Cardinal and Rank Ordering of Criteria — Addressing Prescription within Weight Elicitation

Larsson, Aron; Riabacke, Mona; Danielson, Mats; Ekenberg, Love

doi:10.1142/s021962201450059x

Cited by 14 publications

(9 citation statements)

References 48 publications

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“…A surrogate weight s w i for criterion i is given by a function of i and the total number of criteria N and the number of preference strength steps s ( i ) between of criterion i and its nearest ranked criterion such that s w i = f ( i , N , s ( i )) for each criterion i (see Danielson & Ekenberg, for details). Cardinal imprecision can then be modelled in terms of imprecision constants such that w i ∈ [ s w i − α , s w i + α ] for some constant α ∈ [0,1] together with comparative statements w i > w j to ensure that weight variables cannot assume values inconsistent with the provided ranking; by operating with comparative statements only, such that the ranking would simply be translated into a comparative constraint w i > w j + g ( s ( i )) where g ( s ( i )) is a function of the number of preference strength steps s ( i ) between of criterion i and its nearest ranked criterion, for all criteria pairs whenever criterion i is deemed more important than criterion j (see Larsson, Riabacke, Danielson, & Ekenberg, for details on the properties of such a function).…”

Section: Impact Assessment and Preferencesmentioning

confidence: 99%

“…by operating with comparative statements only, such that the ranking would simply be translated into a comparative constraint w i > w j + g ( s ( i )) where g ( s ( i )) is a function of the number of preference strength steps s ( i ) between of criterion i and its nearest ranked criterion, for all criteria pairs whenever criterion i is deemed more important than criterion j (see Larsson, Riabacke, Danielson, & Ekenberg, for details on the properties of such a function).…”

Section: Impact Assessment and Preferencesmentioning

confidence: 99%

“…Both approaches require the same information from the decision maker, that is, an ordinal ranking with ordinal preference strength information, and both approaches are compatible with the Delta framework for computational decision analysis because they require computational decision analysis with the functionality of expressing points within the solution space acting as “more reliable” points (the surrogate values) and operating upon linear constraints. However, from a validation perspective, the Approach 2a of Danielson and Ekenberg () has a stronger case since it has been explicitly shown to provide more equitable weights compared to other surrogate weight methods whereas the validation of the approach presented in Larsson et al () focused on decision makers' capability of providing rankings. In the implementation described in Section 4, the Approach 1b is therefore selected for implementation.…”

Section: Impact Assessment and Preferencesmentioning

confidence: 99%

See 2 more Smart Citations

Policy analysis on the fly with an online multicriteria cardinal ranking tool

Larsson

Fasth

Wärnhjelm

et al. 2018

Multi Criteria Decision Anal

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This paper reports upon the deliverables and learnings from a project within decision analysis for participatory planning and policy analysis on the municipal level. The project aimed to develop and utilize an online multicriteria decision analysis tool for evaluations of policy alternatives with respect to municipal commercial development policies, acknowledging the objectives and preferences from various local and national stakeholders. The tool itself relied solely on that the users supplied cardinal ranking statements in the appraisal of alternatives and in the ranking of criteria and the surrounding method has been used in three cases. In each case, several significant insights obtained in the decision‐modelling workshops were emphasized by the participants. The better understanding of the general decision situation, preference structures, and possible strategies was highly appreciated. The perception was also that this enabled a better understanding of conflicting issues, even when these were not entirely resolved.

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Section: Impact Assessment and Preferencesmentioning

confidence: 99%

Section: Impact Assessment and Preferencesmentioning

confidence: 99%

Section: Impact Assessment and Preferencesmentioning

confidence: 99%

See 1 more Smart Citation

Policy analysis on the fly with an online multicriteria cardinal ranking tool

Larsson

Fasth

Wärnhjelm

et al. 2018

Multi Criteria Decision Anal

Self Cite

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show abstract

“…Complete information methods require the decision maker to have a well-defined and stable preference structure, which makes the process cognitively demanding, since it requires a lot of precision, time and effort from the DM (Belton & Stewart, 2002, Larsson et al, 2015. The DM's difficulty translates into high rates inconsistencies such as those found in the tradeoff procedure (Weber & Borcherding, 1993) and may explain the discrepancy between the number of theoretical models used in decision making in relation to their actual applications (Weber, 1987).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Flexible and Interactive Tradeoff Method Based on Numerical Simulation Experiments

