Question 1A. Effective complexity is defined as a measure of complexity– in this context– in terms of generative art. Galanter classifies generative art as being a rejection of simple description and easy prediction, and as lying somewhere on a spectrum between highly-ordered and highly-disordered. The concept works to quantify the level of randomness vs. instructed nature of some sort of event. A good example of a system that lies somewhere in the middle of the spectrum is the stock market. There are a lot of factors that influence it–overseas markets, general economic data –making a controlled instructed system; however a lot of stock market analysts work with prediction and random outside occurrences, so much of stock-trading boils down to chance and randomness.
Question 1B. The biggest issue I take with generative art is, as most art should do, its expansion and challenging of the definition of art as whole. Art seemed for the longest time to be limited to human-made materials, objects, and ideas come to fruition. Yet generative art, as this article touches on, relies on a human-made or human-controlled robot/mechanism following a set of instructions in order to create the art itself. This in the article is addressed under the section The Problem of Authenticity. At the same time, I think many generative artworks, such as Harvey Moon’s drawing machines, force the audience to ponder on topics and consider possibilities they once may have not, which are characteristics of a successful art piece.