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 "excerpt": "The multi-attribute rating technique (MART), best known as the Simple Multi-Attribute Rating Technique (SMART), is a decision-analysis method that rates alternatives against weighted attributes and sums the scores.",
 "snippet": "The multi-attribute rating technique (MART), best known as the Simple Multi-Attribute Rating Technique (SMART), is a decision-analysis method that rates alternatives against weighted attributes and sums the scores.",
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 "markdown": "# Multi-attribute rating technique\n\nThe multi-attribute rating technique (MART), best known as the Simple Multi-Attribute Rating Technique (SMART), is a decision-analysis method that rates alternatives against several weighted attributes and adds the weighted scores into a single overall value for each option. It belongs to the family of multi-attribute value theory (MAVT) methods within multi-criteria decision analysis (MCDA). The output is a ranked or scored set of alternatives that supports, but does not replace, the decision maker's judgment; the final decision should be made by the decision maker, not produced automatically by the model.<sup>[1](https://doi.org/10.1201/9781003015154-3)</sup><sup> • </sup><sup>[2](https://backend.orbit.dtu.dk/ws/portalfiles/portal/104276012/DTU_Transport_Compendium_Part_2_MCDA_.pdf)</sup><sup> • </sup><sup>[3](https://escholarship.org/content/qt134790mp/qt134790mp_noSplash_b85985d90fccb9b5fb06f40d38dc4ffa.pdf)</sup>\n\n| Key fact | Detail |\n|---|---|\n| Aggregation model | Linear additive: overall value is the sum of each criterion's score multiplied by its weight<sup>[2](https://backend.orbit.dtu.dk/ws/portalfiles/portal/104276012/DTU_Transport_Compendium_Part_2_MCDA_.pdf)</sup> |\n| Typical scales | 0–100 strength-of-preference scales per criterion, with weights normalized to sum to 1<sup>[4](https://researchonline.lse.ac.uk/id/eprint/12761/1/Multi-criteria_Analysis.pdf)</sup> |\n| Weighting schemes | Direct rating, ratio assignment, swing weighting, and rank-based (SMARTER) weights<sup>[5](https://www.mdpi.com/2076-3417/11/21/10397)</sup> |\n| Practical duration | A full social-program evaluation in as little as a week of concentrated effort; about two days in other application areas<sup>[6](https://www.ojp.gov/pdffiles1/Digitization/76128NCJRS.pdf)</sup> |\n| Additivity condition | Mutual preferential independence among attributes, with at least three attributes<sup>[7](https://www.sciencedirect.com/science/article/abs/pii/S0377221722010062)</sup> |\n| Known variants | SMARTS and SMARTER (1994), MACBETH (1994), GRAPA (1997)<sup>[8](https://doi.org/10.1006/obhd.1994.1087)</sup><sup> • </sup><sup>[9](https://doi.org/10.1016/0969-6016%2894%2990010-8)</sup><sup> • </sup><sup>[10](https://doi.org/10.1006/obhd.1997.2719)</sup> |\n\n## How it works\n\nMART decomposes the overall value of an alternative into a set of attributes, scores each alternative on each attribute, and aggregates. The additive value function is\n\n\\[ v(x) = \\sum_{i=1}^{n} w_{i} \\cdot v_{i}(x_{i}) \\]\n\nwhere \\( x_{i} \\) is the consequence of alternative \\( x \\) on attribute \\( i \\), \\( v_{i} \\) is a component value function scaled between 0 and 1, and the weights \\( w_{i} \\) are normalized to sum to one.<sup>[11](https://sal.aalto.fi/publications/pdf-files/rpoy97a.pdf)</sup> The best alternative is the one with the highest total score, \\( S_{j^{*}} = \\max_{j} \\left( \\sum_{i=1}^{n} w_{i} \\cdot r_{ij} \\right) \\), with non-negative weights summing to one.<sup>[7](https://www.sciencedirect.com/science/article/abs/pii/S0377221722010062)</sup>\n\nAdditivity is a condition, not a default: additive value functions have a sound theoretical justification when mutual preferential independence holds and there are at least three attributes.