et al. 2020

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The Flexible and Interactive Tradeoff (FITradeoff) method is a multicriteria decision making/aiding (MCDM/A) method that uses partial information about the decision maker's (DM's) preferences in order to build a recommendation. This method preserves the strong axiomatic structure of the traditional tradeoff procedure, with an interactive and flexible process that demands less cognitive effort from the DM. Although FITradeoff has already been applied for aiding several practical decision situations, there is no previous study that tests the performance of this method with respect to expected theoretical benefits such as time and effort reduction. In this context, this paper presents the results of a simulation experiment that aims to analyze the behavior of FITradeoff in a wide range of scenarios. Computational constraints were identified in the simulation process, such as the number of simulations for each iteration. Despite the memory limitation of the software used, the number of total simulations performed was greater than what is commonly found in literature. We investigate how the performance of FITradeoff can be affected by changes in number of criteria, number of alternatives and weight pattern, and therefore it is possible to have a deeper understanding of the method and its main features. Therefore, this work focuses on studying, through simulations, the behavior of the multicriteria method FITradeoff, developed by De Almeida et al. (2016), filling a lack in the literature by analyzing said method in a wide array of scenarios by a simulation process in order to bring a better understanding of the method, as well as to validate it in a simulation study.

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“…The CAR method has been demonstrated using both linear inequalities to represent cardinal ranking statements [34] and closed formulas for obtaining surrogate weights [10,30]. More recently, rank-based methods have been suggested for probability elicitation as well, with a particular aim for use in time scarce environments [35].…”

Section: Rank-based Elicitationmentioning

confidence: 99%

Measuring Conflicts Using Cardinal Ranking: An Application to Decision Analytic Conflict Evaluations

Fasth

Larsson

Ekenberg

et al. 2018

Advances in Operations Research

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One of the core complexities involved in evaluating decision alternatives in the area of public decision-making is to deal with conflicts. The stakeholders affected by and involved in the decision often have conflicting preferences regarding the actions under consideration. For an executive authority, these differences of opinion can be problematic, during both implementation and communication, even though the decision is rational with respect to an attribute set perceived to represent social welfare. It is therefore important to involve the stakeholders in the process and to get an understanding of their preferences. Otherwise, the stakeholder disagreement can lead to costly conflicts. One way of approaching this problem is to provide means for comprehensive, yet effective stakeholder preference elicitation methods, where the stakeholders can state their preferences with respect to actions part of the current agenda of a government. In this paper we contribute two supporting methods: (i) an application of the cardinal ranking (CAR) method for preference elicitation for conflict evaluations and (ii) two conflict indices for measuring stakeholder conflicts. The application of the CAR method utilizes a do nothing alternative to differentiate between positive and negative actions. The elicited preferences can then be used as input to the two conflict indices indicating the level of conflict within a stakeholder group or between two stakeholder groups. The contributed methods are demonstrated in a real-life example carried out in the municipality of Upplands Väsby, Sweden. We show how a questionnaire can be used to elicit preferences with CAR and how the indices can be used to semantically describe the level of consensus and conflict regarding a certain attribute. As such, we show how the methods can provide decision aid in the clarification of controversies.

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Cardinal and Rank Ordering of Criteria — Addressing Prescription within Weight Elicitation

Cited by 14 publications

References 48 publications

Policy analysis on the fly with an online multicriteria cardinal ranking tool

Policy analysis on the fly with an online multicriteria cardinal ranking tool

Evaluation of Flexible and Interactive Tradeoff Method Based on Numerical Simulation Experiments

Measuring Conflicts Using Cardinal Ranking: An Application to Decision Analytic Conflict Evaluations

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