Alternatives Analysis Specification (General)
A decision-matrix-driven decision record: the decision is the output of a weighted comparison of viable alternatives, not an assertion made up front.
Method: A weighted decision matrix scored with the Weighted Sum Model (WSM), also known as Simple Additive Weighting (SAW), a standard multi-criteria decision analysis (MCDA) technique, paired with explicit sensitivity and robustness analysis (dominance check, winning-margin and critical-weight analysis, and Monte Carlo weight simulation).
Scope: This specification is domain-general and applies to decisions in any field. A software-specific variant contextualized to architecture and engineering is maintained separately.
Required Content
Each AA document must include:
- Clearly defined problem statement and background context.
- Detailed exploration of at least two viable options, including comparative analysis.
- Explicit identification of trade-offs, including considerations like cost, scalability, sustainability, and reversibility.
- Final decision with clear justification.
When to Perform an Alternatives Analysis
Apply these tests to a proposed decision. If any test is true, an AA is warranted before committing.
Trigger Tests
| # | Test | Ask yourself… |
|---|---|---|
| 1 | Novelty | Does it introduce an approach, tool, vendor, or capability not already in established use? |
| 2 | Reach | Does it materially affect more than one group, team, or stakeholder's responsibilities or interests? |
| 3 | Reversibility | Is the decision hard or costly to reverse once implemented (a "one-way door")? |
| 4 | Significant commitment | Does it commit substantial budget, time, or people, or create a long-term obligation? |
| 5 | Disruption | Does it replace a core component of how things are done, or change an established process, structure, or arrangement? |
Short-Circuit Rules
- Reused established approach, nothing new: no AA needed, record that rationale.
- Existing AA or decision already covers it: reference the prior document instead of duplicating.
Confidence Calibration
- Any single test clearly yes → perform the AA.
- Maybe on two or more tests → treat as warranted.
- All tests clearly no → no AA; record the rationale.
AA Template Structure
Metadata
| Field | Description |
|---|---|
| Summary | Summary of AA |
| Created | Date of creation |
| Status | DRAFT · READY FOR REVIEW ·
FINAL |
Background
Provide background information for this AA
Decision
Provide a decision summary.
Proposal
Write a detailed description of the proposal.
Consequences
Cost Estimate
Provide a high order level cost estimate of the recommended approach.
| Cost and Savings (near-term and long-term horizon) | Option 1 | Option 2 | Option 3 |
|---|---|---|---|
| Total Cost | |||
| Cost Savings |
Positive Impacts
- List positive impacts.
Negative Impacts
- List negative impacts.
Analysis
Alternative 1
- Pros:
- Cons:
Alternative 2
- Pros:
- Cons:
Decision Matrix
Every AA with two or more options must include a transposed decision matrix — evaluation criteria as rows, alternative options as columns. This layout allows criteria to be read naturally top-to-bottom while keeping columns narrow for side-by-side comparison. It follows established multi-criteria decision analysis (MCDA) conventions such as the weighted sum model and Kepner-Tregoe decision analysis, and renders cleanly in Markdown.
Schema
| Evaluation Criteria | Weight | Option A | Option B | Option C |
|---|---|---|---|---|
| Criterion 1 | \(W_1\) | \(S_{11}\) | \(S_{12}\) | \(S_{13}\) |
| Criterion 2 | \(W_2\) | \(S_{21}\) | \(S_{22}\) | \(S_{23}\) |
| … | … | … | … | … |
| Total Weighted Score | \(\sum W_i\) | \(V_1\) | \(V_2\) | \(V_3\) |
Where:
\[V_j = \sum_{i=1}^{n} (W_i \times S_{ij})\]
This is the Weighted Sum Model (WSM), also known as Simple Additive Weighting (SAW): each option's total is the weighted sum of its criterion scores.
Row Categories
Organize criteria into layers:
- Objective Fit — how completely each option satisfies the goals and requirements.
- Constraints & Resources — time, cost, available skills or people, risk tolerance.
