Utility function assessment on a micro-computer: An interactive procedure

Nitzsch, Rüdiger von; Weber, Martin

doi:10.1007/bf02283742

Cited by 35 publications

(15 citation statements)

References 17 publications

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“…This is less stressful on experts, since they are allowed to give incomplete preference statements (see von Nitzsch and Weber, 1988;Ríos Insua, 1994).…”

Section: Quantification Of Dm Preferencesmentioning

confidence: 99%

Monte Carlo simulation techniques for group decision making with incomplete information

Mateos¹,

Jiménez²,

Ríos-Insúa³

2006

European Journal of Operational Research

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“…This is less stressful on experts, since they are allowed to give incomplete preference statements (see von Nitzsch and Weber, 1988;Ríos Insua, 1994).…”

Section: Quantification Of Dm Preferencesmentioning

confidence: 99%

Monte Carlo simulation techniques for group decision making with incomplete information

Mateos¹,

Jiménez²,

Ríos-Insúa³

2006

European Journal of Operational Research

View full text Add to dashboard Cite

“…This is less stressful on experts, since they are allowed to provide incomplete preference statements, see von Nitzsch andWeber (1988) andRíos Insua (1994). Moreover, this makes the system suitable for group decision support, because individual conflicting views or judgements in a group of stakeholders can be captured through imprecise responses.…”

Section: Quantification Of Dms' Preferencesmentioning

confidence: 99%

Monte Carlo Simulation Techniques in a Decision Support System for Group Decision Making

2005

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This paper describes a group decision support system based on an additive multi-attribute utility model for identifying a consensus strategy in group decision-making problems where several decision-makers or groups of decision-makers elicit their own preferences separately. On the one hand, the system provides procedures to quantify the DM's or group of DMs' preferences separately. This involves assessing the DM's or group of DMs' component utilities that represent their preferences regarding the respective possible attribute values and objective weights that represent the relative importance of the criteria. On the other hand, we propose Monte Carlo simulation techniques for identifying a consensus strategy. An iterative process will be carried out, where, after the simulations have been performed, the imprecise component utilities and weights corresponding to the different DMs or groups of DMs are tightened to output more meaningful information in the next simulations to achieve a consensus strategy. Finally, an application to the evaluation of remedial strategies for restoring contaminated aquatic ecosystems illustrates the usefulness and flexibility of this decision support tool.

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“…Moreover, it admits incomplete information allowing the DM to provide a range of responses instead of a precise value, as the above methods demand for each probability question the DM is asked. This is less stressful on experts, since they are allowed to provide incomplete preference statements, see (von Nitzsch and Weber, 1988;R! ı ıos Insua, 1994).…”

Section: Incomplete Information In Preference Elicitationmentioning

confidence: 99%

Modelling individual and global comparisons for multi‐attribute preferences

Mateos

Jiménez

Ríos-Insúa

2003

Multi Criteria Decision Anal

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This paper describes a decision support system based on an additive multi-attribute utility model for identifying the optimal strategy in complex decision-making problems. The system allows for incomplete information on the component utility function and weight assessment, which leads to classes of utility functions and weight intervals, respectively. This makes the system suitable for group decision support, because individual conflicting views or judgements in a group of decision-makers can be captured through imprecise responses in the respective assessment methods. Moreover, the system admits uncertainty about the multi-attribute strategy consequences, which can be defined in term of ranges for each attribute instead of single values. The system computes non-dominance and potential optimality to identify the most preferred strategy. We also propose an approach based on Monte Carlo simulation techniques for group decision-making problems, which could be specially useful for e-democracy, where several decision-makers or groups of decision-makers elicit their own preferences separately, which are then compared to try to reach a consensus on their respective preferences.

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Utility function assessment on a micro-computer: An interactive procedure

Cited by 35 publications

References 17 publications

Monte Carlo simulation techniques for group decision making with incomplete information

Monte Carlo simulation techniques for group decision making with incomplete information

Monte Carlo Simulation Techniques in a Decision Support System for Group Decision Making

Modelling individual and global comparisons for multi‐attribute preferences

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