Morphological analysis (problem-solving)

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Morphological Analysis or General Morphological Analysis is a method developed by Fritz Zwicky (1967, 1969) for exploring all the possible solutions to a multi-dimensional, non-quantified complex problem.[1]

Overview[edit]

General Morphology was developed by Fritz Zwicky, the Bulgarian-born, Swiss-national astrophysicist based at the California Institute of Technology. Among others, Zwicky applied Morphological Analysis (MA) to astronomical studies and the development of jet and rocket propulsion systems. As a problem-structuring and problem-solving technique, MA was designed for multi-dimensional, non-quantifiable problems where causal modeling and simulation do not function well, or at all. Zwicky developed this approach to address seemingly non-reducible complexity: using the technique of cross-consistency assessment (CCA) (Ritchey, 1998), the system allows for reduction by identifying the possible solutions that actually exist, eliminating the illogical solution combinations in a grid box rather than reducing the number of variables involved. A detailed introduction to morphological modeling is given in Ritchey (2002, 2006).

Morphological Analysis of Real-world Problems[edit]

Consider a complex, real-world problem, like those of marketing or making policies for a nation, where there are many governing factors, and most of them cannot be expressed as numerical time series data, as one would like to have for building mathematical models.

The conventional approach here would be to break the system down into parts, isolate the vital parts (dropping the 'trivial' components) for their contributions to the output and solve the simplified system for creating desired models or scenarios. The disadvantage of this method is that real-world scenarios do not behave rationally: more often than not, a simplified model will break down when the contribution of the 'trivial' components becomes significant. Also, importantly, the behaviour of many components will be governed by the states of, and their relations with, other components – ones that may be seen to be minor before the analysis.

Morphological Analysis, on the other hand, does not drop any of the components from the system itself, but works backwards from the output towards the system internals.[2] Again, the interactions and relations get to play their parts in MA and their effects are accounted for in the analysis.

In many problem spaces characterised by complexity and interdependencies there are often dozens of interacting factors. These can, however, be reduced to the seven or fewer principal factors. Each of these factors can be extended into a range of conditions from the most favourable to the least favourable, allowing precise specification of the current conditions and a contrasting view of the desired conditions - or what we would wish for the system to exhibit if we were to take action and we were successful. This approach also provides for a variety of intermediate conditions and pathways that get us from 'here' to 'there'.

References[edit]

  1. ^ Ritchey, T. (1998). General Morphological Analysis: A general method for non-quantified modeling.
  2. ^ Modelling Complex Socio-Technical Systems Using Morphological Analysis (Ritchey 2003-06)[1]

Further reading[edit]

  • Ritchey, T. (1998). General Morphological Analysis: A general method for non-quantified modeling.
  • Ritchey, T. (2006). "Problem Structuring using Computer-Aided Morphological Analysis". Journal of the Operational Research Society (JORS), Vol. 57, No. 7.
  • Ritchey, T. (2011) Wicked Problems/Social Messes: Decision support Modelling with Morphological Analysis. Berlin: Springer.
  • Zwicky, F. (1969). Discovery, Invention, Research - Through the Morphological Approach. Toronto: The Macmillian Company.
  • Zwicky, F. & Wilson A. (eds.) (1967). New Methods of Thought and Procedure: Contributions to the Symposium on Methodologies. Berlin: Springer.
  • Jones, J.C. (1981). Design Methods. Wiley.
  • Ayres, R.U. (1969). Technological Forecasting and Long-Time Planning. McGraw-Hill.

See also[edit]