EEG-riculture: Sustainability and Butterfly-Effects

The motivational background of this paper is to shed new light on the phenomena of butterfly effect and sustainability from a scientific-philosophical and mathematical point of view. We aim to reveal the connection between butterfly effect and sustainability by observing the observer him- or herself and exploring the most significant errors of thinking and operation of the subject, while analyzing the peculiarities of the butterfly effect. Our reasoning is based on cognitive science approach, agricultural scientific experiments, and on parallel EEG (electroencephalogram) measurements. The latter, emerged from the research area of Innoria’s Team Flow Research Team, is a completely new methodological approach in the field of cognitive science on the basis of previous comparative behavioral scientific results1, but built up on new technological opportunities and professional standpoint2,3. As a result, we can see a new contexts and define problems in measurement methodology, while researching the interactions of human minds. These EEG measurements are part of an extensive research, which focuses on the identification of the parallel perception of reality and the synchronized perception-reaction relation of human beings. In the philosophy of science approach the butterfly effect is always provided by the observer by using in his/her rationing the indicator 'small' or 'seemingly insignificant', while one finds that the effect is not linearly related to such approximate (quantitative) attributes of the cause. The consequence is unexpectedly, unpredictably large, as compared to the observer's expectations. Therefore, the problem requires a change of perspective, namely, one needs to confer much greater importance to small causes. To discover these causes, we need to explore the mechanism of human observation much more intensively. The mathematical objective of the paper is to demonstrate an explored butterfly-effect process, based on a real, but anonymous parallel measured EEG data asset, where each step is reproducible. The problems that need to be solved are: (i) How can we classify correctly over EEG measurements the personal time series data (raw individual EEG data series with 0.25 second sampling) within the frame of similarity analysis? (ii) How to deal with the butterfly effect? (iii) How to step forward on the theoretical path of chaotic systems4 designated by Edward N. Lorenz? The butterfly-effect is the unexpected difference between the result of a classification based on a given data asset and the result of another classification, based on a data asset, having just one additional record as the input; in this case, we have data at about every 0.25 s, where the used length of the time series can be over 100 or 1000. Differences will be derived by means of ranked inputs – especially in case of data having the same value. Similarity analysis is a typical ranking-oriented modelling scheme, where these special effects can be detected at once, without the need for any further manipulations. Since similarity analysis produces model chains, symmetry-driven similarity analyses can have, as well, butterfly-effects in a consistence-oriented model structure. Sustainability can be regarded as a mathematical issue5, being a dynamic phenomenon. Sustainability may be redefined as a capability of forecasting system behavior. Random-like, not-planned incidents cannot be accepted as sustainable and realized plan values. The most trivial usage of the ‘here and now’ characterized sustainability approach is precision farming and its analogy, the EEG-riculture, as such.