How to Build a Decision Matrix with Super Intelligence

How to build a decision matrix with Super Intelligence is not about turning a personal choice into fake mathematics. A good decision matrix makes criteria, weights, evidence and trade-offs visible. A bad one hides uncertain judgement behind precise-looking numbers.

Super Intelligence can help define criteria, normalise evidence, calculate weighted scores, run sensitivity analysis and expose where the result depends on one fragile assumption. The human still owns the values, the weights, the disqualifiers and the final choice.

In the eduKateSG life series, Super Intelligence, or SI, is our editorial name for practical AI assistance. This guide follows How to Compare Options with Super Intelligence. Do the qualitative comparison first. Build a matrix only when formal scoring adds clarity.

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What a Decision Matrix Is

A decision matrix places options in rows, criteria in columns, assigns importance to criteria and records how each option performs. A weighted matrix multiplies the performance score by the criterion weight and sums the results.

The arithmetic is simple. The difficult part is upstream: choosing criteria, deciding which are must-haves, defining score scales, sourcing evidence and assigning weights without smuggling the preferred option into the model.

The matrix is therefore a reasoning instrument, not an oracle.

The basic formula

Weighted score for an option = Σ (criterion weight × option score on that criterion).

If weights sum to 100%, a criterion weighted at 30% has three times the influence of one weighted at 10%, assuming the same score scale.

This is useful precisely because it makes hidden weighting visible. It is dangerous when arbitrary weights are mistaken for objective truth.

When a Matrix Helps

• several options share the same broad purpose;
• more than two or three criteria matter;
• trade-offs are difficult to hold in working memory;
• the user wants to make weighting explicit;
• the evidence can be normalised reasonably;
• the decision is important enough to justify the structure; and
• the matrix will be followed by sensitivity analysis rather than accepted blindly.

When a Matrix Does Not Help

• one option fails a must-have and should already be removed;
• most criteria are unknown;
• the options are not comparable;
• one qualitative factor dominates and cannot be represented responsibly;
• the decision is trivial;
• the score scale would be arbitrary; or
• the matrix is being built only to make a preferred choice look objective.

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Step 1: Define the Decision

Write the decision in one sentence and define the outcome.

Weak: “Which laptop is best?”

Stronger: “Which laptop best supports four years of writing, research, video calls and light data work within S$2,000 while remaining easy to carry?”

The stronger version tells the matrix what the criteria should serve.

Step 2: Apply Must-Haves Before Scoring

A matrix should not average away a fatal weakness.

If a required application does not run, software compatibility is a gate, not a 20% criterion.

If a course conflicts with an immovable work schedule, it may be disqualified before scoring.

If a travel option violates an entry requirement, it is not rescued by price.

Use an eligibility gate: PASS / FAIL / UNKNOWN. Resolve UNKNOWN where necessary before scoring.

Step 3: Choose Criteria

Criteria should be decision-relevant, distinct enough to avoid double counting and understandable enough to score consistently.

For a course: skill relevance, feedback, applied practice, schedule, total cost, credibility and completion risk.

For a laptop: compatibility, performance, battery, portability, display, warranty, repairability and total ownership cost.

For a job: work content, manager, autonomy, learning, pay, commute, stability and flexibility.

Avoid criteria that merely restate another criterion. “Portability” and “weight” may be duplicates unless portability includes size and battery as well.

Step 4: Define the Scale Before Scoring

A 1–5 scale works only if the numbers have meaning.

Example for schedule fit:

1: conflicts with fixed commitments.
2: possible only with major recurring disruption.
3: workable with moderate adjustment.
4: fits well.
5: fits naturally with substantial buffer.

Now a score of 4 means something observable.

Define anchors before scoring options to reduce winner-first reasoning.

Step 5: Assign Weights

Weights represent importance to this decision, not universal importance.

A traveller may weight accessibility heavily. Another may weight price. A student needing diagnostic feedback may weight class size more than a student needing routine revision.

A simple method is to distribute 100 points across criteria.

Do not let SI assign weights silently. Ask it to propose a starting distribution, then explain how the outcome changes under alternative user priorities.

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Step 6: Score with Evidence

Each score should be supported by a source, observation or explicit estimate.

Add an evidence-status field: CONFIRMED, ESTIMATED, OBSERVED or UNKNOWN.

