ESTRUCTURA Y METODOLOGÍA DE TRA BAJO DE LA PA

There is ample evidence that complex systems require different approaches to the commonly favoured reductionist analytical approach (Capra, 1996; Dörner, 1997; Kim, 1999). Sterman (2000, p. 5) asserts that successful ways to learn about complex systems require:

(1) “tools to elicit and represent the mental models we hold about the nature of difficult problems;

(2) formal models and simulation methods to test and improve our mental models, design new policies, and practice new skills; and

(3) methods to sharpen scientific reasoning skills, improve group processes, and overcome defensive routines for individuals and teams.”

The System Dynamics computer modelling and simulation fraternity have developed methods to promote better understanding of complex systems and operate more

effectively41 (Kim, 1999; Maani & Cavana, 2009; Meadows, 2008; Senge, 2006). These

methods include systems analysis, causal loop diagrams, stock and flow diagrams, flow charts, and simulation models/system dynamics. They can be categorised broadly under the headings ‘systems thinking’ and ‘system dynamics’. A systems thinking approach is qualitative and is used in this dissertation to better understand the interlinking of the indicators used to measure well-being. What follows is first a description of systems thinking and justification for its use. Then, the method of system dynamics and the value it provides are discussed. As systems thinking and system dynamics approaches are complementary (Maani & Cavana, 2007; Wolstenholme, 2004) they can be applied separately or together.

4.2.1 Systems thinking

Systems thinking emphasises relationships in the structure of a complex system as these determine system behaviour. The more visible and better understood the relationships, are the greater the insight into how things work in the real world. The typical human response to the difficulty of working with a complex system is to draw tight boundaries around an individual part and specialise. For instance, in academia there has been a proliferation of disciplines and multiple journals are published that cater for very narrow discourses that use highly specialised language. This proliferates the Anglo-Saxon reductionist science and multiple disciplinary perspectives (Noll, 2002). However, taking apart and analysing in detail does not provide the information that shows the patterns of organisation that allow the collective to function (Buchanan, 2002) and systems cannot be understood by more detailed information about the parts as stated by Capra (1996, pp. 29-30):

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Systems methods are used in many different areas. In planning, for example, the Soft Systems Method developed by Peter Checkland (1993) is used. Other areas include ecology, computing, operations research, social sciences, psychiatry, and medicine. This list is far from exhaustive.

The great shock of twentieth-century science has been that systems cannot be understood by analysis. The properties of the parts are not intrinsic properties, but can be understood only within the context of the larger whole. Thus the relationship between the parts and the whole has been reversed. In the systems approach, the properties of the parts can be understood only from the organization of the whole. Accordingly, systems thinking does not concentrate on basic building blocks but rather on basic principles of organization. Systems thinking is ‘contextual’, which is the opposite of analytical thinking. Analysis means taking something apart in order to understand it; systems thinking means putting it into the context of a larger whole.

Building on this view, it can be argued that the complexity of modern day problems requires expertise in both analysis and synthesis, as well as the development of appropriate tools to achieve this. Both synthesis and analysis are used in systems thinking (Hutchins, 1996).

The definitions in Table 4-3 provide an overview of what systems thinking sets out to achieve.

Table 4-3: Systems thinking definitions Kim (1999, p. 19 &

p. 2)

Systems thinking is a school of thought that focuses on recognizing the interconnections between the parts of the system and

synthesizing them into a unified view of the whole… Systems thinking is a way of seeing and talking about reality that helps us better understand and work with systems to influence the quality of our lives.

Maani & Cavana, (2009, p. 7)

Maani & Cavana (2000, p. 135)

Systems thinking is a scientific field of knowledge for understanding change and complexity through the study of dynamic cause-and-effect over time. Complexity underlies most business, economic, natural and social systems. System thinking has three distinct but related

dimensions: paradigm, language and methodology, …

… is the ability to see things as a whole. It combines the art of seeing interconnections and the science of explaining complexity.

Richmond (1994, p. 6)

Systems Thinking is the art and science of making reliable inferences about behaviour by developing an increasingly deep understanding of underlying structure.

Sterman (2000, p. 4) Systems thinking – the ability to see the world as a complex system, in which we understand that “you can’t just do one thing” and that “everything is connected to everything else.”

