Developing a Pattern Language For Science-Engineering-Art-Design (SEAD) Collaborations

Acknowledged similarities and prospective synergies underlie the work of artists and scientists in perceiving and describing the natural world; however, there are few formalized or easily recognized methods to utilize those commonalities for enhancing art-science collaborations. The notion that observable or archetypal patterns could represent a form of communication or a language among professionals from different fields was explored by architect Christopher Alexander, who introduced a pattern language consisting of hierarchically arranged patterns that are linked together in countless ways. Whereas the use of patterns and rhythms to communicate art or music is relatively straightforward, the utility of spatial or temporal patterns to portray or explain scientific data is less obvious. Some branches of science focus specifically on nature’s patterns and rhythms; however, most scientific data can be expressed in terms of frequencies, cycles, and other common descriptors of temporal patterns, as well as distributions, geometries, and similar descriptors of spatial patterns.

The use of a pattern language for art-science communication would initially identify and collect numerous patterns to be labeled and categorized, but discernable similarities among seemingly different patterns would likely reduce this number over time. Distinguishing among natural, ideal, and abstract patterns could be a first step in categorization and might provide a handy framework for users to perceive relationships among different forms of information (e.g., ideas, data, designs). The precise structure or syntax of the pattern language (i.e., naming, ranking, illustrating, exemplifying, and referencing patterns) should be less important than an unambiguous depiction of the collected patterns and their links. Patterns and rhythms may be more recognizable to people than are words, numbers, or symbols, thus providing a means of communicating across disciplines and cultures.

This paper was presented as a SEAD white paper, and supported by the National Science Foundation under Grant No.1142510.