Synthesis

(1) Cristian S. Calude, Giuseppe Longo. "The Deluge of Spurious Correlations in Big Data.” Foundations of Science (March 2016): 1-18.

Abstract:

Very large databases are a major opportunity for science and data analytics is a re- markable new eld of investigation in computer science. The effectiveness of these tools is used to support a philosophy against the scientific method as developed throughout history. According to this view, computer-discovered correlations should replace under- standing and guide prediction and action. Consequently, there will be no need to give scientific meaning to phenomena, by proposing, say, causal relations, since regularities in very large databases are enough: with enough data, the numbers speak for themselves . The end of science is proclaimed. Using classical results from ergodic theory, Ramsey theory and algorithmic information theory, we show that this philosophy is wrong. For example, we prove that very large databases have to contain arbitrary correlations. These correlations appear only due to the size, not the nature, of data. They can be found in randomly generated, large enough databases, which as we will prove implies that most correlations are spurious. Too much information tends to behave like very little information. The scientific method can be enriched by computer mining in immense databases, but not replaced by it.