated recommendation. DAA is invoked from three paths: user invocation directly, pipeline-dispatch via the four-stage pre-routing pipeline classifying T3-decision-making at depth-molecular position, or handoff from a sibling mode (constraint-mapping, decision-under-uncertainty) that surfaced the decision warrants the full molecular pass. The framework draws on the Kahneman-Tversky bias catalog (foundational), Knightian risk-uncertainty-ambiguity (optional), expected-utility-theory (via decision-under-uncertainty), Klein’s pre-mortem methodology (via pre-mortem-action), and Mitchell-Agle-Wood / Bryson stakeholder-analysis frameworks (via stakeholder-mapping).

Ora’s output

Example question: I’m trying to decide whether to accept a senior engineering role at a Series B startup that’s offered me a 60% pay raise plus equity, or stay at my current FAANG job with a path to staff in 18 months. I want the full architecture — there are family considerations, the startup might fail, and I’m worried about regretting either choice.

Ora’s response (running DAA on the named decision):

The decision is yours; the stakes warrant the molecular pass; the stakeholder landscape and uncertainty regime are both substantial; DAA proceeds. I’ll work through the four stages and four synthesis stages.

Stage 1 (decision-under-uncertainty). Alternatives initially: A1 = take startup offer; A2 = stay at FAANG. Defer-prompt surfaces A3 = ask FAANG to accelerate timeline, A4 = take startup with negotiated cliff/buyout terms. Uncertainty classification: startup survives 18 months = uncertainty (50-70% per Series B base rates); FAANG path-to-staff materializes = uncertainty (60-75% per org culture). Probability-weighted outcomes: A1 expected value depends on liquidity event (deep uncertainty); A2 modal case +30% TC, low variance; A3 low downside, may not be granted; A4 cushions startup downside. VOI: waiting 60 days to see Q3 hiring results updates survival probability; cost of delay is offer expiry (offer remains open in 60 days: ~30%). Recommendation under Stage 1 alone: A4 leads on EV. Non-quantifiable: spouse’s career mobility constrained by startup location; identity tied to FAANG role.

Stage 2 (constraint-mapping). Hard constraints: family income floor for mortgage; spouse’s job within commuting distance. Soft constraints: equity vesting alignment with kids’ school timing. Per-alternative analysis (symmetric): A1 survives if cash component matches floor; A2 survives all; A3 survives all but signals departure to current employer; A4 survives if cliff/buyout negotiated. No-lose: prepare departure logistics regardless (~8 hours, valuable for any path).

Synthesis Stage 1 (decision frame integration). A1 fails one hard constraint if cash component ≤ floor — the EV story collapses unless cash-floor-conditional. A2’s outcomes are weakest but constraint-compliance is cleanest. A4 leads under integration: hedges both EV and constraint compliance. Tension surfaced: A1’s EV is highest only conditional on cash floor met — verify before integration ranks A4.

Stage 3 (stakeholder-mapping). P1 (User) — definitive. P2 (Spouse) — high power (veto on relocation), high legitimacy, high urgency. P3 (Kids) — low power, high legitimacy, medium urgency. P4 (Current FAANG manager) — medium power (can accelerate path-to-staff), medium legitimacy, low urgency. P5 (Startup founders) — medium power, medium legitimacy, high urgency. P6 (Future-self at year 5) — surfaced as absent party.

Synthesis Stage 2 (stakeholder impact overlay). A1: severe impact on P2 if relocation; P5 wins; P3 mixed. A2: P2 unaffected; P3 stable; P5 disappointed but no severe impact. A3: P4 may interpret as disloyalty — soft cost. A4: optionality protects P2 partially; P5 may decline hedged terms. Power asymmetry: P2 holds veto on A1; P5 holds time pressure constraining A3. Revised leading: A4 still leads if P5 accepts hedged terms; otherwise A2.

Stage 4 (pre-mortem-action). A4 imagined failure narrative: “Eighteen months in, the startup pivoted twice; cliff acceleration kicked in but equity was illiquid; spouse’s career suffered from relocation; user took FAANG re-entry at lower level than starting point.” F1 (assumption): pivots were market not product → leading indicator: hiring profile shifts in next two quarterly all-hands. F2 (interaction): cliff terms protected against involuntary departure not sustained underperformance → leading indicator: review framing language at 6-month mark. A2 imagined failure narrative: “Stayed at FAANG; staff promotion delayed by reorg; declined startup IPO’d at 6×; user developed quiet resentment; left at year 4 in worse market conditions.” F1 (context-shift): reorg delayed promotion → leading indicator: skip-level changes within 6 months. F2 (motivational): counterfactual envy → leading indicator: rumination frequency.