<sup>[7](https://www.sciencedirect.com/science/article/abs/pii/S0377221722010062)</sup> [Empirical evidence](https://www.edgepedia.org/empirical-evidence) suggests that errors from additive aggregation are very small in real settings, and smaller than the errors associated with wrongly applying more advanced aggregation models.<sup>[12](https://pmc.ncbi.nlm.nih.gov/articles/PMC4828475/)</sup>\n\n## How it is done\n\nA full multi-attribute utility evaluation runs through seven steps: identify the objects of evaluation and the evaluation's function; identify stakeholders; elicit the relevant value dimensions or attributes from stakeholders and organize them into a value tree; assess attribute importance weights; measure single-attribute value scores; aggregate weights with scores; and conduct a sensitivity analysis.<sup>[6](https://www.ojp.gov/pdffiles1/Digitization/76128NCJRS.pdf)</sup> Scoring commonly uses 0–100 strength-of-preference scales per criterion.<sup>[4](https://researchonline.lse.ac.uk/id/eprint/12761/1/Multi-criteria_Analysis.pdf)</sup>\n\nIn the SMART weighting procedure specifically, the analyst ranks the attributes, establishes a reference attribute, estimates the importance of the others relative to it, and normalizes each attribute's score against the total across all attributes to obtain weights.<sup>[5](https://www.mdpi.com/2076-3417/11/21/10397)</sup> Weights are normalized to sum to 1 at each level of the value tree, and final weights for twigs are obtained by multiplying through the tree (for example, .43 × .39 = .17).<sup>[6](https://www.ojp.gov/pdffiles1/Digitization/76128NCJRS.pdf)</sup> In group settings, swing weighting can be run with 100-point voting, discussion of outliers, revoting constrained to the group's ordinal ranking, averaging with normalization, and sensitivity analysis of unresolved disagreements.<sup>[13](https://web.mst.edu/lib-circ/files/Special%20Collections/INCOSE/Using%20the%20Swing%20Weight%20Matrix%20to%20Weight%20Multiple%20Objectives.pdf)</sup>\n\n## Origin\n\nThe method descends from multiattribute utility theory. [Ralph L. Keeney](https://www.edgepedia.org/ralph-l-keeney)'s 1972 paper \"Utility Functions for Multiattributed Consequences\" in Management Science developed utility functions for multiattributed consequences,<sup>[14](https://doi.org/10.1287/mnsc.18.5.276)</sup> and the 1976 Wiley book by Keeney and Raiffa, *Decisions with Multiple Objectives: Preferences and Value Tradeoffs*, became a standard reference that helped establish multiattribute value and utility theory as a discipline.<sup>[15](https://link.springer.com/article/10.1007/BF00126471)</sup><sup> • </sup><sup>[16](https://www.mcdmsociety.org/2025/01/12/short-mcdm-history/)</sup> Ward Edwards introduced multiattribute utility measurement as a practical procedure for social decisionmaking in a 1977 paper in *IEEE Transactions on Systems, Man, and Cybernetics*, with applications including coastal zone management and research-program selection.<sup>[17](https://doi.org/10.1109/tsmc.1977.4309720)</sup>\n\n## Variants\n\n**SMARTS and SMARTER** were introduced by Ward Edwards and F. Hutton Barron in a 1994 paper in *Organizational Behavior and Human Decision Processes* (volume 60, issue 3, pages 306–325).<sup>[8](https://doi.org/10.1006/obhd.1994.1087)</sup> SMARTS uses swing weights with linear single-dimension utility approximations, which are easier for decision makers to understand; SMARTER substitutes rank-based calculations, converting an ordinal swing ranking into cardinal weights via the rank order centroid method.