Evaluación | Biopsicología | Comparativo | Cognitivo | Del desarrollo | Idioma | Diferencias individuales | Personalidad | Filosofía | Social | Métodos | Estadística | Clínico | Educativo | Industrial | Artículos profesionales | Psicología mundial | Estadística: Scientific method · Research methods · Experimental design · Undergraduate statistics courses · Statistical tests · Game theory · Decision theory Morphological analysis is a technique developed by Fritz Zwicky (1966, 1969) for exploring all the possible solutions to a multi-dimensional, non-quantified problem complex. In linguistics it refers to identification of a word stem from a full word form (see morpheme). As a problem-structuring and problem-solving technique, morphological analysis 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 however does allow for reduction, not by reducing the number of variables involved, but by reducing the number of possible solutions through the elimination of the illogical solution combinations in a grid box. A detailed introduction to morphological modeling is given in Ritchey (2002). Contenido 1 Visión general 1.1 Swemorph 1.2 Requirements 1.3 Process-data model of morphological analysis 2 Morphological Analysis Activities 2.1 Activity 1: Describe the problem 2.2 Activity 2: Analyze the possible solution’s parameters 2.3 Activity 3: Construct morphological box 2.4 Activity 4: Evaluate possible solution 2.5 Activity 5: Apply the selected solution 3 Morphological Analysis Concepts 3.1 Problem 3.2 Emitir 3.3 Dimension 3.4 Policy problem 3.5 Morphological box 3.6 Value 3.7 Parameter 3.8 Input constraint 3.9 Solution space 3.10 Real action 4 Morphological Analysis Application 4.1 Sample of a policy problem 4.2 Sample of a complete morphological box 5 Related topics 6 Referencias 7 Ver también 8 External links Overview This article or section appears to contain a large number of buzzwords. Because: Section contains seeming tautologies and vague abstractions. Please help rewrite this article to make it more concrete and meaningful. Morphological analysis (MAMÁ) is a method for exploring all possible solutions in a complex problem space. The method was developed by Fritz Zwicky, the Swiss astrophysicist based at the California Institute of Technology. Zwicky applied MA inter alia to astronomical studies and the development of jet and rocket propulsion systems. Morphology comes from the classical Greek word morphe, meaning shape or form. MA concerns the arrangement of objects and how they conform to create a whole of Gestalt. The objects in question can be a physical system (p. ej.. anatomy), a social system (p. ej.. an organisation) or a logical system (p. ej.. a language or system of ideas). A complex problem has several characteristics: Multi-dimensionality: A multi-dimensional problem has many interelated aspects. Por ejemplo, the problem might have to deal with financial, political and social dimensions, as a whole. Uncertainty: Aspects of complex problems are often non-quantifiable and are continuously evolving, making causal methods or simulation unsuitable. Subjectivity: There is no right or wrong solution to the problem, only better or worse solutions. Swemorph The Swedish Morphological Society is a non-profit scientific organization, whose purpose is the development and dissemination of knowledge concerning the scientific use of morphological analysis, its theory and practice. The site contains articles and links on morphological analysis and on its pioneer Fritz Zwicky. For other examples, discussions, a tutorial and the history of morphological analysis, please refer to www.swemorph.com. Requirements As with any idea generation method, the quality of the output depends on the input. Idealmente, a morphological analysis that has more than four values should be performed using MA software, such as MA/Casper Advanced Computer Support for Morphological Analysis. Teams should not have more than eight members but should include people with knowledge of the problem. Time is dependent on complexity - research conducted by Swemorph suggests from 1 – 30 full workshop days. Process-data model of morphological analysis This section presents the process-data model explaining the generic concepts and the activities involved in morphological analysis methods, as shown by figure 1 below. Figure1: Morphplogical Analysis process-data model The left figure shows the meta-process model, which is the representation of the activities involved in performing morphological analysis. These activities are explained in more depth in section 2. On the right hand of the figure you see the meta-data model of morphological analysis, which is the presentation of the data that are produced by the activities of the method. Each of these data or concepts is detailed in section 3. The dashed lines in between the two models indicate the relationship between the activities and the produced data. An example of Morphological Analysis project to clarify the application of Morphological Analysis’ activities is then presented in section 4. The aim of this example is to perform analysis and explore the possibilities to grow a start up company. Morphological Analysis Activities There are five main activities that need to be performed in morphological analysis as depicted in the grey rounded-rectangles at the left-hand side of the figure 1. Some activities might contain sub-activities as shown by the white rounded rectangles. These activities and their sub-activities could be sequential or iterative - and could be adapted at any point in case of new ideas or a change in circumstances. Activity 1: Describe the problem In describing the problem, you identify all issues (Sub-activity 1-1a) that might relate to/ be caused by/ cause the problem. In line with that, you need to define the