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cat-harness/skills/process/process-core/decision-methodology-selector.md— do not edit here.
Decision methodology selector
Input: a decision context — what is being decided, how many alternatives, how many criteria, available data, time constraints, stakeholder count, reversibility, uncertainty level.
Output: a ranked list of applicable methodologies, each with:
- the methodology name and its graph-node id
- why it fits this context (matched
applies-when) - why it does NOT fit the alternatives (matched
not-for) - complexity rating
- data requirements
Relationship to methodology-adoption
methodology-adoption carries the protocol —
four questions asked in order, the first yes decides. This skill carries the
implementation: given that the protocol routes the decision to a category
(computable, evidence-grading, decision-record, decision-analysis), which
specific methodology in the graph serves that category best for this context?
methodology-adoption names the lane; this skill names the runner.
Decision context schema
A decision context has these dimensions. Not all are required — an agent provides what it knows and the selector grades against what is declared.
| dimension | type | when it matters |
|---|---|---|
questionType | "computable" \| "evidence-grading" \| "decision-record" \| "decision-analysis" | Always — from methodology-adoption §”Choosing which applies” |
alternativeCount | number | MCDM methods scale differently |
criteriaCount | number | AHP pairwise comparisons grow as n² |
criteriaWeightsKnown | boolean | Some methods derive weights, others require them |
dataType | "quantitative" \| "qualitative" \| "mixed" | Aggregation methods need numbers |
stakeholderCount | number | Group methods (Delphi, ANP) vs solo |
timeConstraint | "minutes" \| "hours" \| "days" \| "weeks" | Eliminates complex methods |
reversibility | "reversible" \| "costly" \| "irreversible" | Higher stakes → more rigorous method |
uncertainty | "low" \| "moderate" \| "high" \| "deep" | Bayesian methods for deep uncertainty |
sequential | boolean | True when decisions recur or adapt over time |
Method families and when to use them
1. MCDM aggregation methods
When: multiple alternatives scored against multiple criteria, quantitative data available, goal is a ranking.
Source: Wang & Rangaiah, “Multi-Criteria Decision-Making: Aggregation-Type Methods” (arXiv:2509.06388).
| method | best for | avoid when | complexity |
|---|---|---|---|
| SAW (Simple Additive Weighting) | First pass, well-understood criteria, all benefit-type after normalisation | Criteria on incomparable scales without good normalisation | Low |
| MEW (Multiplicative Exponent Weighting) | Criteria with exponential importance differences | Any criterion value is zero (product collapses) | Low |
| AHP (Analytic Hierarchy Process) | Weights unknown, need stakeholder consensus on priorities | >9 criteria (pairwise comparisons explode: n(n-1)/2) | Medium |
| ANP (Analytic Network Process) | Criteria have interdependencies (feedback loops) | Simple independent criteria (overkill) | High |
| COPRAS | Benefit and cost criteria naturally separated | All criteria same direction | Medium |
| MOORA | Quick ranking with ratio normalisation | Need for weight derivation (requires pre-set weights) | Low |
| FUCA | Alternatives close in score, need tie-breaking | Very few alternatives (<3) | Medium |
| WASPAS | Combines SAW + MEW for robustness check | When SAW and MEW agree (no added value) | Medium |
2. Probabilistic / sequential decision methods
When: decisions recur, data arrives over time, exploration-exploitation trade-off matters, experiments are costly.
Source: Kristiadi, “Introduction to the Analysis of Probabilistic Decision-Making Algorithms” (arXiv:2508.21620).
| method | best for | avoid when | complexity |
|---|---|---|---|
| Multi-armed bandits (UCB, Thompson sampling) | Repeated choice among options with unknown payoffs | One-shot decisions | Medium |
| Bayesian optimisation | Expensive-to-evaluate black-box functions, few evaluations | Cheap evaluations (just try them all) | High |
| Tree search (MCTS) | Sequential decisions with branching outcomes | No natural tree structure | High |
| Contextual bandits | Payoff depends on observable context | Context is not available or not meaningful | Medium |
Two caveats on the source, measured 2026-09-30. The monograph’s abstract
promises “bandit algorithms, Bayesian optimization, and tree search
algorithms”, but v2 as ingested has no tree-search chapter — its sections
run decision theory, concentration inequalities, frequentist bandits
(explore-then-exploit, UCB), Gaussian processes, and discrete then continuous
Bayesian optimisation. And contextual bandit occurs nowhere in it. So of
the four rows above, two are backed by the cited source and two are not.
3. Social / behavioral decision methods
When: multiple stakeholders, group consensus needed, behavioral biases must be accounted for.
Source: Ravichandran, “Algorithmic Approaches to Sequential Decision-Making
and Social Epistemology” (arXiv:2607.20636) — for grit-aware evaluation
only. Checked against the ingested text 2026-09-30: grit occurs in 35 of
its sections; Delphi occurs in none, and nominal group in none. Those
two come from the group-consensus literature and are UNSOURCED here; the
attribution above covered all three rows until this was measured.
| method | best for | avoid when | complexity |
|---|---|---|---|
| Delphi method | Expert consensus without groupthink | Time pressure (<1 day) | Medium |
| Nominal group technique | Structured brainstorming → voting | >15 participants | Low |
| Grit-aware evaluation | Decisions requiring sustained investment | Reversible/low-stakes | Medium |
4. Classical decision methods — UNSOURCED
When: well-understood problem structure, textbook applicability.