If a job’s manager quality is unknown, do not give it a confident 4 because the interview felt positive.

If a laptop’s battery result comes from a specific independent test, note the source and test conditions.

If a course workload is estimated from the provider’s minimum hours, label the estimate.

The matrix should show uncertainty rather than laundering it into numbers.

Step 7: Calculate

SI can perform the arithmetic quickly, but verify consequential calculations. Make sure weights sum correctly, score scales are consistent and missing values are not silently treated as zero.

A missing score should usually remain missing or be handled through a scenario, not automatically penalised as though poor performance were confirmed.

Step 8: Run Sensitivity Analysis

The total score is the beginning of analysis, not the end.

Change the weights within plausible ranges.

Change uncertain scores.

Remove one criterion.

Ask which option wins under different reasonable settings.

If the winner changes easily, the decision is fragile. If one option remains strong across many plausible weightings, the result is more robust.

Step 9: Write the Decision Narrative

Explain the result without numbers.

“Option A wins because schedule fit and feedback are the two most important criteria, and its higher cost remains within the budget. Option B becomes preferable only if flexibility is weighted much more heavily or if live feedback proves less important than expected.”

This narrative proves that the matrix is understood rather than merely calculated.

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Worked Example: Course Decision Matrix

Options: Course A live, Course B self-paced, Course C short intensive.

Criteria and weights: relevance 25%, practice 20%, feedback 20%, schedule 15%, total cost 10%, portfolio evidence 10%.

The learner defines scoring anchors before evaluating.

Illustrative scores: A = relevance 5, practice 5, feedback 5, schedule 2, cost 2, portfolio 5. B = 4, 4, 2, 5, 5, 4. C = 4, 3, 4, 3, 3, 3.

The arithmetic may place A slightly ahead because feedback and practice carry high weights.

Sensitivity test: if schedule rises from 15% to 30% and feedback falls, B may become preferred.

The matrix therefore reveals the real decision: how much the learner values feedback relative to schedule flexibility.

The correct next step may be to test independent self-paced learning before locking the weights.

Worked Example: Laptop Decision Matrix

Must-have gates: software compatibility, budget ceiling, local warranty.

Criteria: performance 20%, battery 15%, weight 20%, display 10%, repairability 10%, ownership cost 15%, ports 10%.

Option A wins on weight and cost. Option B wins on performance and battery.

Sensitivity test shows that B wins only if performance weight exceeds about one-third of the decision. The user’s real workload rarely uses the extra performance.

The matrix helps expose that the original preference for B depended on a high implicit performance weight.

The user can then choose deliberately rather than treating “future-proof” as an objective fact.

Worked Example: Job Decision Matrix

Criteria: work content, manager, autonomy, learning, compensation, commute, stability and flexibility.

Problem: several criteria are poorly known for the external job.

A naive matrix scores them anyway. A better matrix marks unknowns and calculates scenarios: optimistic, neutral and conservative.

If the external job wins only under optimistic assumptions about manager and autonomy, the next action is more evidence gathering, not immediate acceptance.

SI can draft targeted questions and update the matrix after conversations.

The matrix becomes a research guide as well as a scoring tool.

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False Precision

The difference between 78.4 and 77.9 can look meaningful even when the inputs are subjective 1–5 ratings.

Round sensibly.

Use ranges.

Report ties when the difference is smaller than the uncertainty.

A matrix should clarify large trade-offs, not manufacture tiny ones.

Double Counting

Two criteria may represent the same underlying value.

A job matrix with “flexibility”, “work-life balance” and “schedule control” may triple-weight one concept.

A laptop matrix with “portability”, “weight” and “easy to carry” may do the same.

Ask SI to cluster criteria by underlying construct and identify overlap before weights are assigned.

Compensability

Weighted sums assume strength in one criterion can compensate for weakness in another.

Sometimes that is false.

A course cannot compensate for being impossible to attend by having excellent content.

A hotel cannot compensate for an accessibility failure with better breakfast.

A job cannot compensate for a non-negotiable legal or family constraint through salary.

Use gates and minimum thresholds before the weighted sum.

Nonlinear Preferences

The value of a criterion may not increase evenly.

Battery life from six to eight hours may matter greatly; from eighteen to twenty may not.

Commute from 20 to 40 minutes may be tolerable; from 60 to 80 may cross a personal threshold.