dimensions (Sub-activity 1-1b) that affect the problem. Respectivamente, you gather the result of these activities into a policy problem [Sub-activity 1-2]. You might use any other idea generation method, such as brainstorming, voting, etc, in performing this step. See policy problem sample for more concrete description. Activity 2: Analyze the possible solution’s parameters Once you have a policy problem in hand, you decompose it into some problem’s values (Sub-activity 2-1). Each dimension that you have defined in the policy problem might consist of one or more values. Then you decompose the values into more specific concepts, called parameters (Sub-activity 2-2). Again, you might use any other idea generation method, such as brainstorming, voting, etc, in performing these (sub) activities. See policy problem sample for more concrete description. Activity 3: Construct morphological box This activity, constructing morphological box, forces you to document/ organize what you have thought/ discussed so far. You create a two dimensional matrix, transform the pre-defined values as the column header and list the corresponding parameters under each values. See morphological box sample for more concrete description. Activity 4: Evaluate possible solution The first sub-activity you need to perform in evaluating possible solution is cross-consistency assessment (Sub-activity 4-1) to all parameters against each other. If you find two parameters are contradictory, you put a cross (x) mark into the morphological field. Secondly you determine one or more input constraint (Sub-activity 4-2) that is parameter(s) that must be included in the future solution or solution space. (Sub-activity 4-3) The last activities is to combine the pre-constructed morphological box (Activity 3), the cross consistency assessment result (Sub-activity 4-1), and the predefined input constraint (Sub-activity 4-2). You highlight the morphological field on the morphological box that fulfills the input constraint with a certain color, p. ej.. rojo. Then you map the cross consistency assessment result of the input constraint to the parameters on the morphological box and finally you highlight the blank morphological field with another color, p. ej.. blue. The parameters highlighted in red and blue are compose the solution space. See solution space sample for more concrete description. Activity 5: Apply the selected solution Based on the solution space that is resulted from performing activity 4, you can decide whether to put the suggested solution into a real action or adapt one or more previous activity. Morphological Analysis Concepts The following section explains the main concepts related to Morphological Analysis. On the right hand side of figure 1, you can see the relationship of these concepts. By the means of the dashed arrows, you can see what activity produces the corresponding concepts. Problem A problem is 'what is' is not equal to 'what is desired' (Rubinstein 1975). Issue An issue is a relevant aspect that might cause a problem to occur (Ritchey 2003). Dimension A dimension is the corresponding properties that belong to an ISSUE, such as a technical, financiero, political and/ or ethical issue (Ritchey 2003). Policy problem Policy problem is a collection or document or list or thought that consists of a collection of problem (Ritchey 2003). [explanation] The figure 2 below serves as a preliminary introduction of the concepts that will be described in section 3.5 – 3.9. Figure 2: Morphological analysis concepts introduction. Morphological box A morphological box is a n-dimensional box that consists of a collection of morphological field, which is constructed to facilitate the development of ideas/ solutions (Ritchey 2003, Erikson & RItechey 2002, Bridgewater 1968). A morphological field is a cell, es decir. an intersection of a column and a row that contains a parameter of a value, in morphological box (Ritchey 2003). Value A value is a representation of the possible, relevant condition that each issue can assume, which will become the column header of the morphological box (Ritchey 2003). Parameter Parameter is the detail aspects of values, which will become the rows under a specific value (Ritchey 2003). Input constraint Input constraint is special/ selected parameter that must be included in the solution space and serve as the view point in determining the solutions. Solution space Solution space is the result derived by performing cross-consistency assessment, which is a process to reduce the total set of formally possible configuration in a morphological field to smaller set of internally consistence configurations (Ritchey 2003). Real action Real action is a set of detailed action derived from the solution space. Morphological Analysis Application The knowledge in this example is derived from the Informatics Business course at Utrecht University. The aim of this example is to explore all aspects involved in being a successful entrepreneur. Sample of a policy problem Issue: Everyone can be an entrepreneur, however only one out of twenty persons who started their own business is a survivor. Related questions: What lesson could we adopt from this survivor? What personal quality can determine the success or failure in attempt to become a better entrepreneur? Which factors affect a success of a product/ service? How do we develop excel competitive strategies for start up company? etc. Dimension: Being an entrepreneur involving the following dimensions: Financial Social Psychological Technological Sample of a complete morphological box The purpose of this Morphological Box is to decompose the dimensions of policy problem as detailed as possible into their related aspects in an organized way. It is important that we never (intentionally) exclude any aspect in listing