Research replication
Choosing what deserves capital
Open the exact fictional inputs behind each worked study. Project cases use their companion model; auxiliary cash bridges retain a separate, verifiable calculation.
Return to the paper↗These are illustrative scenarios, not actual project or supplier data. Inputs and method boundaries govern each result.
Illustrative study
Illustrative balanced mandate
Expose the supplied score contributions before interpreting a preferred alternative.
What changes
- Test: Canonical individual weight shifts
- Held constant: The alternative set and every raw criterion value.
- Change: Each normalised weight moves up or down by the app sensitivity increment while other weights rescale proportionally.
Limits of the study
- All quantities are expressly illustrative assumptions, not observed project data.
- Min-max scales depend on the supplied alternative set. Scores cannot be compared across changed sets.
- The model is additive and compensatory; it does not determine an optimal funded portfolio, dependencies or a feasible schedule.
Open this case in the model↗Illustrative study
Illustrative impact-led mandate
Test whether a different legitimate priority changes the preferred alternative while supplied performance remains identical.
What changes
- Test: Compare with balanced-baseline
- Held constant: Every alternative name and raw performance value.
- Change: Preference weights only; this is stakeholder judgement, not new evidence.
Limits of the study
- All quantities are expressly illustrative assumptions, not observed project data.
- Min-max scales depend on the supplied alternative set. Scores cannot be compared across changed sets.
- The model is additive and compensatory; it does not determine an optimal funded portfolio, dependencies or a feasible schedule.
- No claim that this mandate represents an actual stakeholder or institution.
Open this case in the model↗Illustrative study
Illustrative dominated-set extension
Demonstrate that adding an inferior option can change the scales and preference scores of unchanged options.
What changes
- Test: Compare with balanced-baseline
- Held constant: Existing raw values and declared weights.
- Change: Only the candidate universe adds a dominated proposal.
Limits of the study
- All quantities are expressly illustrative assumptions, not observed project data.
- Min-max scales depend on the supplied alternative set. Scores cannot be compared across changed sets.
- The model is additive and compensatory; it does not determine an optimal funded portfolio, dependencies or a feasible schedule.
- An analytically irrelevant proposal should not be inserted solely to produce a convenient scale. The fixture illustrates that failure mode.
Open this case in the model↗Illustrative study
Illustrative close preference contest
Inspect ties and sensitivity reversals without describing the frequency of tested cases as a probability.
What changes
- Test: Canonical individual weight shifts
- Held constant: Every raw criterion value.
- Change: Only one preference weight moves per tested case.
Limits of the study
- All quantities are expressly illustrative assumptions, not observed project data.
- Min-max scales depend on the supplied alternative set. Scores cannot be compared across changed sets.
- The model is additive and compensatory; it does not determine an optimal funded portfolio, dependencies or a feasible schedule.
- A deterministic display tie-break is not an additional substantive decision criterion.
Open this case in the model↗Illustrative study
Illustrative dominance with ignored criteria
Show why a displayed winner can tie on weighted preferences while being dominated on the full raw criteria set.
What changes
- Test: Canonical weight shifts off the impact-only mandate
- Held constant: Both alternatives and their performance.
- Change: Positive weight is introduced to an ignored criterion.
Limits of the study
- All quantities are expressly illustrative assumptions, not observed project data.
- Min-max scales depend on the supplied alternative set. Scores cannot be compared across changed sets.
- The model is additive and compensatory; it does not determine an optimal funded portfolio, dependencies or a feasible schedule.
- Dominance is calculated across all raw criteria even when a criterion has zero weight. Its governance meaning therefore needs explicit interpretation.
Open this case in the model↗Illustrative study
Illustrative conservative evidence bound
Represent incomplete implementation evidence as an explicit conservative scenario, preserving known cost and impact assumptions.
What changes
- Test: Compare with evidence-optimistic
- Held constant: All weights; all costs and impacts; metering and inspection values.
- Change: Only digital-twin readiness and execution-risk assumptions.