If the scale is nonlinear, define score anchors that reflect the actual utility rather than raw numbers.

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Uncertainty-Aware Matrices

Instead of one score, uncertain criteria can use ranges.

Manager quality: 2–4 until more evidence arrives.

Course workload: 6–10 hours per week.

Ownership cost: S$1,800–2,300 depending on repair.

SI can calculate best-case and worst-case totals or run scenarios.

If the preferred option changes across plausible ranges, the matrix should say the decision is unstable.

Confidence Weighting

Do not automatically multiply scores by confidence; doing so can create another layer of arbitrary arithmetic.

A simpler approach is to display confidence beside the score and run sensitivity on low-confidence fields.

High-weight, low-confidence criteria deserve the most investigation.

This is a useful rule: research where importance is high and confidence is low.

Threshold Matrices

Some decisions are better represented by minimum thresholds than rankings.

Example: choose a tuition option only if diagnostic feedback ≥ acceptable, schedule fit ≥ acceptable and travel ≤ threshold. Among passing options, compare cost and preferences.

This mirrors how many real decisions work: first qualify, then optimise.

SI can help separate the qualifying layer from the ranking layer.

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A Full Matrix Protocol

1. Define outcome.
2. Expand option set.
3. Apply must-have gates.
4. Choose non-overlapping criteria.
5. Define score anchors.
6. Assign user-owned weights.
7. Gather evidence and label uncertainty.
8. Score consistently.
9. Calculate and verify.
10. Run sensitivity and scenarios.
11. Write the plain-language decision narrative.
12. Record the review trigger.

This sequence protects the matrix from becoming a calculator attached to a weak decision model.

A Copyable Matrix Prompt

“Build a decision matrix for the options below, but do not score immediately. First restate the outcome, identify missing options, separate must-have gates from weighted preferences and check the criteria for overlap. Propose explicit score anchors and a first-pass weight set for me to approve. Label every score by evidence status. After calculation, run sensitivity analysis on the highest-weight and lowest-confidence criteria. Do not declare a winner if plausible changes flip the ranking. Finish with a plain-language explanation of the trade-off.”

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Decision Matrix Failure Modes

Winner-first weighting

Weights are adjusted until the preferred option wins. Repair: set weights before scoring and document why.

Too many criteria

The matrix becomes noise. Repair: keep only criteria that can change the decision.

Duplicate criteria

One value is counted several times. Repair: cluster underlying concepts.

Hidden gates

A fatal constraint is treated as a low score. Repair: disqualify before weighting.

Unknown scored as average

Missing evidence becomes an invented 3/5. Repair: keep UNKNOWN and run scenarios.

False precision

Tiny score differences are treated as meaningful. Repair: use ranges, rounding and tie language.

Weights assigned by SI

The model silently chooses the user’s priorities. Repair: the user approves weights explicitly.

No sensitivity analysis

The total is accepted as stable. Repair: vary weights and uncertain scores.

No decision narrative

Nobody can explain why the winner won. Repair: state the trade-off in ordinary language.

When to Stop Using the Matrix

A matrix should be abandoned when one decisive qualitative issue emerges, when most important information is unknown, when options change too rapidly for the model to remain current or when the ranking is so unstable that another experiment will teach more than more scoring.

The matrix is a temporary thinking structure. It does not need to survive after the decision unless the choice will be reviewed later.

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Thresholds and Utility Curves

Decision criteria do not always increase in value linearly. Some have thresholds. Once battery life clears a full working day, additional hours may matter much less. Once travel time exceeds a personal limit, fit may decline sharply. Once price crosses a hard budget, the option may become ineligible.

A matrix should therefore distinguish raw performance from human value. The raw number may rise smoothly while the value to the decision-maker changes in steps or curves.

Ask SI to help define the utility curve before scoring. “At what point does extra performance stop changing my decision?” is often more useful than “Which option has the highest specification?”

Floor thresholds

A floor threshold defines the minimum acceptable level. Example: a course must include at least one assessed practical project. Below the floor, the option is disqualified.

Saturation thresholds

A saturation threshold is the point beyond which extra performance adds little value. Example: battery beyond ten hours may not matter to someone who always has access to power.

Penalty thresholds

A penalty threshold is the point where the option becomes sharply worse. Example: a commute above sixty minutes may have a disproportionate effect on family time.

