ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Measuring Uncertainties Through Uncertainties: A Theoretical Approach

11 September 2017, 10:50 – 11:10


Submitted by
Filomena Maggino
Filomena Maggino (Università di Firenze), Carolina Facioni (Istituto Nazionale di Statistica - ISTAT), Isabella Corazziari (Istituto Nazionale di Statistica - ISTAT)
What does define a work as “scientific”? While pointing out that the definition of "scientific" is far from unproblematic, and that behind it there is a long debate – perhaps, not yet concluded - we can accept the fact that a scientific work is characterized by a systematic, controlled, empirical, and critical approach. Anyway, when our aim is to draw the possible developments of future events, we are faced with a practical obstacle. Indeed, we cannot have any empirical experience of the future. Have we, therefore, to be inferred that forecasting, exploring future – or, better: exploring futures (Barbieri Masini. 2000) – anticipating futures (Arnaldi, Poli, 2012) - have not to be considered activities of a scientific kind?
Of course, they are, in a very particular and complex way, which involves many sciences. For example, designing the possible future trends is a very practiced exercise in statistics. It belongs to the field of inferential statistics, aimed at establishing knowledge from data by taking into account the error associated to them. This kind of knowledge allows statistical forecasts and predictions to be determined. One of the logical and instrumental concepts allowing trends to be read is that of change - and, of course, its opposite one, stability - which is far from being easy to be defined and managed through observed data (Maggino, Facioni, 2015). This is particularly true in the presence of complex phenomena, such as those defining and composing, e.g., the quality-of-life topic.
A great help in understanding complexity and trends, comes from the whole contributes of methods to analyze multi-way or multi-mode data, developed extensively in years ’80s-‘90s. One of those methods, applied in many different fields (social, demographic, economic, environmental) is the Dynamic Factor Analysis (DFA) (Coppi and Zannella, 1979; Corazziari, 1999), a method for multi-way data, based on the joint application of a factorial analysis and regression over time. DFA considers quantitative array of data classified according to three criteria: statistical unit, quantitative variable and time of data collection.
The Futures Studies approach is in Europe finds its theoretical basis in the French Bertrand de Jouvenel’s philosophical reflections (de Jouvenel, 1964). We can find a link between philosophical theory of futures and its translation in the practice of social research in de Jouvenel’s theorization about possible, probable, and desirable futures. How can we understand if a probable future can be more - or less - probable respect to a different hypothesis of future? Answer to such a difficult question requires a multidisciplinary approach, where statistical models, methodology of social science are enhanced in their ability to express the change - and sometimes the risk that the change itself implies.

Return to programme