<sup>[5](https://www.mdpi.com/2076-3417/11/21/10397)</sup><sup> • </sup><sup>[13](https://web.mst.edu/lib-circ/files/Special%20Collections/INCOSE/Using%20the%20Swing%20Weight%20Matrix%20to%20Weight%20Multiple%20Objectives.pdf)</sup> In 1997, Orfelio G León proposed GRAPA in the same journal as a successor to SMART.<sup>[10](https://doi.org/10.1006/obhd.1997.2719)</sup>\n\n**MACBETH** (Measuring Attractiveness by a Categorical Based Evaluation Technique), an interactive path toward constructing cardinal value functions, was published by C. Bana e Costa in 1994 in *International Transactions in Operational Research*<sup>[9](https://doi.org/10.1016/0969-6016%2894%2990010-8)</sup> and developed from the early 1990s by C.A. Bana e Costa and J.-C. Vansnick, later joined by J.-M. De Corte. It adopts the additive value model, in part to avoid ordinal-aggregation difficulties such as Condorcet's Paradox and Arrow's Theorem, and elicits value functions from qualitative judgments of value differences between pairs of attribute levels using seven semantic categories.<sup>[18](https://researchonline.lse.ac.uk/id/eprint/22761/1/MACBETH_LSE_working_paper_0356_30set.pdf)</sup><sup> • </sup><sup>[12](https://pmc.ncbi.nlm.nih.gov/articles/PMC4828475/)</sup>\n\nThe outranking method **PROMETHEE**, introduced by J.P. Brans, Ph. Vincke, and B. Mareschal in a 1986 paper in the *European Journal of Operational Research*, ranks projects without a full value function; theoretical work shows [PROMETHEE](https://www.edgepedia.org/promethee) can mimic simple additive rating and [simple additive weighting](https://www.edgepedia.org/simple-additive-weighting) algorithms.<sup>[19](https://doi.org/10.1016/0377-2217%2886%2990044-5)</sup><sup> • </sup><sup>[20](https://onlinelibrary.wiley.com/doi/10.1002/mcda.468)</sup> Full **MAUT** in the Keeney and Raiffa tradition takes uncertainty formally into account and allows non-additive attribute interaction, but is complex and best implemented by specialists; the simple linear additive model suits wider use.<sup>[4](https://researchonline.lse.ac.uk/id/eprint/12761/1/Multi-criteria_Analysis.pdf)</sup>\n\n## Applications\n\nMultiattribute evaluation was developed for social decision making and program evaluation: Edwards's 1977 paper applies it to coastal zone management and research-program selection,<sup>[17](https://doi.org/10.1109/tsmc.1977.4309720)</sup> and an Edwards and Newman chapter reports a real analysis of the Midwood-Kings Highway Development Corporation anti-crime project in Brooklyn, with attributes and weights supplied by the project director.<sup>[21](https://web.pdx.edu/~stipakb/download/PA557/ReadingsPA557sec7.pdf)</sup> The underlying theory has been applied to siting nuclear power facilities, Mexico City airport development, fire department policies, and school-system fund allocation.<sup>[22](https://pure.iiasa.ac.at/id/eprint/375/1/WP-75-053.pdf)</sup>\n\nIn **health technology assessment**, MAVT-based MCDA proceeds through problem structuring, model building, model assessment, model appraisal, and action plans; the resulting index score can serve as a measure of value, and purchasing costs can be combined with it into an incremental cost value ratio (ICVR) for priority setting.<sup>[12](https://pmc.ncbi.nlm.nih.gov/articles/PMC4828475/)</sup> A review of 36 healthcare MCDA studies found 29 included cost as a criterion and applied a \"maximizing value\" allocation, an approach the review's authors argue cannot adequately capture opportunity costs; several authors instead propose a cost-per-value allocation rule.<sup>[23](https://msh.org/wp-content/uploads/2020/02/baltussen_multicriteria_decision_analysis_to_support_health_technology_assessment.pdf)</sup>\n\n## Limitations and alternatives\n\n**Scale dependence.