心理学と心理療法のジャーナル
オープンアクセス

概要

Surprise of Complexity and Complexity of Surprise: What Happened to Predictability? Limits and New Possibilities of Complexity for Physical, Psychological and Social Sciences

Rosalia Condorelli

Complexity, as emergent self-organization, is indicative of the end of a science which has always rooted its own aims, its own reasons, its own meaning in the most exalting project: making the uncertain certain, knowing order so to predict and control the future, what is unknown. Emergence, with its burden of surprise and unpredictability, and non-linearity meet at all levels, in physical, inorganic and material, systems as well as in living, human and mental systems. At this point, some question could be raised: if this is true, if everything is emergent, surprising, unpredictable, where does this acknowledgment lead us? What is the sense of Science? What are its objectives? Does Complexity ‘promises’ and ‘permits’ us to better understand nature and the real behaviour of systems, or are emergence, surprise, and unpredictability really sic et simpliciter only alibis to hide our scientific failures? Are we at the end of Science? Complexity, as “essential unpredictability” opens up problems which all Sciences must face. And certainly, puts to the test our ability to redefine the way of conceiving Science and its objectives, of viewing and doing Science. This paper faces this issue. Innovating in respect to the concept of classical science, the study of Complexity and Chaos, although it cannot articulate on forecasting purposes, revolves around the imperative of taking into account underlying mechanisms governing real phenomena and structuring and generating stability and emergence, by modelling deterministic non-linear interaction structure in physical and human systems. In Physics, Chemistry, Geology, Biology, Medicine, Neuroscience as well as in Sociology, Psychology and Psychotherapy, Economy, Politics and International Relationships, non-linear modelling can introduce a new way of conceiving predictability.