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Multiple criteria decision making: Facilitating a learning environment

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  • Linda Holz
  • George Kuczera
  • Jetse Kalma

Abstract

Effective decision making for sustainability requires consideration of multiple evaluation criteria. A numerical weight, assigned to each criterion, is the most common tool used to formalize preferences in Multiple Criteria Decision Making (MCDM). However, there are methods other than applying weights, which can be used to explore and articulate preferences. Two such groups have been identified as aspirational and holistic methods. The authors are interested in establishing if the different approaches to MCDM vary in their ability to facilitate a learning environment. There has been little examination of how this might best be achieved. An attempt is made to set out some hypotheses about which characteristics of MCDM tools may best support such learning. Additionally, three MCDM tools, representing the weighting, aspirational and holistic methods, are evaluated through a workshop for their ability to support individual learning. This includes a new tool, referred to as Target Ordering, which explores preferences through criteria targets rather than applying a weight to the criteria themselves.

Suggested Citation

  • Linda Holz & George Kuczera & Jetse Kalma, 2006. "Multiple criteria decision making: Facilitating a learning environment," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 49(3), pages 455-470.
  • Handle: RePEc:taf:jenpmg:v:49:y:2006:i:3:p:455-470
    DOI: 10.1080/09640560600601744
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    References listed on IDEAS

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    1. Stefan Hajkowicz & Mike Young & Darla Hatton MacDonald, 2000. "Supporting Decisions: Understanding natural resource management assessment techniques," Natural Resource Management Economics 00_003, Policy and Economic Research Unit, CSIRO Land and Water, Adelaide, Australia.
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    Cited by:

    1. Fabio Blanco-Mesa & Anna M. Gil-Lafuente & José M. Merigó, 2018. "Subjective stakeholder dynamics relationships treatment: a methodological approach using fuzzy decision-making," Computational and Mathematical Organization Theory, Springer, vol. 24(4), pages 441-472, December.

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