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What happens to a model when the world it claims to represent changes? This question is the starting point of the book, inspired by Paolo Savona's suggestion to explore the proposal of transitioning from econometrics to e.conometrics, formulated with Antonio Simeone and Marco D'Ambra, through a logical-semantic and epistemological examination of the conditions of economic knowledge.
Artificial intelligence, machine learning, nonlinear models, and the increasing availability of data seem to promise an unprecedented predictive capability. But just as computational power increases, questions resurface that no algorithm can evade: does predicting also mean explaining? Is a more accurate prediction necessarily better knowledge? What happens when the prediction enters the world it forecasts and contributes to changing it? And how do we recognize the moment when a model, while still functioning, no longer adequately represents reality?
It is at this threshold that the book crosses the boundaries of econometrics without abandoning them. Economics, philosophy of science, complexity theory, artificial intelligence, and, with necessary caution, some transformations introduced by contemporary physics engage in dialogue around a common problem: what can we call knowledge when the world changes as we attempt to understand it? The discussion with Popper, Kuhn, Lakatos, Hayek, Prigogine, and Morin intertwines with the issues posed by Homo oeconomicus, Homo-Abstractus, and the progressive externalization of cognitive abilities in the algorithmic environment.
From this emerges a reflection where error does not merely represent the failure of prediction but can become information about the change in reality; validation no longer coincides with a conclusive check but becomes an open process of verification and revision; and even the ability not to predict, when reliability conditions fail, can assume epistemic value.
The question mark in the title is therefore essential. Is e.conometrics truly the beginning of a new paradigm of economic knowledge? The book does not anticipate the answer: it tests its possibility. Because in the age of artificial intelligence, the challenge is not only to build tools capable of predicting more but to understand what we can know, under what conditions, and for how long.
Presentation by Paolo Savona
“Un tramonto abbelliva il cielo di Forte. Il mare era color viola, il sole che si spegneva lentamente all’orizzonte era di un rosso vivido con raggi che variavano dal verde all’azzurro. Piccole, sparse nuvole dai riflessi arancione completavano il quadro.”
“Siamo presi nel fuoco incrociato di due spacciatori e dei signori del riciclo di denaro sporco che temono le tue, e forse, le mie indagini”.
Luciana La Stella è psicoanalista e psicologa. Laureata in Psicologia, Filosofia, Economia e Giurisprudenza, ha conseguito un Master MBA SDA Bocconi. Membro OPL – Ordine Psicologi Lombardia e SLP – Scuola Lacaniana di Psicoanalisi, dal 2015 è il Presidente di OPIFER – Organizzazione Psicoanalisti Italiani Federazione e Registro, membro I-AEP Inter-Associatif de Psychanalyse e FEDEPSY – Fédération Européenne de Psychanalyse. Ha collaborato con lo psichiatra Carlo Viganò in attività di formazione e ricerca. Da sempre è interessata alla multidisciplinarietà e ai diversi saperi, nell’ottica di un’integrazione nell’ambito della conoscenza e della scienza. Collabora con Paolo Ferrari nei Seminari multidisciplinari In-Assenza. Docente MIUR presso SPPG e CPL, collabora con l’Università di Cesena e di Mosca. È autrice e curatrice di molte pubblicazioni scientifiche e dirige per NeP la collana “Nuovi Orizzonti di Inconscio e Società”.