Systems Thinking - Approaches

Transcription

1 Systems Thinking - Approaches 1 Introduction to two approaches Hard systems approach Soft systems approach Soft and hard systems thinking : two different pairs of spectacles References and Acknolwedgements Introduction to two approaches Over the years, a number of approaches have been developed to use systems thinking and improve our capability to manage and improve systems. Here, we describe two generalized approaches: hard systems and soft systems approaches. In general, we can say that the soft systems approach is a learning process designed to determine what needs to be done in an ill-defined problem situation, and hard systems approaches are used to determine how to make improvements to a better-defined problem. 2 Hard systems approach The hard systems approach was developed to maximise the efficiency of a system in terms of amount of output per given input. The purpose of the system is not in dispute, and the measure of efficiency is usually not questioned. It is an approach with begins with the definition of the system and the measure of efficiency, and then proceeds by looking for technical solutions to optimise the system. When a new system is designed, the outputs and measures of efficiency are compared with the old one. The hard systems approach is often one of quantification (often quantitative models are developed as part of the system description). Examples of the hard systems approach are to be found in the work of the International Agricultural Research Centres such as IRRI and ICRISAT in the 1970s and 1980s. In the ICRISAT microwatershed programme, a system of field-level water catchments and cultivation methods such as tied ridging were designed to improve water use and production in semi-arid areas. Measures of efficiency were amount of crop produced per rainfall amount and per area. However, the suggested improved system was rarely adopted, as farmers found that the benefits did not outweigh the difficulties and increased labour requirements of the cultivation methods recommended. Under the IRRI Cropping Systems Programme, a number of more intensive cropping patterns were designed and tested according to the agro-ecological conditions particularly the length of the growing season which determined if it was technically feasible to grow a second crop of flooded rice or upland crop (such as maize, or ICRA Learning Materials Defining the System Systems Thinking Approaches - 1/5

2 soybean) after the first crop of rice in the same season. The measures of efficiency used to compare the old traditional system with the new improved cropping system were amount of rice produced or the income gained from the crops. These programmes - like many of the changes promoted during the green revolution met with mixed success: many of the new cropping patterns in countries such as the Philippines and Indonesia were adopted by farmers (often with heavy incentives) and did indeed dramatically improve production of staple food crops such as rice, but detractors point to the difficulties faced by small farmers in buying inputs, the resulting disadvantaged position of these farmers, the loss of biodiversity implicated by widespread adoption of a few varieties of the major crops, as well as other negative effects. The intensive debate about the merits and drawbacks of these new crop technologies - green revolution - which raged during the 1970s and 1980s, show that different people have quite different ideas about what constituted success or improvement in agriculture and rural livelihoods. Adapted from Wilson and Morren, Soft systems approach Recognising the difficulty associated with learning about and improving complex real world situations, Checkland and his colleagues at the University of Lancaster in the UK developed what they termed soft systems methodology or SSM (Checkland, 1981; Checkland and Scholes, 1990). Adaptations of this methodology have increasingly been advocated and used in environmental management and rural development projects. One of the main sources of learning using SSM occurs when we compare the the current situation - the existing what - and the future vision - the ideal what. In SSM the existing what is visualized in a situation summary (or rich picture, because it includes diverse elements and viewpoints) and the future vision in a conceptual model. It is only after making these comparisons that discussion of how things could be improved occurs. Learning occurs with each cycle of the SSM activities. One of the important features of SSM is its focus on repeated cycles of learning to arrive at new and better appreciations of complex situations. ICRA Learning Materials Defining the System Systems Thinking Approaches - 2/5

3 The ways in which SSM has been applied continue to evolve. However a generalized approach consists of something like the following steps or phases: 1. Explore the problem in an unstructured way using spray diagramming to gather wide range of ideas from different people with an interest in the situation. 2. Visualize the existing problem situation using diagrams to summarize the situation (situation summaries or rich pictures ), to help understand the context and relationships surrounding the problem situation. 3. Identify relevant systems and develop descriptions of these, including information on: The transformation process The beneficiaries, or important stakeholders The owners, or influential stakeholders The perspectives that shape the system The environment within which the system operates 4. Develop conceptual models of systems expressing ideal or improved scenarios, including inputs and outputs, components, boundaries and feedback linkages. 5. Compare the conceptual models of future ideal scenarios with the existing problem situation as expressed in the existing situation summaries. 6. Explore feasible and desirable change through a process of debate and negotiation, comparing existing and future scenarios. 7. Take action to solve the problem or to realize the future desired scenario. adapted from Checkland and Scholes, 1990 ICRA Learning Materials Defining the System Systems Thinking Approaches - 3/5

4 4 Soft and hard systems thinking : two different pairs of spectacles We can summarise the above by saying that hard systems thinkers take the world as being systemic. They consider systems to exist and to have a clear purpose and well-defined boundaries. Negotiations on the purpose of the system or its boundaries are not searched for. Hard systems analysis is useful in the case of mechanical or relatively simple administrative or biophysical problems and is thus concerned with settings in which clear-cut goals can be set, performance maintained and implementation achieved. Hard systems thinkers experience biophysical but also social phenomena as constant, regular, reoccurring and predictable. Soft systems thinkers argue that problems will occur when hard systems thinking is applied to problem situations in which human perceptions, behaviour or action seem to be dominating factors and where goals, objectives and even the interpretation of events are all problematic. A soft systems thinker experiences phenomena, including the social ones, as dynamic, chaotic, changing and unpredictable. Soft systems thinkers do not take the world to be systemic but think it is sometimes useful to deal with it as if it were systemic. They consider soft systems to be deliberate or negotiable social constructs, in that soft systems exist only to the extent that people agree on their goals, their boundaries, their membership, and their usefulness. These differences between the two types of thinking are summarized in Table 1 (following page) : ICRA Learning Materials Defining the System Systems Thinking Approaches - 4/5

5 Table 1 Hard and Soft Systems Thinking Compared Hard system thinkers Soft system thinkers Philosophical approach Postivist Constructivist Ontological position (about the form and nature of reality) Epistemological position (about the relationship between the researcher and the researched) How are phenomena experienced? Research design Purpose Reality exists Systems do exist and do have a clear purpose and well-defined boundaries Observations are not coloured by subjective aspects of the scientist or his/her instruments Biophysical and social phenomena are experienced as constant, regular, reoccurring and predictable Strong focus on the testing of hypothesis Focus on the use of quantitative methods Focus on improving current problem Objective knowledge Generalisations Maximising efficiency Multiple perceptions of reality Systems do exist only to the extent that people agree on the goals, the boundaries and their components Neutral observations are impossible Biophysical and social phenomena are experienced as dynamic, chaotic, changing and unpredictable Less focus on the use of hypothesis Focus on the use of qualitative methods Focussing on how to realise a desired future situation Socially constructed knowledge to increase our understanding for more effective action Particularities or generalisations for one particular context Innovations 5 References and Acknolwedgements Checkland, P. and J. Scholes Soft systems methodolodgy in action. Wiley and Sons, Chichester, UK. Wilson, K. and G.E.B. Morren Jr Systems approaches for improvement in agriculture and resource management. Macmillan Publishing Company, New York. This learning resource was prepared by Richard Hawkins, using material from Annemarie Groot. ICRA Learning Materials Defining the System Systems Thinking Approaches - 5/5