Tagliagambe, Silvano2018-09-242018-09-242017Ethics in Progress (ISSN 2084-9257). Vol. 8 (2017). No. 1, Art. #8, pp. 117-146. Doi: 10.14746/eip.2017.1.82084-9257http://hdl.handle.net/10593/23881In 2008 Chris Anderson wrote a provocative piece titled The End of Theory. The idea being that we no longer need to abstract and hypothesis; we simply need to let machines lead us to the patterns, trends, and relationships in social, economic, political, and environmental relationships. According to Anderson, the new availability of huge amounts of data offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models and unified theories. But numbers, contrary to Anderson’s assertion, do not, in fact, speak for themselves. From the neuroscience’s standpoint, every choice we make is a reflection of an, often unstated, set of assumptions and hypotheses about what we want and expect from the data: no assertion, no prediction, no decision making is possible without an a priori opinion, without a project. Data-driven science essentially refers to the application of mathematics and technology on data to extract insights for problems, which are very clearly defined. In the real world, however, not all problems are such. To help solve them, one needs to understand and appreciate the context. The problem of landscape becomes, for this reason, critical and decisive. It requires an interdisciplinary approach consisting of several different competencies and skills.itainfo:eu-repo/semantics/openAccessBig DataModelProjectLandscapeAntifragilityDemocracyArtykułhttps://doi.org/10.14746/eip.2017.1.8