- Quality & Outcomes — effectiveness, reliability, sustainability, adaptability, stakeholder satisfaction.
- Operational Fit — ongoing effort and maintenance, dependency or lock-in risk, ease of operation, monitoring and feedback.
When selecting criteria, ensure ongoing and non-obvious obligations (maintenance effort, risk, dependencies, feedback and monitoring, transition cost) are represented as criteria rows rather than overlooked.
Rules
- Normalize weights. All weights must sum to 1.0 (or 100).
- Standardize scoring. Use a uniform integer scale (1–5 recommended) across all cells.
- Anchor scores. Define what each score level means for each criterion (e.g., 1 = poor, 5 = excellent). Include the anchor definitions in a footnote or companion table.
- Include a summary row. The final row must show the total weighted score (\(V_j\)) for each option.
- Negative criteria. For criteria where lower is better (cost, risk, complexity), invert the scale so that a higher score still means "more desirable" — or document the inversion explicitly.
Robustness
A weighted score is only as trustworthy as its weights, which are the most subjective input in the model. Because weights can never be proven non-arbitrary on their own, every AA with two or more options must test how sensitive the result is to them. Report the following:
Dominance check. If one option scores at least as high as every other option on every criterion, it wins under any weights. When this holds, declare the dominant option and note that weighting is moot.
Winning margin. Report the gap between the top two options, both absolute and normalized to the scoring scale: \[\Delta = V_{(1)} - V_{(2)}, \qquad \bar{\Delta} = \frac{\Delta}{S_{\max} - S_{\min}}\] where \(V_{(1)}\) is the highest total weighted score, \(V_{(2)}\) the runner-up, and \(S_{\max}, S_{\min}\) are the bounds of the scoring scale (e.g., 5 and 1). Because weights sum to 1, \(\bar{\Delta}\) lies in \([0, 1]\). A margin is close when \(\bar{\Delta} < 0.05\) (within 5% of the scale).
Critical-weight (flip-point) analysis. For each criterion \(k\), compute the smallest change \(\delta_k\) to its weight that would reverse the top two options, holding scores fixed and renormalizing the remaining weights proportionally: \[\delta_k = \frac{\Delta}{\dfrac{\Delta - W_k d_k}{1 - W_k} - d_k}, \qquad d_k = S_{k,(1)} - S_{k,(2)}\] The criterion with the smallest feasible \(|\delta_k|\) — one that keeps \(W_k + \delta_k \in [0, 1]\) — is the most critical criterion. If that \(|\delta_k|\) is a small fraction of \(W_k\), the decision hinges on a contestable weight and must be discussed explicitly.
Monte Carlo weight simulation. Required when the margin is close (\(\bar{\Delta} < 0.05\)); recommended otherwise. To summarize overall robustness, perturb the weights and recompute the ranking many times:
- Sampling. Draw each weight from a symmetric band around its base value (e.g., ±20% relative) and renormalize to sum to 1, or sample the full weight vector from a Dirichlet distribution centered on the base weights. Hold scores fixed.
- Iterations. Run at least 1,000 trials (10,000 when the margin is close).
- Reproducibility. Fix and record the random seed so the result can be audited and regenerated.
- Report. For each option, the fraction of trials in which it ranks first ("win probability"). Treat a top option that wins in a clear majority of trials as robust; otherwise flag the decision as weight-sensitive and justify the final choice on other grounds.
Diagrams
Include diagrams or other visuals where they clarify context or distinguish options. At minimum:
- Context diagram — the decision in its surrounding environment: the people, groups, external factors, and boundaries it touches.
- Per-alternative sketch — for each viable option, a visual showing how its structure, flow, or arrangement differs from the others.
A simple table or hand sketch is acceptable. Where a reproducible format is preferred, use a code-first, text-based one (e.g., Mermaid) so visuals render in Markdown and can be generated and revised by both humans and automated agents. Agent-harness-specific visualization features (e.g., interactive widgets or canvases, such as those in Gemini or Claude) are equally acceptable where they aid understanding.