Correlations without an encompassing concept yield no real knowledge

Evidence

But causality does not stand at the highest level of knowledge. Reciprocity (Wechselwirkung) represents a more complex relation than causal relation. It declares: A and B condition each other mutually. A necessary connection holds between A and B. And yet, even at the level of reciprocity, the overarching context for the connection between A and B has not yet been grasped, i.e., conceived (begriffen): ‘If we stop at considering a given content just from the point of view of reciprocal action, we are in fact proceeding quite unconceptually.’ … Only the ‘Concept’ brings forth knowledge. The Concept is C, which comprehends within itself (in sich begreift) A and B – here, A and B are first conceived (begriffen). The concept is the higher context surrounding A and B, which provides the foundation (Begründung) for the relationship between A and B. Thus, A and B stand as ‘moments of a third, higher [whole]’. Knowledge becomes possible only at the level of the Concept: ‘The Concept dwells within the things themselves, it is that through which they become what they are, and to comprehend an object means therefore to become conscious of its concept.’ Only from the all-comprehending Concept C is complete comprehension (Begreifen) of the correlation between A and B possible. In contrast, Big Data affords only extremely rudimentary knowledge, that is, correlations in which nothing is comprehended. Big Data lacks comprehension – it lacks the Concept – and thus it lacks Spirit. The absolute knowledge intimated by Big Data coincides with absolute ignorance. — Byung-Chul Han, Psychopolitics, 160

Categorization

• DEFINE: Defines Han’s core distinction: correlation (Big Data) vs. the Hegelian Concept as the only source of genuine comprehension.
• DEEPTHINKING: The compressed Hegelian argument that data-correlation is categorically inferior to conceptual comprehension carries the whole chapter and rewards sitting with it intra-class
• XREF: Connects to my reading on epistemic uncertainty in colonial archives (Along the Archival Grain) - both attack the notion that accumulating data points yields understanding. — connects to: Correlational knowledge lacks the necessity that causal knowledge requires — Byung-Chul Han, Psychopolitics: Neoliberalism and New…, 159; Aggregate correlations cannot reveal the belief structures of individuals — John T. Jost and Jim Sidanius, Political Psychology, 1933; Big data correlations alone cannot deliver causal knowledge to prediction systems — Shoshana Zuboff, The Age of Surveillance Capitalism, 930 (+6 more)

Related

• Correlational knowledge lacks the necessity that causal knowledge requires (similarity 0.93)
• Correlation knowledge without causality is, for Hegel, primitive ignorance (similarity 0.91)
• Big Data’s correlational mode is displacing causal and spirit-driven knowledge (similarity 0.89)
• Aggregate correlations cannot reveal the belief structures of individuals (similarity 0.79)

Broader Connections

• Big data correlations alone cannot deliver causal knowledge to prediction systems #exemplifies (0.85) — Shoshana Zuboff, The Age of Surveillance Capitalism, 930
• Good theory explains data but cannot remain at data’s level #corroborates (0.85) — Mark Fisher (edited by Darren Ambrose), K-Punk, 2456
• Aggregate correlations cannot reveal the belief structures of individuals #exemplifies (0.82) — John T. Jost and Jim Sidanius, Political Psychology, 1933
• Correlation knowledge without causality is, for Hegel, primitive ignorance #corroborates (0.80) — Byung-Chul Han, Psychopolitics, 159
• Understanding arises only from perceiving causal links among facts, not from the facts themselves #corroborates (0.80) — Janet Burroway; Elizabeth Stuckey-French; Ned Stuckey-French, Writing Fiction, Tenth Edition, 471
• Large samples produce significant correlations that explain little variance #exemplifies (0.80) — Roderick M. Kramer, Geoffrey J. Leonardelli, Robert W. Livingston, Social Cognition, Social Identity, and…, 1036
• Correlational accuracy measures cannot reveal over- or underestimation direction. #exemplifies (0.75) — , Rudman, Laurie A - On the Nature of…, 872
• Big Data’s correlational mode is displacing causal and spirit-driven knowledge #exemplifies (0.75) — Byung-Chul Han, Psychopolitics, 158