** A monotonic transformation of a constructed rating scale that preserves ordinal preferences can still change alternative rankings and cause preference reversals in the weight-and-rate method; the reversal rate depends on the initial score difference, and tied alternatives are most vulnerable.<sup>[7](https://www.sciencedirect.com/science/article/abs/pii/S0377221722010062)</sup> With incomplete information, normalization of additive value functions can itself produce preference reversals, and even sensitivity analyses can depend on the normalization chosen.<sup>[7](https://www.sciencedirect.com/science/article/abs/pii/S0377221722010062)</sup>\n\n**Weight misinterpretation.** Weights in a multi-attribute value function are swing weights, scaling factors that relate scores across criteria, not free-standing importance judgments; they cannot be assigned until criterion scales are defined, and an intrinsically important criterion that does not differentiate between options may be ranked quite low.<sup>[2](https://backend.orbit.dtu.dk/ws/portalfiles/portal/104276012/DTU_Transport_Compendium_Part_2_MCDA_.pdf)</sup> Assessing weights on importance alone, ignoring the range of each value scale, is a common analyst error.<sup>[13](https://web.mst.edu/lib-circ/files/Special%20Collections/INCOSE/Using%20the%20Swing%20Weight%20Matrix%20to%20Weight%20Multiple%20Objectives.pdf)</sup> After experiments with 139 participants, the P-SWING authors advise against pure swing-style elicitation \"on the grounds of misunderstanding and misinterpreting the relative nature of swing weights\"; other literature, however, describes swing weighting as the usual method for eliciting criteria weights in MAVT, so this remains an open disagreement.<sup>[24](https://helision.com/documents/10.1016j.knosys.2019.01.001.pdf)</sup><sup> • </sup><sup>[12](https://pmc.ncbi.nlm.nih.gov/articles/PMC4828475/)</sup>\n\n**Compared with alternatives.** Different MCDA models, including the Weighted Sum Model, Weighted Product Model, and AHP, can give conflicting rankings of the same alternatives even under certainty.<sup>[3](https://escholarship.org/content/qt134790mp/qt134790mp_noSplash_b85985d90fccb9b5fb06f40d38dc4ffa.pdf)</sup> AHP's distributive and ideal synthesis modes do not always yield the same ranking, and in distributive mode adding an inferior alternative can cause rank reversal.<sup>[3](https://escholarship.org/content/qt134790mp/qt134790mp_noSplash_b85985d90fccb9b5fb06f40d38dc4ffa.pdf)</sup> AHP also treats pairwise 1–9 responses as ratio judgments, which is inconsistent with the value function approach; REMBRANDT uses a logarithmic scale and the geometric mean to overcome some AHP mathematics.<sup>[2](https://backend.orbit.dtu.dk/ws/portalfiles/portal/104276012/DTU_Transport_Compendium_Part_2_MCDA_.pdf)</sup>\n\n## References\n\n1. [Simple Multi-Attribute Rating Technique, SMART (book chapter, Gomes & Martins, 2022)](https://doi.org/10.1201/9781003015154-3)\n2. [Multi-criteria decision analysis for use in transport decision making (DTU compendium)](https://backend.orbit.dtu.dk/ws/portalfiles/portal/104276012/DTU_Transport_Compendium_Part_2_MCDA_.pdf)\n3. [Multi-Criteria Decision Analysis: Limitations, Pitfalls, and Practical Difficulties (Kujawski)](https://escholarship.org/content/qt134790mp/qt134790mp_noSplash_b85985d90fccb9b5fb06f40d38dc4ffa.pdf)\n4. [Multi-criteria analysis: a manual (UK government / LSE copy)](https://researchonline.lse.ac.uk/id/eprint/12761/1/Multi-criteria_Analysis.pdf)\n5. [Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques (Applied Sciences)](https://www.mdpi.com/2076-3417/11/21/10397)\n6. [Multiattribute Utility Technology (MAUT) evaluation manual (Edwards