… But under conditions of data abundance, and even population comprehensiveness, when every point in a field can be accounted for and represented, where n of sample = n of population, then the modality of pattern shifts off-center from one of reductive average to other emergent, shifting, plural clusters of likeness, difference, relation, proximity, and inferred causality.20 Multiple and unlike queries of one set are more easily expressed, such that one data point might be more or less significant depending on what is asked about the entire population as a whole. The mean is no longer the spine of the diagram, but instead the possibility of a less final form of incremental variation within an open field without center. Because of that plurality, the seeing and recognizing of patterns within diagrams becomes a more contingent and partial reading delimited as much by the textuality of inquiry as by the materiality of the inquired-upon. Imagine the superimposition and stacking of multiple, layered visualizations of the same information, diagramming different distributions, each the result of differing inquiries (models really) upon its matrix of data points. Eventually the accumulated diagrams become absolute, opaque, over-coded, hyper-imposed within one another; all points made to appear according to some possible model: pattern not as mean or median but as torque. That arc toward a heterogeneity of response tilts away from spinal averages and toward the opposite of information (noise) and the inverse of pattern (the plainness and openness of the well-blackened canvas).

This endeavor will require an understanding of questions such as: how do organizations evolve and maintain focus and momentum for 100 years or more; what models have supported long term technology development; what resources and financial structures have initiated and sustained prior settlements of “new worlds?