A good framing action might be based on the system behaviour perception. In other words it answers to the question: Where do we expect starting patterns of interactions change? Any frame is identified by who is attempting to describe the system for a particular purpose and therefore affected by biases, interests and vision. Cilliers (1998) explains this concept through the interrelation of the framing process to the position of the observer of the system. Moreover, framing is to be related to the temporal dimension of the system, not only they evolve through time, but past events are co-responsible of present behaviour. Ignoring the time dimension could produce inaccurate representations, synchronic snapshot of diachronic processes. Such kind of action is a very delicate one. There is a need to communicate and share how the framing process has been performed in order to discuss and to create consensus among all the actors involved in a specific design process. The action of framing ensures the system is defined in both relevant and manageable way, working on the domain extension field is wanted to be known.
4.2. Graining In order to manage the description of the system, what information accuracy will be considered, have to be also decided. To set a resolution level, defining the systems structure, could be useful to arrange a process of graining. To grain information is a fundamental action considering the amount of sensible data much greater than the available, perceptible and intelligible one. Even if we assume the possibility of obtaining all the information about a complex system it will be almost impossible to use it, since it creates a situation of information overload6. Furthermore, analysing a complex system implies the acquisition also of noisy and incomplete data. Their huge amount, its noisiness and incompleteness if associated to a lack of selected and monitored data, constraints the system describer to a cumbersome filtering and sieving procedure. The building of tools able to effectively parse data is required. Graining is the properly tool for doing that. It operates by making approximations, by ignoring details on finer scales, creating grained observation of the system at a resolution that shows the overall pattern of the system and the pattern of the elements in it. It is a crucial process for highlighting the regularities immersed in the observed system7. Adopting graining as conceptual tool, however able to transform the way we look and act in complex systems, it is possible to set two end points of a continuum where the various way where grained observations could fit in. On one hand are fine-grained observations, a near sighted way to perceive rendering detailed impressions, on the other hand coarse-grained observation, a far sighted observation rendering rough impressions. In other terms, if we make a coarse observation, the system describer can consider only large cluster of agents in the systems (i.e. institutions) obviously a lot of smaller detail will get lost in this process. On the contrary, a fine system examining, in microscopic details, the system observer has to keep track of each agent and of all patterns. Looking for regularities could be obscured “by the buzzing activity at lower level”8 (Cilliers 1998). Grain is a quite complicated concept, and requires more than a metaphor to clearly depict how it works. In addition, another example could be given (Gell-Mann 1995): envisioning taking a whole picture of a complex system in order to spot on a very small detail, the observer should zoom a lot the picture. Reaching a certain level he will only see the single grain of the picture film 6 The term was used by Toffler in 1970 and is often used to describe the simple notion of receiving too much information. It has led to various synonyms and related terms as for example cognitive overload, sensory overload , communication overload, knowledge overload, or information fatigue syndrome. 7 It is useful to remind that Complexity science mostly asks: What causes order and regularities? (Mainzer 1996) . 8 A useful example is given by Chris Stephens: consider the number of degrees of freedom of the atoms composing a solid object (like a pen). This is enormous (≈ 1022). However, in order to describe the motion of a solid object, we just need 6 degrees of freedom. We have then a very much reduced “coarse-grained” description in terms of many fewer variables. So, we need to understand how more appropriate effective degrees of freedom, such as the translational and rotational ones of the rigid body, emerge and offer a more appropriate description of the dynamics. Of course, the coarse-grained description is not exact. How the resultant loss in precision affects the description depends on what one wants to know about the system.
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