Synthesis Stage 3 (failure-mode stress test). A4’s F1+F2 jointly catastrophic if realized; reversibility low. A2’s F1 recoverable; F2 partially mitigated by knowing A4 was on offer and was hedged. Revised ranking: A2 rises if A4’s joint F1+F2 probability ≥ 30%; otherwise A4 leads. Decision turns on: P5’s openness to hedged terms × user’s actual estimate of joint F1+F2 probability.

Synthesis Stage 4 (integrated decision architecture). Recommendation: A4 (startup with hedged terms) IF P5 accepts cliff acceleration AND cash floor exceeds family income threshold AND user estimates joint F1+F2 probability ≤ 25%. Otherwise A2 (stay at FAANG with explicit conversation about path-to-staff timeline). Decision-conditions-to-monitor: hiring-profile shift at 3-month mark (F1 leading indicator); review framing language at 6-month mark (F2 leading indicator); spouse career satisfaction at 9-month mark (P2 impact); skip-level org changes within 6 months (for A2 if chosen). Residual risks accepted: deep uncertainty in startup liquidity event; counterfactual envy in either choice; spouse’s career mobility constrained in either case.

That is what DAA produces on a high-stakes personal decision. The recommendation is conditional on three observable conditions, not a clean “do X.” The residual risks are named explicitly rather than minimized. The decision-conditions-to-monitor are concrete observable signals with thresholds, not vague wait-and-see phrases. The user can act on the recommendation with eyes open about what they’re trading and what they’re watching for.

Commercial AI comparison

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How to use this framework

You can run the Decision Architecture Analysis pattern with any AI of your choice. The composition is single-pass through the four stages and four synthesis stages.

The prompt:

[Paste the framework specification]

Run DAA on this decision.

Decision: [The choice you’re making, framed as a question.]

Alternatives (optional): [If you have a list, provide it; the framework will test breadth and add the do-nothing baseline if absent.]

Criteria (optional): [If you have decision criteria, provide them.]

Stakeholder inventory (optional): [If you’ve named stakeholders, provide them.]

Time pressure (optional): [If the decision must be made by a specific date.]

The AI runs the four component stages, four synthesis stages, and produces the Decision Architecture Document with executive summary plus eight required sections (decision frame, alternatives with probability-weighted outcomes, binding constraints, stakeholder impact per alternative, failure-mode stress-test findings, recommended alternative with residual risks, decision-conditions-to-monitor, confidence map).

For best results:

  1. Confirm decision authority is yours. DAA recommends; DCA does not. If you’re producing the document for someone else’s hand, route to DCA instead. The framework will halt and re-route if it detects the user is the analyst rather than the decision-maker, but stating it up front is faster.
  2. Don’t suppress alternatives. When the framework surfaces defer/sequence/hedge/buy-information alternatives in Stage 1, take them seriously. The most common framing failure is binary (do A or don’t); the framework’s job is to widen the option set with creative third options before evaluation.
  3. Push back on silo-aggregation. If the synthesis stages read as concatenation (probability-weighted outcomes here, stakeholders there, pre-mortems over there), that’s the silo-aggregation failure mode. Ask explicitly which alternative leads under the integration of all four lenses, and where do they disagree?
  4. Insist on concrete decision-conditions-to-monitor. A monitoring condition like “watch how things develop” is unfalsifiable. Push for observable signals with thresholds — if hiring drops below X in Y months, the F1 leading indicator has fired.

The framework is deliberately tool-agnostic. The integrate-don’t-concatenate discipline, the per-alternative stakeholder mapping rule, the plan-specific failure mode requirement, and the monitoring-vagueness defense are conceptual disciplines that survive the lift to any environment.