the decomposition. For example we could detail ‘financial’ dimension which we derived in the previous example, into ‘financial sources’. We call this decomposition as a value. Other examples of values are ‘relation with existing business’ and ‘educational level’, which are result of decomposing the ‘social’ dimension. We list this value into the column header as depicted in figure 3. Further decomposition of a value will result in parameters definition, which we list in the columns under the related value as depicted in figure 3. Por ejemplo, the value ‘sector’ has parameters such as ‘automation service’, ‘automotive industry’, ‘business consultancy’, ‘electric apparatus’, etc. This gives you a complete list of available sectors and you can start a new business in any of these sectors. The sector’s parameters are derived from registered industry KVK website, which is the official chamber of commerce of the Netherlands. Another example of value is ‘personal characteristics’. It has parameters, such as ‘extroversion’, ‘introversion’, ‘aggressive’, ‘open to new ideas’, etc. This gives you a complete list of possible characters that every human has. Since personality is closely related to profession, this personality list could advice you about what personal character is lacking in yourself in order to be an entrepreneur. The personal characteristics parameters are derived from another wikipedia entry, Personalidad. The resulted two-dimensional matrix is derived from identifying the values of dimensions and parameters of values, is shown in the figure 3 below. The column header lists the values and the columns under each header represent the decomposition of a value (es decir. the parameters). Figure 3: The morphological box – Entrepreneur to be Sample of a complete Cross Consistency Assessment (CCA)t result The purpose the cross consistency assessment is to check a parameter against other parameters. The contradictive judgment of two parameters are shown with the cross (x) mark in a morphological field in the figure 4 below. The figure is an excerpt of a CCA result. Figure 4: Excerpt of Cross Consistency Assessment result - Entrepreneur to be In the following paragraph, the two cells in the blue circle are explained further as the example of CCA. We checked whether the parameter ‘automation service’ contradicts to parameter ‘personal saving’. We do not find any contradiction between these two parameters, so we leaved the box empty. Our motivation was, the start up budget required to establish a new business in the sector of automation service, p. ej.. software house, is affordable by only using personal saving. In the other hand, we found a contradiction between the parameter ‘automotive industry’ and parameter ‘personal saving’, since the capital required to establish an automotive industry is (most of the time) too much for a personal saving. Therefore we put the contradiction/ cross (x) mark into the corresponding cell. Example of an Input Constraint definition and the corresponding Solution Space Input Constraint: Competitor (relation with existing business) of existing IT companies (sectors). These input constraints are shown with the red highlight in the figure 4 and figure 5. These input constraints are chosen based on our needs. In this example we wanted to establish a software house, however we do not want to be part of a larger company (subsidiary or joint venture), therefore we decided to be competitor of the existing business. The solution space that fulfill the input constraints are shown by the blue boxes in figure 5, which are derived from combining the non-contradict parameters (empty cells) of our input constraints (highlighted in red color) in the figure 4. In other word, a parameter could be in a solution space is and only if it does not contradict with the input constraint. This also means that we could derive more than one solution by varying the input constraint. Figure 5: The Input constraint and solution space – Entrepreneur to be The lessons learned from the solution space shown in figure 5 son, in order to be an ‘independent’ (no join venture nor become sub diary) entrepreneur in IT industry: It is possible to obtain the start up budget from any combination of the financial resources available. If you require lower startup budget, then you can obtain it from you own personal saving. To have enough knowledge you need to at least finish the MBO level of education and no special diploma needs to be obtained. The IT industry requires high-innovation ideas. Related topics Since ideally the morphological analysis requires a lot of recourses, the implementation of this method might be specific to the development of strategy that might excel the competitive advantage of the business, como: Corporate Strategy Market Research Organizational Planning References Ritchey, T. (1998). General Morphological Analysis: A general method for non-quantified modeling. Ritchey, T. (2002). Modeling Complex Socio-Technical Systems using Morphological Analysis Adapted from an address to the Swedish Parliamentary IT Commission, Estocolmo. Zwicky, F. (1969). Descubrimiento, Invention, Investigar - Through the Morphological Approach (1969) Toronto: The Macmillian Company. Zwicky, F. & Wilson A. (Eds.) (1967). New Methods of Thought and Procedure: Contributions to the Symposium on Methodologies. Berlín: Salmer. Available at www.swemorph.com/ma.html [www.kvk.nl] Personality See also Morphological box Fritz Zwicky TRIZ External links Swedish Morphological Society MA/Casper Advanced Computer Support for Morphological Analysis. How to choose axes for analysis (rus.) Nl:Morfologisch overzicht ru:Морфологический анализ This page uses Creative Commons Licensed content from Wikipedia (ver autores).
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