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Problem structuring using computer-aided morphological analysis

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  • T Ritchey

    (Swedish Defence Research Agency)

Abstract

General morphological analysis (GMA) is a method for structuring and investigating the total set of relationships contained in multidimensional, usually non-quantifiable, problem complexes. Pioneered by Fritz Zwicky at the California Institute of Technology in the 1930s and 1940s, it relies on a constructed parameter space, linked by way of logical relationships, rather than on causal relationships and a hierarchal structure. During the past 10 years, GMA has been computerized and extended for structuring and analysing complex policy spaces, developing futures scenarios and modelling strategy alternatives. This article gives a historical and theoretical background to GMA as a problem structuring method, compares it with a number of other ‘soft-OR’ methods, and presents a recent application in structuring a complex policy issue. The issue involves the development of an extended producer responsibility (EPR) system in Sweden.

Suggested Citation

  • T Ritchey, 2006. "Problem structuring using computer-aided morphological analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(7), pages 792-801, July.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:7:d:10.1057_palgrave.jors.2602177
    DOI: 10.1057/palgrave.jors.2602177
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    References listed on IDEAS

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    1. Jonathan Rosenhead, 1996. "What's the Problem? An Introduction to Problem Structuring Methods," Interfaces, INFORMS, vol. 26(6), pages 117-131, December.
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    9. Pereverza, Kateryna & Pasichnyi, Oleksii & Lazarevic, David & Kordas, Olga, 2017. "Strategic planning for sustainable heating in cities: A morphological method for scenario development and selection," Applied Energy, Elsevier, vol. 186(P2), pages 115-125.
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    13. Duczynski, Guy, 2018. "Investigating traffic congestion: Targeting technological and social interdependencies through general morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 161-167.
    14. Durugbo, Christopher M., 2020. "Affordance-based problem structuring for workplace innovation," European Journal of Operational Research, Elsevier, vol. 284(2), pages 617-631.
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    16. C Ram & G Montibeller & A Morton, 2011. "Extending the use of scenario planning and MCDA for the evaluation of strategic options," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(5), pages 817-829, May.
    17. Markus Spiekermann, 2019. "Data Marketplaces: Trends and Monetisation of Data Goods," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 54(4), pages 208-216, July.
    18. Panula-Ontto, Juha, 2019. "The AXIOM approach for probabilistic and causal modeling with expert elicited inputs," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 292-308.
    19. Panula-Ontto, Juha & Luukkanen, Jyrki & Kaivo-oja, Jari & O'Mahony, Tadhg & Vehmas, Jarmo & Valkealahti, Seppo & Björkqvist, Tomas & Korpela, Timo & Järventausta, Pertti & Majanne, Yrjö & Kojo, Matti , 2018. "Cross-impact analysis of Finnish electricity system with increased renewables: Long-run energy policy challenges in balancing supply and consumption," Energy Policy, Elsevier, vol. 118(C), pages 504-513.
    20. Seeve, Teemu & Vilkkumaa, Eeva, 2022. "Identifying and visualizing a diverse set of plausible scenarios for strategic planning," European Journal of Operational Research, Elsevier, vol. 298(2), pages 596-610.
    21. Ram, Camelia, 2020. "Scenario presentation and scenario generation in multi-criteria assessments: An exploratory study," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
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    23. Johansen, Iver, 2018. "Scenario modelling with morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 116-125.
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