Fritz Zwicky pioneered the development of morphological analysis (MA) as a method for investigating the totality of relationships contained in multi-dimensional, usually non- quantifiable problem complexes. During the past two decades, MA has been extended and applied in the area of futures studies and for structuring and analysing complex policy spaces. This article outlines the fundamentals of the morphological approach and describes recent applications in policy analysis. "... within the final and true world image everything is related to everything, and nothing can be discarded a priori as being unimportant." (Fritz Zwicky: Discovery, Invention, Research through the Morphological Approach.) Note: The original article contained diagrams and pictures of morphological fields, which are not available in this text format. The original article can be downloaded from the Swedish Morphological Society at: www.swemorph.com/ma.html. INTRODUCTION General Morphological analysis (MA) was developed by Fritz Zwicky - the Swiss astrophysicist and aerospace scientist based at the California Institute of Technology (CalTech) - as a method for structuring and investigating the total set of relationships contained in multi-dimensional, non-quantifiable, problem complexes (Zwicky 1966, 1969). Zwicky applied this method to such diverse fields as the classification of astrophysical objects, the development of jet and rocket propulsion systems, and the legal aspects of space travel and colonization. He founded the Society for Morphological Research and advanced the "morphological approach" for some 40 years, between the early 1930's until his death in 1974. More recently, morphological analysis has been extended and applied by a number of researchers in the U.S.A and Europe in the field of policy analysis and futures studies (Rhyne 1981, 1995a, 1995b; Coyle 1994, 1995, 1996; Ritchey 1997, 1998, Ritchey, Stenström & Eriksson, 2002). The method is presently experiencing somewhat of a renaissance, not the least because of the development of small, fast computers and flexible graphic interfaces. This article will begin with a discussion of some of the methodological problems confronting complex, non-quantified modelling, especially as applied to policy analysis and futures studies. This is followed by a presentation of the fundamentals of the morphological approach along with a recent application to policy analysis. METHODOLOGICAL BACKGROUND Analysing complex policy fields and developing futures scenarios presents us with a number of difficult methodological problems. Firstly, many, if not all of the factors involved are non- quantifiable, since they contain strong social-political dimensions and conscious self-reference among actors. This means that traditional quantitative methods, causal modelling and simulation are relatively useless. Secondly, the uncertainties inherent in such problem complexes are in principle non-reducible, and often cannot be fully described or delineated. This represents even a greater blow to the idea of causal modelling and simulation. Finally, the actual process by which conclusions are drawn in such studies is often difficult to trace - i.e. we seldom have an adequate "audit trail" describing the process of getting from initial problem formulation to specific solutions or conclusions. Without some form of traceability we have little possibility of scientific control over results, let alone reproducibility. An alternative to formal (mathematical) methods and causal modelling is a form of non- quantified modelling relying on judgmental processes and internal consistency, rather than causality. Causal modelling, when applicable, can - and should - be used as an aid to judgement. However, at a certain level of complexity (e.g. at the social, political and cognitive level), judgement must often be used -- and worked with -- more or less directly. The question is: How can judgmental processes be put on a sound methodological basis? Historically, scientific knowledge develops through cycles of analysis and synthesis: every synthesis is built upon the results of a proceeding analysis, and every analysis requires a subsequent synthesis in order to verify and correct its results (Ritchey, 1991). However, analysis and synthesis - as basic scientific methods - say nothing about a problem having to be quantifiable. Complex social-political problem fields can be analysed into any number of non-quantified variables and ranges of conditions. Similarly, sets of non-quantified conditions can be synthesised into well-defined relationships or configurations, which represent "solution spaces". In this context, there is no fundamental difference between quantified and non- quantified modelling. Morphological analysis - extended by the technique of cross consistency assessment (CCA, see below) - is a method for rigorously structuring and investigating the internal properties of inherently non-quantifiable problem complexes, which contain any number of disparate parameters. It encourages the investigation of boundary conditions and it virtually compels practitioners to examine numbers of contrasting configurations and policy solutions. Finally, although judgmental processes may never be fully traceable in the way, for example, a mathematician formally derives a proof, MA does go a long way in providing