Encoding these thresholds makes the matrix more faithful to real preferences than blindly scaling raw data.

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Score Ranges Instead of Single Scores

When evidence is uncertain, one number can hide more than it reveals. Use score ranges.

If manager quality is only partly known, record 2–4 rather than 3. If future maintenance cost may vary widely, score cost under optimistic and pessimistic assumptions.

Then calculate best-case and worst-case totals for each option.

If Option A beats Option B even under A’s pessimistic range and B’s optimistic range, the ranking is robust. If the ranges overlap, more information may have decision value.

SI is especially useful here because it can run multiple scenarios quickly, but the ranges still need human justification.

Scenario Matrices

Instead of one matrix, build several matrices for distinct futures.

Base case

The most plausible current assumptions.

Stress case

A plausible downside environment: higher costs, longer commute, lower demand, more workload.

Upside case

A plausible favourable environment: stronger demand, more time, lower cost, better access.

Compare whether the preferred option changes across scenarios. An option that remains strong in all three is more robust than one that wins only in the base case.

This method is often more honest than squeezing every uncertainty into one average score.

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Rank Reversal: Why Matrices Can Change When You Add or Remove Options

Some scoring systems can produce rank reversal: Option A beats B, but adding or removing another option changes the relative ranking even though A and B themselves did not change.

This can happen when scores are normalized relative to the option set rather than against fixed anchors.

Protect against this by defining score anchors independently of the current options. A 5 for commute should mean the same thing whether there are two options or ten.

Ask SI whether the matrix uses fixed absolute anchors or option-relative normalization. Prefer fixed anchors when you want stable comparisons over time.

Avoid Hidden Weight Multiplication

Weights can be multiplied accidentally through the way criteria are structured.

Example: “family time”, “commute”, “working hours” and “flexibility” may all partially express the same underlying value: control over non-work time.

If each receives a separate large weight, that value can dominate the matrix more than intended.

Sometimes that is appropriate. The key is to know it is happening.

Ask SI to cluster criteria into underlying values. Then decide whether the apparent double counting reflects genuine distinct mechanisms or an accidental repetition.

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Decision Matrices for Groups

Group decisions are harder because the weights themselves may be contested.

Do not average everyone’s preferences immediately.

First show each person’s weighting separately.

A family comparing travel options may reveal that one person prioritises cost, another travel time and another accessibility. A team comparing software may reveal that engineering cares about technical control while operations cares about reliability and support.

The disagreement is useful information.

Consensus matrix

Create one shared weighting only after discussing differences. Record where compromise occurred.

Parallel matrices

Keep separate matrices for different stakeholders and identify options that perform acceptably across all of them.

Veto criteria

Some group members may have legitimate hard constraints—medical accessibility, budget ceilings, caregiving obligations—that should be treated as gates rather than diluted through averaging.

SI can organise the different views, but it should not decide whose preferences deserve more weight.

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Worked Matrix 4: Choosing a Tuition Format

Decision: select a support format for a Secondary student with a diagnosed algebra bottleneck and limited weekly time.

Eligible options: one-to-one tutoring, small-group tuition, large-group tuition, structured self-practice plus periodic tutor review.

Must-haves: targeted repair, feedback on working, sustainable timetable, independent transfer test.

Criteria and weights: diagnostic precision 25%, feedback quality 20%, independence support 20%, schedule fit 15%, cost 10%, peer-learning value 10%.

Suppose one-to-one scores highest on diagnostic precision and feedback, but lower on cost and peer interaction. Small group scores strongly across most criteria. Large group scores well on cost and peer environment but weakly on targeted repair.

The base-case matrix favours small group narrowly.

Sensitivity test: if the student’s independence is very low and immediate diagnostic repair is more important than assumed, one-to-one becomes first. If the algebra bottleneck stabilises after several weeks, independence support and cost become more important, moving the preferred format toward small group or structured self-practice.

The matrix reveals that the “best” tuition format can change by phase. The decision may therefore be sequential rather than permanent.

Worked Matrix 5: Household Move Options

Decision: choose between three moving arrangements.

Options: full-service mover, basic mover plus self-packing, or self-managed rental vehicle.

Criteria: total cost, physical effort, schedule control, risk to valuables, time required, reliability, ability to handle unexpected delay.

A family with young children may weight physical effort and reliability more heavily. A student household may weight cost and flexibility more heavily.

