MA/CarmaTM (Computer-Aided Resource for Morphological Analysis) is the successor of MA/Casper, the world leading proprietary software system which supports an extended form of Morphological Analysis. MA/Carma serves as a development platform for morphological (what-if) inference models and for creating scenario and strategy laboratories. The MA Software series was first developed in 1995, it is in its 5th programming version.
PLEASE NOTE: The MA/Carma Development Platform is not a commercially available, "over-the-shelf" product. It is available through consultancy work, joint ventures and partnerships. For academics, it is available in a pro bono Research Support Program [Go to]
As a development platform for morphological modeling, MA/Carma supports the entire GMA process:
- Analysis-synthesis cycles
- Cross Consistency Assessments
- Relational database development
- Scenario generator features
- Single and multiple driver features
- Input-output (if-then) modeling features
- Notes and documentation
MA/Carma is implemented as a Windows XP/Vista/7/8/10 program written in C++. It is designed along normal and widely implemented Windows application forms and provides an easy-to-understand interface to the user. However, the successful application of computer-aided morphological analysis requires qualified facilitation by analysts experienced in the method. It is not suitable as a "do-it-yourself" software implementation.
NOTE: The example morphological fields provided below derive from a study done for the Swedish National Rescue Services Agency. The study involved developing a computer-aided instrument to assess Swedish Rescue Services' preparedness for chemical accidents and terrorist actions involving the release of chemical agents. (Ritchey T., Sternström, M. & Eriksson, H. (2002). Using Morphological Analysis to Evaluate Preparedness for Accidents Involving Hazardous Materials).
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The model consists of two linked morphological fields: A 5-parameter Preparedness Resource field
(the first five columns on the left) and a 3-parameter Scenario Response field
, which is based on an accident scenario representing a general class of chemical substances. (The scenario is based on an actual accident in Sweden involving the release of ammonia caused by a railroad accident.) Although this is a relatively simple model, for that very reason it suffices to illustrate how morphological inference models function.
The main user interface of MA/Carma is divided into three working areas:
- E - Edit Field
- C - Cross-Consistency Matrix
- D - Display Field
The EDIT Field
Figure 1: Segment of a morphological model for assessing Rescue Services' preparedness for accidents involving chemical releases.
The Edit Field (E) is used to enter the variables and variable-values for the problem complex to be studied. It allows the morphological matrix to be formatted -- e.g. re-sized and color coded if desired. Comments and documentation can be entered in text areas associated with each text cell (the red dot indicates that there is text in the cell's text area).
The Cross-Consistency Matrix
Figure 2: Cross-consistency matrix for the field in Figure 1. "X" marks inconsistent value-pairs.
The Cross-Consistency matrix relates the conditions (values) of each parameter with those of all other parameters -- pare-wise. This allows for Cross Consistency Assessments
(CCA) to be made within the parameter space. Comments and motivation for the CCA judgments are documented in text areas behind each cross-consistency cell. Consistency keys
can be defined for different purposes. When consistency checks are run, internally inconsistent configurations are deleted from the solution space, and other configurations can be flagged for different qualities.
The Display Field
Figure 3: Display field showing output (blue) for given input conditions (red).
The display field allows the solution space (or outcome space) of the morphological (scenario, strategy or policy) model to be examined. In this case (Figure 3
, above), the field displays a given input
(in red) concerning a rescue service's preparedness resources for a particular chemical accident, and output
(in blue) showing the level of response associated with this preparedness level.
In Figure 4
, the original configuration from Figure 3
is "frozen" and new parameters values are chosen (the three light blue cells on the Resource field) in order to see how preparedness response levels can be most efficiently increased (the three light blue cells on the Response field). Note that both Planning, Training and Equipment must be augmented in order to gain the best response to the chemical release itself. This does not, however, improve Information and Human Rescue responses.
In Figure 5
, the input
has been shifted to the response segment, in order to ascertain what resources would be required for a (given) desired response. It is the characteristic of morphological models, that anything can be an input, and anything an output.
The morphological model presented above represents only one of many possible applications. During the past 25 years, general morphology has been used for:
- Engineering design, architecture and general design theory
- Technological forecasting, scenario development and futures studies in general
- Policy analysis, management science and organisational development
- Creativity, innovation and knowledge management
Further reading [Download in PDF]:
The Author: Dr. Tom Ritchey is a former Research Director for the Institution for Technology Foresight and Assessment at the Swedish National Defence Research Agency (FOI) in Stockholm. He is a modelling theorist and methodologist who works primarily with non-quantified decision support modelling -- especially with General Morphological Analysis (GMA), Bayesian Networks (BN) and Multi-Criteria Decision support. Since 1995 he has directed more than 100 projects involving computer aided GMA for Swedish government agencies, national and international NGO:s and private companies. He is the founder of the Swedish Morphological Society and Director of Morphologics (formerly Ritchey Consulting).