Meadows (2008, p. 2)

Systems thinking helps us manage, adapt, and identify the wide range of choices we have. It is a way of thinking that gives us the freedom to identify root causes of problems and see new opportunities.

Senge, (2006, pp. 68 & 69)

Systems thinking is a discipline for seeing wholes. It is a framework for seeing interrealtionships rather than things, for seeing patterns of change rather than static “snapshots.” … is a discipline for seeing the “structures” that underlie complex situations, and for discerning high from low leverage change.

Capra (1996, p. 30) Systems thinking is synthesis and ‘contextual’, rather than analytical thinking. Analysis means taking something apart in order to

understand it; systems thinking means putting it into the context of a larger whole.

Systems thinking uses tools such as causal loop diagrams and behavior-over-time graphs to visualize and build the skills required to identify and understand relationships and feedback loops in systems. Such tools are helpful as dynamic learning is difficult (Sterman, 2000), and people, while not inherently incapable, usually lack the requisite expertise to think in systems (Forrester, 1975; Karakul & Qudrat-Ullah, 2008). The roots of systems approaches are based in biology, cybernetics, and ecology (Bateson, 1972; Churchman, Ackoff, & Arnoff, 1957; Vester, 1988; von Bertalanffy,

1945). Reservations have been expressed as to how transferable a systems approach that was developed in the natural systems is for social systems (Ulrich, 2005). This view is not shared by everyone and many systems models exist that integrate human behaviour (e.g. Forrester, 1971; Sterman, 2000).

4.2.2 System dynamics

System dynamics involves the construction of quantitative stock and flow models to show accumulation over time and the dynamics that occur as a result of delays inherent in a system. The strength of system dynamics is the model simulation capability that reveals the behaviour of the system over time, and how the long-term effects of an intervention might play out (Ford, 2010; Forrester, 1994; Hürlimann, 2009; Morecroft, 2007; Sterman, 2000). Without simulation, it is argued, it is not possible to demonstrate the logical implications of a model and compare this with reality (Hovmand, 2014; Sterman, 2000). Even if there are significant uncertainties regarding data and how to include soft variables, quantitative models, it is argued add value over and above qualitative models (Ford, 2010; Homer & Oliva, 2001). A quantitative model can be validated against data, which boosts confidence in the model’s explanatory powers (Robèrt, 2000; van den Belt, 2004). Regardless of whether the outputs are correct or incorrect simulation models are seen to be useful ways to explore the nature and relationships of a system and provide insights on feedback loops (Robèrt, 2000). Additionally, quantification provides an opportunity to learn about the order of magnitude of different variables, it provides a reality, and is a way to identify gaps in understanding and data (van den Belt, 2004).

System dynamics modelling is a powerful and valuable tool but was not applied with this research due to the large number of variables that influence sustainable well- being. Instead a systems thinking approach is used.

4.2.3 Research rationale

The research approach taken in this dissertation is to provide insights into well-being interactions and behaviour through better understanding of the structure of the well- being system rather than trying to explain phenomena with increased levels of detail and data. Dealing with complexity by using more advanced computing ability and data it is argued is the wrong approach (Dryzek, 2005; Northrop, 2011; Vester, 2007). The

resultant flood of information creates both insecurity and confusion, leading to a situation where information is interpreted as knowledge (Deming, 1997; Dryzek, 2005; Northrop, 2011; Senge, 2006; Vester, 2007).

With this research initial efforts to build a system dynamics model of well-being confirmed the findings of Smith (2010) that systems models can rapidly advance to a level of complexity at which understanding breaks down. Figure 4-3 from Robinson (2011) sets out the diminishing returns in terms of accuracy as levels of complexity increase. An additional research obstacle was lack of data on how the various indicators inter-link resulted in the need for a large number of assumptions to be made. This was problematic, as a wrong assumption makes the accuracy of a model decline rapidly.

Figure 4-3: Simulation model complexity and accuracy. (Source: Robinson, 2011, p. 1429).

An accepted alternative systems approach is to work with stakeholders through dialogue using a process that is flexible and transparent (Hovmand, 2014; Hürlimann, 2009; van den Belt, 2004; Vester, 2007). This is the approach applied here. It involves implementing a number of commonly used systems techniques that support integrated thinking and learning. These tools are set out in the next section.