18
F. Maggino et al.
be developed and managed so that they represent different aspects of the reality.
They picture the reality in an interpretable way, allow meaningful stories to be told
and support evaluations and decisions.
Therefore, any discussion of indicators must start from two fundamental questions: what are indicators? and why are they so important?
In order to fully understand the importance of indicators, complexity needs
to be addressed. In his “millennium” interview on January 23, 2000 (San Jose
Mercury News), Stephen Hawking said: I think this century will be the century
of complexity (Gorban and Yablonsky 2013). We can encounter this concept in
different fields (e.g., physics, chemistry, biology, engineering, software, social
sciences). It is sometimes abused and used interchangeably with other ones (e.g.,
large, complicated), nevertheless having different meanings. It has no precise
meaning and no unique definition (Erdi 2008). This notion does not belong to a
particular theory or discipline, but rather to a “discourse about science”. According
to Morin (1984), we cannot approach the study of complexity through a preliminary
definition: there is no such thing as “one” complexity, but “different” complexities.
“Complex” is often associated with the concept of “system” and the topic of
“complex systems” is a subject of great scientific debate and interest. While a
simple system has a small number of components with defined roles and clear
rules, a complex one contains many elements, which are interdependent and interact
non-linearly. A system isn’t just any old collection of things. A system is an
interconnected set of elements that is coherently organized in a way that achieves
something (Meadows 2009, 11). This definition takes up and updates the idea
expressed by Aristotle in The Politics: the whole is something over and above its
parts, and not just the sum of them all. The analysis and understanding of complex
systems require approaches allowing more concise views. The guiding concept is
synthesis. Generally speaking, synthesizing responds to a need for concreteness
in the relation with things. It is justified by the fact that knowledge of complex
phenomena involves some form of reductio ad unum (Sacconaghi 2017). The correct
way of understanding those phenomena is to conceive them as a whole, adopting a
synthetic approach.
Getting in contact with reality always involves some process of synthesis, more
or less conscious, consisting in the reduction of a multiple in units. This reduction
could be a risk. Any synthesis should be a stylization and not an over-simplification
of reality.
Indicators play a key role in describing, understanding and controlling complex
systems. An indicator is, therefore, a tool for understanding reality. It is not
necessarily a number. It can be an object, a map, an image. It is what allows us
to grasp the complexity and guide us in understanding it. There is a large amount
of literature on the use of metaphoric images for the representation of phenomena,
especially for complex ones (Lima 2013; Tufte 2015). In Fig. 1, we can see an
example of the representation of multidimensional poverty in Italy (2008) and
its dynamics (Lima 2013). This infographic shows how poverty “red thread” has
various weight, that depends on the different criterion and perspective that the
Italian National Institute of Statistics – Istat used to photograph the society. As
F. Maggino et al.
be developed and managed so that they represent different aspects of the reality.
They picture the reality in an interpretable way, allow meaningful stories to be told
and support evaluations and decisions.
Therefore, any discussion of indicators must start from two fundamental questions: what are indicators? and why are they so important?
In order to fully understand the importance of indicators, complexity needs
to be addressed. In his “millennium” interview on January 23, 2000 (San Jose
Mercury News), Stephen Hawking said: I think this century will be the century
of complexity (Gorban and Yablonsky 2013). We can encounter this concept in
different fields (e.g., physics, chemistry, biology, engineering, software, social
sciences). It is sometimes abused and used interchangeably with other ones (e.g.,
large, complicated), nevertheless having different meanings. It has no precise
meaning and no unique definition (Erdi 2008). This notion does not belong to a
particular theory or discipline, but rather to a “discourse about science”. According
to Morin (1984), we cannot approach the study of complexity through a preliminary
definition: there is no such thing as “one” complexity, but “different” complexities.
“Complex” is often associated with the concept of “system” and the topic of
“complex systems” is a subject of great scientific debate and interest. While a
simple system has a small number of components with defined roles and clear
rules, a complex one contains many elements, which are interdependent and interact
non-linearly. A system isn’t just any old collection of things. A system is an
interconnected set of elements that is coherently organized in a way that achieves
something (Meadows 2009, 11). This definition takes up and updates the idea
expressed by Aristotle in The Politics: the whole is something over and above its
parts, and not just the sum of them all. The analysis and understanding of complex
systems require approaches allowing more concise views. The guiding concept is
synthesis. Generally speaking, synthesizing responds to a need for concreteness
in the relation with things. It is justified by the fact that knowledge of complex
phenomena involves some form of reductio ad unum (Sacconaghi 2017). The correct
way of understanding those phenomena is to conceive them as a whole, adopting a
synthetic approach.
Getting in contact with reality always involves some process of synthesis, more
or less conscious, consisting in the reduction of a multiple in units. This reduction
could be a risk. Any synthesis should be a stylization and not an over-simplification
of reality.
Indicators play a key role in describing, understanding and controlling complex
systems. An indicator is, therefore, a tool for understanding reality. It is not
necessarily a number. It can be an object, a map, an image. It is what allows us
to grasp the complexity and guide us in understanding it. There is a large amount
of literature on the use of metaphoric images for the representation of phenomena,
especially for complex ones (Lima 2013; Tufte 2015). In Fig. 1, we can see an
example of the representation of multidimensional poverty in Italy (2008) and
its dynamics (Lima 2013). This infographic shows how poverty “red thread” has
various weight, that depends on the different criterion and perspective that the
Italian National Institute of Statistics – Istat used to photograph the society. As