et al., NCJRS/OJP)](https://www.ojp.gov/pdffiles1/Digitization/76128NCJRS.pdf)\n7. [Scale dependence in weight and rate multicriteria decision methods (EJOR)](https://www.sciencedirect.com/science/article/abs/pii/S0377221722010062)\n8. [Ward Edwards, F.Hutton Barron (1994). SMARTS and SMARTER: Improved Simple Methods for Multiattribute Utility Measurement. Organizational Behavior and Human Decision Processes.](https://doi.org/10.1006/obhd.1994.1087)\n9. [MACBETH — An interactive path towards the construction of cardinal value functions (International Transactions in Operational Research, 1994)](https://doi.org/10.1016/0969-6016%2894%2990010-8)\n10. [Orfelio G León (1997). On the Death of SMART and the Birth of GRAPA. Organizational Behavior and Human Decision Processes.](https://doi.org/10.1006/obhd.1997.2719)\n11. [On the convergence of multiattribute weighting methods (Pöyhönen, Hämäläinen)](https://sal.aalto.fi/publications/pdf-files/rpoy97a.pdf)\n12. [Value-Based Assessment of New Medical Technologies (MCDA/MAVT in Health Technology Assessment)](https://pmc.ncbi.nlm.nih.gov/articles/PMC4828475/)\n13. [Using the Swing Weight Matrix to Weight Multiple Objectives (INCOSE paper)](https://web.mst.edu/lib-circ/files/Special%20Collections/INCOSE/Using%20the%20Swing%20Weight%20Matrix%20to%20Weight%20Multiple%20Objectives.pdf)\n14. [Ralph L. Keeney (1972). Utility Functions for Multiattributed Consequences. Management Science.](https://doi.org/10.1287/mnsc.18.5.276)\n15. [Multiattribute utility theory: A survey (Theory and Decision, 1978)](https://link.springer.com/article/10.1007/BF00126471)\n16. [Short MCDM History, Multiple Criteria Decision Making (International Society on MCDM)](https://www.mcdmsociety.org/2025/01/12/short-mcdm-history/)\n17. [Ward Edwards (1977). How to Use Multiattribute Utility Measurement for Social Decisionmaking. IEEE Transactions on Systems Man and Cybernetics.](https://doi.org/10.1109/tsmc.1977.4309720)\n18. [An up-to-date overview of MACBETH (Bana e Costa, De Corte, Vansnick, LSE working paper)](https://researchonline.lse.ac.uk/id/eprint/22761/1/MACBETH_LSE_working_paper_0356_30set.pdf)\n19. [How to select and how to rank projects: The Promethee method (European Journal of Operational Research, 1986)](https://doi.org/10.1016/0377-2217%2886%2990044-5)\n20. [On the Similarities of Some Multi-Criteria Decision Analysis Methods (J. Multi-Criteria Decision Analysis, 2011)](https://onlinelibrary.wiley.com/doi/10.1002/mcda.468)\n21. [Multiattribute Evaluation (Edwards & Newman chapter, public administration readings)](https://web.pdx.edu/~stipakb/download/PA557/ReadingsPA557sec7.pdf)\n22. [Decisions with Multiple Conflicting Objectives: Preferences and Value Tradeoffs (Keeney & Raiffa working paper, IIASA WP-75-053)](https://pure.iiasa.ac.at/id/eprint/375/1/WP-75-053.pdf)\n23. [Multicriteria Decision Analysis to Support Health Technology Assessment Agencies: Benefits, Limitations, and the Way Forward](https://msh.org/wp-content/uploads/2020/02/baltussen_multicriteria_decision_analysis_to_support_health_technology_assessment.pdf)\n24. [P-SWING: a refined SWING-family elicitation method (Knowledge-Based Systems, doi:10.1016/j.knosys.2019.01.001)](https://helision.com/documents/10.1016j.knosys.2019.01.001.pdf)\n\n---\n*Topic: Encyclopedia › Society and history › Economics and business › Business and work*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n",
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 "speakable": "The multi-attribute rating technique, best known as the Simple Multi-Attribute Rating Technique, is a decision-analysis method that rates alternatives against weighted attributes and sums the scores."
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