Limits of the study
- All quantities are expressly illustrative assumptions, not observed project data.
- Min-max scales depend on the supplied alternative set. Scores cannot be compared across changed sets.
- The model is additive and compensatory; it does not determine an optimal funded portfolio, dependencies or a feasible schedule.
- The bounds are deliberately authored possibilities. They are neither measured confidence intervals nor estimated probability distributions.
Open this case in the model↗Illustrative study
Illustrative optimistic evidence bound
Determine whether resolving implementation uncertainty would change the decision under the declared preferences.
What changes
- Test: Compare with evidence-conservative
- Held constant: All quantities other than digital-twin readiness and execution risk.
- Change: The named implementation assumptions take the alternative bound.
Limits of the study
- All quantities are expressly illustrative assumptions, not observed project data.
- Min-max scales depend on the supplied alternative set. Scores cannot be compared across changed sets.
- The model is additive and compensatory; it does not determine an optimal funded portfolio, dependencies or a feasible schedule.
- Moving a score is not evidence collection; an actual decision would require records supporting the revised performance values.
Open this case in the model↗Illustrative study
Illustrative capital-constrained preference mandate
Separate preference for low initial cost from a solved budget-allocation problem.
What changes
- Test: Compare with balanced-baseline and impact-mandate
- Held constant: Every candidate and raw criterion value.
- Change: Only the declared preference weights.
Limits of the study
- All quantities are expressly illustrative assumptions, not observed project data.
- Min-max scales depend on the supplied alternative set. Scores cannot be compared across changed sets.
- The model is additive and compensatory; it does not determine an optimal funded portfolio, dependencies or a feasible schedule.
- A high capital-cost weight does not encode an actual budget constraint or select a feasible combination.
Open this case in the model↗Illustrative study
Illustrative dominated-option range reversal: original pair
Test whether adding an explicitly dominated alternative changes the relative ordering of unchanged eligible options under endogenous min-max scales.
What changes
- Test: Controlled alternative-set contrast
- Held constant: The original alternatives and all four declared weights.
- Change: The expanded case adds Screened option, which both original options weakly dominate on every raw criterion and strictly dominate on cost.
Limits of the study
- This is an explicitly fictional counterexample, not an empirical frequency or investment result.
- The unchanged app uses the supplied alternative set to set criterion endpoints. An eligibility screen and externally anchored value functions are governance and method choices outside this engine.
- The built-in individual weight shifts are selected diagnostics, not an exhaustive stability proof.
Open this case in the model↗Illustrative study
Illustrative dominated-option range reversal: expanded set
Test whether adding an explicitly dominated alternative changes the relative ordering of unchanged eligible options under endogenous min-max scales.
What changes
- Test: Controlled alternative-set contrast
- Held constant: The original alternatives and all four declared weights.
- Change: The expanded case adds Screened option, which both original options weakly dominate on every raw criterion and strictly dominate on cost.
Limits of the study
- This is an explicitly fictional counterexample, not an empirical frequency or investment result.
- The unchanged app uses the supplied alternative set to set criterion endpoints. An eligibility screen and externally anchored value functions are governance and method choices outside this engine.
- The built-in individual weight shifts are selected diagnostics, not an exhaustive stability proof.
Open this case in the model↗Illustrative study
A positive supplied weight remains part of the preference rule
Verify that a supported small positive criterion receives released weight proportionally, instead of activating the exactly-zero fallback.
What changes
- Change: Increase the impact weight from1e-9 to2e-9, or remove it exactly.
- Held constant: Cost weight1000 and the same three alternatives.
- Interpretation: Both positive-tail cases retain impact as the sole other preference; only the exactly absent tail invokes equal-thirds redistribution.
Limits of the study
- Artificial preference magnitudes diagnose implementation of the stated rule, not accepted stakeholder preferences.
- A small supplied weight can be deliberate or an input error. The computation preserves it; the committee must challenge its substantive meaning.
Open this case in the model↗