The same options therefore produce different matrix results without either family being “wrong”.

The matrix exposes that the difference comes from weights, not from disagreement about facts.

Sensitivity analysis can show whether a modest cost increase would buy a large reduction in coordination risk.

Worked Matrix 6: Selecting an SI Workflow

Decision: choose how to handle a recurring weekly report.

Options: fully manual, reusable prompt with manual source paste, connected read-only workflow, connected prepare-and-send workflow.

Criteria: total time, verification burden, privacy exposure, failure detectability, maintenance, control, convenience.

The most automated option may score highest on convenience and time but lowest on control and privacy. The manual option may score highest on control but lowest on time.

A connected read-only workflow can sit on the Pareto frontier: much of the convenience without broad action authority.

This illustrates why a matrix is valuable for technology decisions. The technically most capable option is not automatically the best fit.

The weights should reflect the consequence of the report, not enthusiasm for automation.

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The Decision Matrix Audit

Before trusting the result, audit the matrix itself.

• Does the decision statement match the real outcome?
• Were must-haves applied before scoring?
• Are any criteria duplicates?
• Do the weights add to the intended total?
• Can each weight be explained in plain language?
• Are score anchors defined before looking at the options?
• Are unknowns left visible?
• Are subjective scores labelled as subjective?
• Are current facts sourced?
• Are correlated criteria double counting a trade-off?
• Does sensitivity analysis test the most uncertain inputs?
• Is the winner stable or fragile?
• Would adding another irrelevant option change the ranking?
• Is there a no-action or delay option?
• Would a reversible experiment produce better evidence than deciding now?

If the audit exposes a weak criterion or invented score, fix the structure before discussing the winner.

Decision Matrices and AI Hallucination

A matrix can make hallucinated data look authoritative because false information is placed inside a precise table.

Verify current prices, specifications, deadlines, legal requirements, eligibility rules and other factual cells before allowing them to influence the score.

Ask SI to identify which cells came from your supplied facts, which from current sources, which from inference and which remain unknown.

Do not accept a sourced-looking table without checking the sources behind consequential claims.

The stronger the numerical presentation, the more important it is to inspect the factual foundation.

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Using a Matrix Over Time

A decision matrix can become a living decision record.

Date the evidence and weights.

When the situation changes, update only the affected cells or criteria.

Example: a job offer changes salary, a course changes timetable, a product price drops or a family constraint changes.

Recalculate and observe whether the conclusion changes.

Do not silently rewrite history. Keep the earlier version when the reason for the original decision matters.

This makes the matrix useful for review rather than only for initial choice.

Matrix Versioning

Use simple labels: v1 — initial evidence; v2 — after manager clarification; v3 — after salary revision.

For each version, note what changed and whether the preferred option changed.

If the ranking repeatedly flips after small updates, the decision is fragile. That itself is important information.

If the same option remains ahead despite substantial changes, confidence in robustness increases.

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From Matrix to Action

The matrix is not the end.

Translate the chosen option into a next action and completion test.

Course decision → confirm enrolment requirements and deadline.

Job decision → review contract terms and accept through the proper channel.

Purchase decision → verify model, seller, price and return terms before payment.

Tuition decision → define the first four-week learning objective and retest condition.

SI workflow decision → implement the smallest-permission version first.

A matrix that ends in another spreadsheet has not completed the decision loop.

Post-Decision Review

Later, compare the matrix assumptions with reality.

Which criterion mattered more than expected?

Which score was wrong?

Which weight did not reflect actual behaviour?

Which evidence source was most useful?

Which unknown resolved in a surprising direction?

Would you make the same choice with the information you had then?

Do not judge only by outcome. A sound matrix can lead to a disappointing result under uncertainty, and a poor matrix can get lucky.

The review improves future weighting and evidence standards.

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The Matrix Simplicity Rule

A good decision matrix should be as simple as the decision permits.

If three criteria resolve the choice, do not use twelve.

If a must-have eliminates two options, remove them.

If qualitative comparison is clearer than scoring, do not force numbers.

If sensitivity shows the choice is robust, stop refining decimal places.

Complexity in the matrix should correspond to complexity in the decision, not to the capabilities of the software.

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The Matrix Confidence Statement

Every finished decision matrix should end with a confidence statement written in plain language. Example: “Option B leads under the current weighting, but the result is sensitive to manager-quality uncertainty and the importance assigned to flexibility. If either assumption changes materially, Option A may become preferable.”

