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A Reward-Driven Process for Local, Noospheric, and Computational Detection of Stochastic Deviation Fields

This article was originally published in the MIT Press journal Leonardo. The current implementation is conceptually equivalent (and rather fun).

This abstract outlines an experimental process for detecting stochastic deviations. The process harnesses a noise with known overlaps onto common experience. It is theorized that humans will seek these overlaps; it is further theorized their search will measurably prejudice the noise.

The noise is presented as a random selection of three alphabetic characters. In most cases the presentation will be meaningless; but, a few percent of the time, a word or a pattern will emerge. Organized in the guise of a game—the search for recognizable words—the process entices repeated interactions.

The process employs a pair of computer programs: a user interface controller written in JavaScript, and a relational data collection and analysis backend written in PHP. This configuration invites community participation, but, as it relies on an open portal to the Internet, some considerations are required for installation. For robustness, local computer hardware is stripped of keyboard and outfitted with a reduced-function “mouse,” only capable of clicking.

To assist participants in loading the experimental field with potentially stochastic altering thoughts, visual aids, including a list of recent deviation candidates found, and a physical dictionary of three letter words, are part of the installation.

At the conclusion of the installation a poem of the computationally detected stochastic deviations is read.

Noise is data composed of absolute randomness. Recognized words may seem like “brilliant” noise as they rise above their surrounding muck triggering human and machine interest, but they are not. Recognized words are an expected part of the noise stream; they do not, by themselves, indicate a stochastic field distortion. To indicate such distortion requires a more subtle and perhaps poetic repetition of recognitions.

Both artists and scientists value brilliance. Many believe that the brilliance within each discipline can spill beyond its professional borders. To some extent this osmosis of brilliance may rely on stochastic field distortions similar to those described herein.

Previous researchers have demonstrated aesthetic possibility within aleatoric processes. John Cage frequently used the I Ching as a random number source for his compositions. Although Cage chose a fortune telling system familiar to millions, he avoided its oracular claim to detect and/or cause significant chance deviations. The 3Letter process shares Cage’s belief that well structured questions can be aesthetically answered by ostensibly noisy sources; unlike Cage, this new process specifically seeks significant deviations within that noise.

Contemporary noise deviation research centered at the Princeton Global Consciousness Project provides both visual and aural representations of data accumulations, but does not specifically mine for poetic possibilities. They seek event / noise correlation by focusing on narrow world moments; in contrast, the 3Letter process invites holistic memory correlations amidst the inclusive field of common vocabulary.

It is hoped that this exploration of the boundaries surrounding both art and science will establish an illuminating basis for further research.