as good an audit trail as one can hope for. THE MORPHOLOGICAL APPROACH The term morphology comes from antique Greek (morphe) and means shape or form. The general definition of morphology is "the study of form or pattern", i.e. the shape and arrangement of parts of an object, and how these "conform" to create a whole or Gestalt. The "objects" in question can be physical objects (e.g. an organism, an anatomy, a geography or an ecology) or mental objects (e.g. linguistic forms, concepts or systems of ideas). Fritz Zwicky proposed a generalised form of morphological research: "Attention has been called to the fact that the term morphology has long been used in many fields of science to designate research on structural interrelations - for instance in anatomy, geology, botany and biology. ... I have proposed to generalize and systematize the concept of morphological research and include not only the study of the shapes of geometrical, geological, biological, and generally material structures, but also to study the more abstract structural interrelations among phenomena, concepts, and ideas, whatever their character might be." (Zwicky, 1966, p. 34) Essentially, general morphological analysis is a method for identifying and investigating the total set of possible relationships or "configurations" contained in a given problem complex. In this sense, it is closely related to typology construction (Bailey 1994), although it is more generalised in form and conceptual range. The approach begins by identifying and defining the parameters (or dimensions) of the problem complex to be investigated, and assigning each parameter a range of relevant "values" or conditions. A morphological box - also fittingly known as a "Zwicky box" - is constructed by setting the parameters against each other in an n-dimensional matrix (see Figure 1, below). Each cell of the n-dimensional box contains one particular "value" or condition from each of the parameters, and thus marks out a particular state or configuration of the problem complex. Ideally, one would examine all of the configurations in the field, in order to establish which of them are possible, viable, practical, interesting, etc., and which are not. In doing so, we mark out in the field a relevant "solution space". The solution space of a Zwickian morphological field consists of the subset of configurations, which satisfy some criteria - one of which is internal consistency. However, a typical morphological field of 6-10 variables can contain between 50,000 and 5,000,000 formal configurations, far too many to inspect by hand. Thus, the next step in the analysis-synthesis process is to examine the internal relationships between the field parameters and reduce the field by identifying, and weeding out, all mutually contradictory conditions. This is achieved by a process of cross-consistency assessment (CCA). All of the parameter values in the morphological field are compared with one another, pair-wise, in the manner of a cross-impact matrix. As each pair of conditions is examined, a judgment is made as to whether - or to what extent - the pair can coexist, i.e. represent a consistent relationship. To the extent that a particular pair of conditions is a blatant contradiction, then all those configurations containing this pair of conditions would also be internally inconsistent. Using this technique, a typical morphological field can be reduced by up to 90 or even 99%, depending on the problem structure. There are three types of inconsistencies involved here: purely logical contradictions (i.e. those based on the nature of the concepts involved); empirical constraints (i.e. relationships judged be highly improbable or implausible on empirical grounds), and normative constraints (e.g. relationships ruled out on e.g. ethical or political grounds). Normative constraints must be used with great care, and clearly designated as such. We must first discover what we judge as possible, before we make judgements about what is desirable. The reduction of the field to a solution space allows us to concentrate on a manageable number of internally consistent configurations. These can then be examined as elements of scenarios or specific solutions in a complex policy space. With computer support, the morphological field can be treated as an inference model. (For this purpose, FOA has developed a Windows-based software package which supports the entire analysis-synthesis process which General Morphology entails. The program is called MA/Casper: Computer Aided Scenario and Problem Evaluation Routine.) The morphological approach has several advantages over less structured approaches. Zwicky calls MA "totality research" which, in an "unbiased way attempts to derive all the solutions of any given problem". It may help us to discover new relationships or configurations, which may not be so evident, or which we might have overlooked by other - less structured - methods. Importantly, it encourages the identification and investigation of boundary conditions, i.e. the limits and extremes of different contexts and factors. It also has definite advantages for scientific communication and - notably - for group work. As a process, the method demands that parameters, conditions and the issues underlying these be clearly defined. 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