This sentence forces the numerical result back into human language. It reveals whether the matrix is robust, fragile or dependent on one uncertain cell.

If the confidence statement cannot be written without sounding more certain than the evidence, the matrix is being asked to do too much.

The strongest matrix does not merely produce a score. It explains why the score deserves—or does not deserve—confidence.

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The Final Human Override

A decision matrix may produce a result that the decision-maker does not want to follow. That reaction is useful information rather than proof that the matrix is useless.

Ask why the result feels wrong. Perhaps an important criterion is missing. Perhaps a weight does not reflect the real value structure. Perhaps one score is based on weak evidence. Perhaps the decision contains a non-quantifiable concern that should remain outside the matrix. Or perhaps the user is simply attached to a preferred option and the matrix is exposing that attachment.

Do not obey the spreadsheet automatically, and do not discard it automatically. Investigate the disagreement between the calculated result and human judgement.

If you override the matrix, record the reason in plain language. “I chose Option A despite the lower weighted score because the matrix could not adequately represent the importance of keeping this commitment reversible” is a legitimate decision record. “I just felt like it” may also be honest, but it should not be presented as an objective conclusion.

The matrix is strongest when it makes judgement more visible, including the moment when the human deliberately decides not to follow the numerical ranking.

The matrix should also state what evidence would invalidate its ranking. If one current unknown, changed constraint or revised weight could reverse the result, record that condition explicitly. A decision matrix is strongest when it shows not only why one option leads today, but also what would make another option rational tomorrow.

A useful final discipline is to keep the unrounded working, but present the conclusion at the precision the evidence deserves. If subjective scores and uncertain weights produce totals that differ by less than the plausible uncertainty, describe the options as effectively tied rather than declaring a mathematical winner. The purpose of the matrix is to expose judgement, not disguise it.

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The Matrix Review Trigger

A decision matrix should state when it becomes stale. Review it when a price changes materially, a timetable moves, a new option appears, a key criterion changes, an uncertain score becomes verified or the user’s priorities change. Do not preserve an old ranking merely because the spreadsheet still calculates cleanly.

A review trigger keeps the matrix connected to reality. It also prevents unnecessary re-analysis: if none of the load-bearing inputs changed, the decision usually does not need to be rebuilt from zero.

The matrix is a dated model of a decision, not a permanent verdict.

Frequently Asked Questions

What is a decision matrix?

A structured comparison that scores options against criteria, often using weights to represent importance.

Should all criteria be weighted?

No. Must-haves and disqualifiers should often be handled as gates before weighted scoring.

What score scale should I use?

A 1–5 or 1–10 scale can work when each value has clear anchors. The exact size matters less than consistent meaning.

Who should choose the weights?

The person or people responsible for the decision. SI can propose and explain weights but should not silently own them.

What if a criterion is unknown?

Keep it unknown, use a range or run scenarios. Do not invent a neutral score merely to complete the table.

What is sensitivity analysis?

Testing whether plausible changes in weights or uncertain scores change the preferred option.

What if two options tie?

Treat the tie as information. Use reversibility, value of information, qualitative trade-offs or a small experiment rather than forcing a numerical winner.

Can SI calculate the matrix?

Yes, but consequential arithmetic should be verified and the logic upstream of the calculation matters more than the arithmetic itself.

When is a matrix too complicated?

When the user cannot explain the criteria, weights and trade-off or when maintenance exceeds the decision value.

What comes next?

The next guide covers second-order thinking: what happens after the immediate effect of a decision.

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Helpful Reading

• How to Compare Options with Super Intelligence
• How to Use Super Intelligence to Challenge Your Assumptions
• How to Think More Clearly with Super Intelligence
• How to Leverage Your Life with Super Intelligence

Use the Matrix to Expose the Decision

A good decision matrix does not make judgement disappear.

It makes judgement inspectable.

The criteria show what matters.

The weights show how much it matters.

The scores show what the evidence currently suggests.

Sensitivity shows how fragile the ranking is.

The narrative shows whether the person actually understands the trade-off.

Use Super Intelligence to calculate and challenge the matrix. Keep the meaning of the weights and the final responsibility human.

Continue with How to Use Super Intelligence for Second-Order Thinking to examine what happens after the immediate result.