Generative Art Theory
ion where basic elements are rotated, translated, and modulated in scale was practiced by the Argentinians Eduardo McEntyre and Miguel Angel Vidal (Osborne 1981) The term has often also seen unrelated generic use. For example, Richard Serra has referred to some of his works as "generative" in the sense that they introduced a new basic form he would go on to use in a series of later works (Serra et al. 1980) Critic Lawrence Alloway introduced the term "Systemic Art" when he organized the well known exhibition Systemic Painting (1966) at the Solomon R. Guggenheim Museum in New York. He understood systemic art as a form of abstraction using simple standardized forms that are repeated on the basis of a clear organizing principle. Noland's series of chevron paintings, begun in 1963, would be a prime example. Alloway may have shifted his intended meaning closer to that of generative art when he also extended the term to process oriented color field painting (Osborne 1981) "Generative Systems" was introduced by Sonia Landy Sheridan as an academic program at the School of the Art Institute of Chicago in 1970. It was a response to the telecommunications and imaging technologies, but also political and pedagogical ideas, of the time. The hue shifts and distortions created by the color copier technologies that artists used at the time could be viewed as related to the understanding of "generative" here. Beginning with the album Discreet Music (1975) musician Brian Eno popularized the term "generative music" with a meaning relatively consistent with "generative art" as it is used here. There was, however, a somewhat narrow focus on repeated note patterns, phase shifts, and tape loops that had already been introduced in the 1950s by composer Terry Riley, and further developed in the 1960s by composer Steve Reich. The architect Celestino Soddu helped to popularize the use of the term "generative art" by creating the International Generative Art Conference in 1998. While the conference continues to invite all manner of opinion on generative art, Soddu's own definition leans toward a genetic approach that captures the aesthetic of the artist/programmer rather than leading in arbitrary directions. The original call for participation states: Generative Art builds possible worlds by creating evolutionary rules that produce events that if, on one side, they are unpredictable and amazing, from the other one they mirror the identity and recognizability of the idea, they are the natural representation of it. (Soddu 1998) The encoding of an artist's vision as a system is certainly a valid approach to generative art, but it isn't the only valid one. In some cases, for example, the generative artist creates a system without a pre existing vision of what the result should be. The artist then explores the system as a new territory and discovers treasures here and there along the way. Complexity, Systems, and Generative Art If the defining feature of generative art is the artist ceding control to an autonomous system, it is worth exploring whether a deeper understanding of systems can shed additional light on generative art. For a state of the art view of systems we can turn to the field of complexity science where a revolution in systems thinking is taking place. But before doing that, some previous notions regarding systems, system complexity, and aesthetics should be considered. 156 p h i l i p g a l a n t e r 20th Century Notions of Complexity in Aesthetics Birkhoff's aesthetic measure In 1933 the mathematician George David Birkhoff proposed what he called the "Aesthetic Measure." With this measure he relates "aesthetic effectiveness" (M) to "the degree of complexity" (C) and "the degree of order" (O) using the formula M O/C. The operationalization and application of this formula almost immediately turned out to be problematic. Nevertheless it was an interesting attempt in that, as Birkhoff points out, "The well known aesthetic demand for 'unity in variety' is evidently closely connected with this formula" (Birkhoff 1933) What is often overlooked is that Birkhoff based his formula on what we would now call a (speculative) neuroaesthetic theory (Skov and Vartanian 2009, iv, 302) He describes complexity (C) as the degree to which unconscious psychological and physiological effort must be made in perceiving the object. Order (O) is seen as the degree of unconscious tension released as the perception is realized. Shannon's information theory and the information aesthetics of Bense and Moles While we have an intuitive sense of what we mean when we refer to a system as "simple" or "complex," it is not easy to develop a formal technical measure of complexity that corresponds well to our intuitive sense. Unrelated to Birkhoff, information theory, as an attempt to better understand communication systems, was developed by Claude Shannon in 1948. Shannon was interested in analyzing the capacity of communication channels, and one of his core ideas was that the more "surprise" a given communication exhibits, the more information it contains (Shannon 1948) From the information theory view, a stream of characters consisting of just the letter "a" over and over again offers no information whatsoever. A stream of English language sentences would offer considerably more variation and thus more information. The information density can be further increased, and redundancy reduced, via contractions and acronyms. Cell phone text messages such as "R U there" or "Don't B L8" are examples. Ultimately, a stream of totally random letters delivers the largest amount of information. It offers no redundancy and any attempt at compression will immediately result in an irreversible loss of content. Abraham Moles combined these notions with findings from perceptual psychology in order to analyze the arts. Using various statistical measures, Moles showed how musical works exist on a spectrum from "banal" to "novel" corresponding to the relative order and disorder they exhibit as information (Moles 1966) In addition, he demonstrated how various forms of media can be thought of as the union of aesthetic possibilities in a high dimension space. For example, considering only pitch, a musical instrument has a single dimension. Adding loudness adds a second independent dimension, and so the possible states map into a two dimensional space, that is, a plane. Given two such mutually independent instruments the entire media space requires four dimensions, that is, a hyperspace. Around the same time Max Bense emphasized the notion of analyzing media space in terms of information theory, and then used it as the basis for what he called "generative aesthetics" The system of generative aesthetics aims at a numerical and operational description of characteristics of aesthetic structures (which can be realized in a number of material g e n e r at i v e a rt t h e o ry 157 elements) which will, as abstract schemes, fall into the three categories of the formation principle, distribution principle and set principle. These can be manipulated and applied to an unordered set of elements, so as to produce what we perceive macro aesthetically as complex and orderly arrangements, and micro aesthetically as redundancies and information. (Bense 1971) Following Shannon, both Moles and Bense equated high degrees of order with simplicity, and high degrees of disorder (i.e. randomness) with complexity. However, as much as information theory has its place in the analysis of communication channels, it does not correspond very well with our experiential sense of complexity in the world generally, or in art specifically. To the extent it equates complexity with disorder, information theory breaks down as a general model of our experience. This is where contemporary complexity science has fashioned a response in the form of effective complexity. Effective complexity Considered as a system, a crystal is made of atoms arranged in a highly regular lattice forming planes and facets. What emerges at human scale is a high degree of order that is easy to describe and easy to predict; in this sense, crystals seem simple. Because of their highly ordered nature any one crystal seems quite similar to others. By comparison, molecules that make up the gas in our atmosphere could not be more different. As a system, gas molecules are in constant motion, each with a random direction and momentum, and they are all constantly bouncing off each other without any discernible structure at all. Nevertheless we experience gasses as simple systems at human scale. Gas is easy to describe and predict, and a cubic foot of air in one place seems no different than a cubic foot of air in another. Things we think of as complex systems defy simple description and easy prediction. Many would agree that the most complex systems we encounter are other living things. Life requires a mix of order and disorder: order to maintain integrity and survival; and disorder to allow flexibility and adaptation. It was this kind of intuition that led physicists Murray Gell Mann and Seth Lloyd to suggest the notion of effective complexity (Gell Mann 1995) As illustrated in Figure 5.1, Shannon's information complexity increases with disorder, but effective complexity peaks where there is a mix of order and disorder. To underscore the contrast, where Shannon would consider white noise or a display of random pixels as being highly complex, Gell Mann and Lloyd would likely point out that all white noise sounds alike and all displays of random pixels look alike, and as such we perceive them as having low complexity. Effective complexity as a framework for generative art Effective complexity introduces a paradigm where high degrees of order and disorder both create simple systems, and complex systems exhibit a mixture of both order and disorder (Figure 5.2) Given this understanding, we can classify forms of generative art as simp