Other examples

  • Architectural decision in software systems. A technical lead is choosing between rebuilding a legacy system or refactoring it incrementally. DAA runs all four stages: probability-weighted outcomes per alternative under risk regime (rebuild has higher variance; refactor has lower variance); hard constraints (deployment continuity, team capacity, deadline); stakeholder impacts per alternative (engineering team for rebuild, product team for refactor, ops for both); pre-mortem on rebuild surfaces specific failure modes (incomplete migration, knowledge loss in legacy code that no documentation captures). Synthesis surfaces tension: rebuild has higher EV but pre-mortem reveals catastrophic failure mode (incomplete migration with no rollback path); refactor has lower EV but failure modes are recoverable. Recommendation: refactor with explicit decision-conditions-to-monitor (refactoring velocity at 3-month mark; if velocity drops below X, escalate to rebuild). Demonstrates DAA on a technical decision where pre-mortem stress-test flips the leading alternative.

  • Organizational decision involving acquisition integration. A leader is choosing between three approaches to integrating an acquired team (full integration, hold-separate, gradual integration). DAA produces the integrated architecture with stakeholder impact per alternative (acquired team for hold-separate; existing team for gradual; both for full); pre-mortem on each surfacing plan-specific failure modes (cultural reject for full; identity drift for hold-separate; integration fatigue for gradual). Synthesis Stage 2 surfaces severe impact on acquired team if full-integration; Synthesis Stage 3 reveals the gradual approach’s failure mode (integration fatigue) is recoverable while full-integration’s failure mode (cultural reject) is not. Recommendation: gradual integration with decision-conditions-to-monitor (acquired team retention at 6-month mark; cultural conflict signals at 12-month mark). Demonstrates DAA on an organizational decision where stakeholder impact and failure-mode stress test together change the recommendation.

  • Resource allocation decision under deep uncertainty. A founder is choosing between three product directions for limited engineering resources. DAA runs all four stages with explicit deep-uncertainty classification on the demand side (no base rates available for the new product category). Defer/hedge alternative surfaced: build the smallest possible MVP for the highest-uncertainty direction to convert deep uncertainty into estimable uncertainty. Stakeholder impact (early adopters, existing customers, team) per alternative; pre-mortem on each. Recommendation: ship MVP for direction A (the deep-uncertainty path) while continuing direction B at reduced velocity; revisit at 90-day mark when MVP signal is in. Demonstrates DAA on a deep-uncertainty decision where the recommendation is itself a hedge that converts unknown unknowns into measurable signals.

Citations

The Decision Architecture Analysis Framework draws on the integrated decision-analysis tradition. The probability-weighted-outcomes work draws on von Neumann and Morgenstern’s Theory of Games and Economic Behavior (1944), Savage’s The Foundations of Statistics (1954), Raiffa’s Decision Analysis (1968), and Howard’s foundational decision-analysis articles (1966 onward) for expected-utility theory and decision-tree methodology; Knight’s Risk, Uncertainty, and Profit (1921) for the foundational distinction between risk (assignable probability) and uncertainty (estimable range); and Lempert, Popper, and Bankes’s Shaping the Next One Hundred Years (RAND, 2003) for robust decision-making under deep uncertainty.

The constraint-mapping work draws on Rumelt’s Good Strategy / Bad Strategy (2011) for the strategy-kernel test and the alternative-design discipline. The stakeholder-mapping work draws on Freeman’s stakeholder theory tradition, Mitchell, Agle, and Wood’s “Toward a Theory of Stakeholder Identification and Salience” (1997) for the power × legitimacy × urgency salience classification, and Bryson’s strategic-planning tradition for the power-interest grid. The pre-mortem-action work draws on Klein’s “Performing a Project Premortem” (Harvard Business Review 2007) for the prospective-hindsight discipline.

The synthesis-stage architecture (decision-frame integration → stakeholder-impact overlay → failure-mode stress test → integrated decision architecture) is internal to Ora and operationalizes the integration discipline that the four component traditions independently fail to accomplish — each tradition is strong on its own dimension but the integration across dimensions is where decision-makers most often need help.

The framework is single-author and originated 2026-05-01 as part of the analytical territory build-out (Wave 4 depth-molecular composition pattern, shared with argument-audit in T1 and domain-induction in T14). The framework’s character — recommend with eyes open, name residual risks explicitly, produce concrete decision-conditions-to-monitor — is the deliberate counter to clean-recommendation framing that omits residual risks, the most common failure of decision-aid output.

Downloads

  • Framework specification (PDF) — link to ora-ai.org canonical artifact when published
  • Framework specification (plain text) — link to ora-ai.org canonical artifact when published
  • Full white paper (PDF) — link when published