DecisionMind · Academy
Use decision methods with understanding.
DecisionMind Academy explains, in plain language, the decision methods DecisionMind Crisp runs on classical (crisp) data: which one applies when, what you need in hand, common mistakes and field examples.
- Method cards
- 103
- Field examples
- 417
- Cited sources
- 3
Data type
All data types →DecisionMind Crisp works with classical numbers: a single, undisputed value in every cell.
Methods
All methods →Method cards told through cases. Each card carries the method's philosophy, how to read the output and a literature case; the step-by-step formulae close the card.
Ranking
60 methods
- TOPSISTOPSIS ranks alternatives by how far they sit from two hypothetical points built out of the best and worst value on every criterion: the alternative closest to the ideal and furthest from the worst comes out on top.
- VIKORVIKOR ranks alternatives by weighing their total distance from the ideal together with their distance on the single worst criterion; it looks for a compromise solution that "everyone can accept."
Outranking
9 methods
- PROMETHEE IIPROMETHEE II does not compare alternatives one by one against an ideal. Instead it compares them pairwise against each other, grading "how much better" on every criterion with a preference function. Summing these grades into a net flow yields a complete ranking.
- ELECTRE IIt does not rank alternatives; for every pair, it asks together "am I superior on enough criteria" (concordance) and "am I not very poor on any criterion" (discordance), and extracts a "kernel" of alternatives that no other alternative outranks.
Subjective weighting
14 methods
- AHPA subjective weighting method that sets criteria against one another through pairwise comparison, and from those comparisons derives both a weight vector and a consistency measure showing how far the underlying judgements conflict with one another.
- BWMA subjective weighting method that weights criteria through a non-linear model built from the comparisons an expert makes against only the most important and the least important criterion, and that asks for comparatively few judgements.
Objective weighting
15 methods
- Entropy WeightingA method that derives criterion weights not from expert opinion but from the data itself: the more a criterion separates alternatives from one another, the more weight it receives.
- CRITICA method that derives criterion weights from the data itself: a criterion earns more weight the more it separates the alternatives and the less it repeats what other criteria already say.
Efficiency
1 methods
Aggregation and voting
1 methods
Sensitivity
3 methods
- Kendall's TauA coefficient that compares two rankings pair by pair, reduces the difference between concordant and discordant pairs to a single figure, and measures how closely two rankings coincide.
- Kendall's WA method that reduces how closely the rankings given by three or more rankers (experts, methods, criteria) resemble one another to a single concordance coefficient between zero and one.
Where should you start?
Does each cell hold a single, undisputed number?
Classical (crisp) data: this is what DecisionMind Crisp works with.
Do you not know how much each criterion matters?
Start with objective methods that derive the weights from the data: Entropy, CRITIC.
Will experts decide how important the criteria are?
Subjective methods: AHP asks for pairwise comparisons, BWM for the best and worst criterion, SWARA for an order.
Can a strong criterion make up for a weak one?
If yes, compensatory methods (TOPSIS, SAW, VIKOR); if not, outranking methods (PROMETHEE, ELECTRE).