No source in this repository. Nothing in library/ covers cost-benefit
analysis, decision trees, game theory or expected utility, and no methodology
node renders any of them. The rows below are textbook recall, not adopted
method — see §”What this does NOT cover”.
| method | best for | avoid when | complexity |
|---|---|---|---|
| Cost-benefit analysis | Monetisable outcomes, policy decisions | Non-quantifiable values | Low |
| Decision trees | Sequential binary/multi-way choices with probabilities | Too many branches (>50 leaves) | Medium |
| Game theory / minimax | Adversarial settings, zero-sum contexts | Cooperative settings | High |
| Expected utility | Single decision under known probability distributions | Deep uncertainty, unknown distributions | Low |
5. Hybrid / distance-based MCDM — UNSOURCED
When: reference-point comparison (ideal solution), outranking rather than scoring.
No source in this repository, and the gap is a known one rather than an oversight: the MCDM source ingested here is Chapter 8, “selected aggregation-type” methods by its own abstract, so outranking (ELECTRE, PROMETHEE) and distance-based (TOPSIS, VIKOR) are somebody else’s chapter. TOPSIS appears in that chapter only inside a reference title. The rows below are textbook recall — see §”What this does NOT cover”.
| method | best for | avoid when | complexity |
|---|---|---|---|
| TOPSIS | Ranking by distance to ideal/anti-ideal solution | Criteria weights uncertain (use AHP first) | Medium |
| ELECTRE | Partial ordering, incomparability is valid | Need a total ranking | High |
| PROMETHEE | Preference functions on each criterion | All criteria are simple linear | Medium |
| VIKOR | Compromise solution for conflicting criteria | Stakeholders refuse compromise | Medium |
What this does NOT cover — bean 7e59, measured 2026-09-30
The title of the bean that commissioned this skill claims it “covers all
methodology families with when-to-use criteria”. Checked against the three
sources now that they are in library/, that does not hold, and the honest
shape is:
| methods | backed by an ingested source | |
|---|---|---|
| §1 MCDM aggregation | 8 | yes — mcdm-aggregation |
| §2 probabilistic | 4 | 2 of 4 — bandits and Bayesian optimisation |
| §3 social / behavioural | 3 | 1 of 3 — grit-aware evaluation |
| §4 classical | 4 | no |
| §5 distance-based / outranking | 4 | no |
| total | 23 | 11 |
An unsourced row is not thereby wrong — these are standard methods and the
guidance in them may be perfectly good. It means something narrower and worth
saying: nobody here has read a source for it, no methodology node renders it,
and methodology-adoption’s “never adopt a method by naming it” applies.
The output schema already makes this structural, and that is the sharper
point. MethodologyRecommendationSchema requires methodologyId — “the
methodology’s name from its front matter” — so a recommendation must name a
node in the methodology graph. No individual method above has one. The
graph’s nodes are families and adopted processes (mcdm-aggregation,
probabilistic-decision-analysis, adequacy-for-purpose-modelling,
kepner-tregoe, dmn, swot, madr, raci/rasci, …), so the tables in
this skill are at METHOD granularity while the declared output is at NODE
granularity.
Two consequences, and neither is cosmetic. A recommendation of “use TOPSIS” or
“run a Delphi” cannot be expressed in the output shape — there is no id to
put in methodologyId. And an implementation that emits one anyway is
inventing an id, which is how a consumer ends up resolving a methodology that
does not exist.
Owner ruled 2026-09-30: FAMILY plus prose. The selector returns a
methodology node that exists — mcdm-aggregation,
probabilistic-decision-analysis, kepner-tregoe, dmn and the rest — and
names the within-family method in rationale, as prose. The 23 rows above are
material for that prose. They are not a menu of returnable answers, and no
individual method gets a node.
Three things follow, and the third is the one to keep in view:
methodologyIdis always resolvable. Every value the selector can emit is a node in the graph, so a consumer that follows the id finds a document.- An unsourced row may still be named in prose, and must be named as what it is. “Of the distance-based family, TOPSIS is the usual first reach — note that nothing in this repository’s library covers it” is honest; “use TOPSIS” as a returned recommendation is not.
- A request that lands wholly outside the sourced families gets a refusal,
not the nearest fit.
SelectionResultSchemaalready carries the field for it — the explanation for when no methodology fits. An agent asking for an outranking method is told none is adopted here.
And these families are entirely absent, even as recall: ambiguity and robust decision-making (maximin under unknown distributions), real options, voting rules and social choice beyond the thesis’s cascades, multi-objective optimisation proper (Pareto fronts rather than scalarisation), and satisficing / bounded-rationality methods.
Selection algorithm
function selectMethodology(context: DecisionContext): MethodologyRecommendation[] {
// Step 1: Route via methodology-adoption protocol
const lane = routeByQuestionType(context);
// Step 2: Load methodology graph nodes
const nodes = readMethodologyGraph();
// Step 3: Classify each node against the context
const classified = nodes.map(n => ({
...n,
matchTier: classifyFit(n, context), // primary | secondary | excluded
rationale: explainFit(n, context)
}));
// Step 4: Return primary matches first, then secondary. Excluded are named
// but NOT recommended — per methodology-adoption, refusals are stated.
return classified.sort((a, b) =>
tierOrder(a.matchTier) - tierOrder(b.matchTier)
);
}
Rules
- Never blend. The output is one or more methods, each whole. A composite is a house method that §methodology-adoption forbids creating here.
- Name refusals. Every recommended method comes with its
avoid when, matched against the context. If a method’savoid whenfires, say so rather than omitting silently. - Cite the source. Each recommendation names the paper or standard the method comes from.
- Report no-match. If no methodology fits, that is the output — §”If none fits, that is a finding, not a licence.”
- The graph is the catalogue. Do not recommend methods not in the methodology graph. If a paper describes a method worth adopting, the output is a recommendation to ingest it via §methodology-adoption, not to use it directly.