1. This may be a pretty obvious and elementary take on the question of effective complexity, but cliches are cliches for a reason… If, in grade school, you ever did the wintertime craft of folding paper and cutting out pieces to make a snowflake, you might remember that every single one came out looking completely different and unique. Hell, I still do it sometimes for fun when I’m bored, and I’m always entertained by the outcome. I think that’s a good indication that it’s effectively complex.

Since it’s human made and not generated, I do think it sits more toward order than random, especially considering that you can probably try and recreate a pattern by cutting a new one out in the same way you previously did. But, if you are trying to create different ones every time, you will!

And, for the age of technology, there is also a Tik Tok filter where you can make a snowflake pattern using your nose, and it’ll unfold into something unique! Here’s an example of the filter in use.

2. A lot of the problems that are brought up regarding generative art really make me think… especially “The Problem of Authenticity.” This is one I’ve seen debated quite a bit: “Given that it is in part created by an unemotional and unthinking system, is generative art really art at all?”  My opinion on this subject also takes into account “The Problem of Locality, Code, and Malleability”: “Is the art in the object, the system, the code, or something else entirely?”

I believe that the person coding it and the machine making it are partners in  the artwork. Of course it’s art! The machine wouldn’t be making that art unless it was told to make that art by an artist who had an idea, a plan. And of course, the artist wouldn’t have any art to show if the machine didn’t carry out their instructions. Any time I code something generative, I have a vision, and I add in the randomness that makes it generative. The machine carrying it out every time is my medium.


Question 1A:

I consider the hair accumulated in my shower an example of effective complexity. It’s pretty much total randomness in that you don’t really know how much hair is in there, the shape is totally random. But it has certain expected features (it’s made out of hair, is denser in the middle, has less hair on the outer part, etc).




Question 1B: Problem of Uniqueness

My problem is actually about the question itself: “Does it diminish the value of the art when unique objects can be mass-produced?” I think value really shouldn’t be at the center of the discussion with generative art. For me, value comes from exclusivity, ownership, and scarcity, and a big part of generative art is about its ability to dismantle such things in art. With generative art, we shouldn’t be looking at the value of each individual mass-produced unique object/works; the focus should be the mere realization/appreciation that mass-produced uniqueness is made possible through generative approach.



Interestingly, I would argue that Effective Complexity is fully determined by human perception.

Mathematical Equations, to some people, are mostly chaos since the change of numbers in the equation would not invoke psychological change to them because they are not interested in extracting information from a blackboard of non-sense. To them, the image above reflects low effective complexity. To mathematicians, however, each bit of information is important and thus the image above reflects high effective complexity.

My Personal Note

“Generative Art Theory” talks about generative art as repeating execution of rule-based art, which incorporates many ancient generative arts not executed by computers.

“Generative Art Theory” by Philip Galanter discussed Effective Complexity. The intuition lies within although the trajectory of individual gas molecules is not predictable, the overall effect of gas property is well known only with little random error. But this is just an intuition, is there a way to find a mathematical definition for Effective Complexity? If we can systematically quantify such metrics, the next generation GAN could be optimized to achieve high Effective Complexity! (Given that the metrics is computable and well defined, we can have genetic algorithm do the generation. It doesn’t have to be differentiable)

  • The information theory counts every detail in the system as bits of information, but the human perception clearly does not.
  • Is the Effective Complexity only exist given human perception or is it more fundamental?
  • One way to model a complex system is to use statistical tools like discerning the mean and standard deviation. Two gas systems with different information will still have similar mean and standard deviation which aligns with human perception.
The article suggest that extremely low or high information complexity gives low effective complexity
The article suggest that extremely low or high information complexity gives low effective complexity
Reaction-Diffusion System: simulation of chemical reactions that produce its own catalysts with changing diffusion rate
Reaction-Diffusion System: simulation of chemical reactions that produce its own catalysts with changing diffusion rate


My opinion about The Problem of Authorship: by defining generative art as repeating execution of rule-based art, all information is, therefore, encodable and can be represented by the rules themselves. If the final products follow exactly as the rules describe, then the final product, as a reflection of the rule, does not add additional meaning to the work. In this case, the authorship should fully belong to who wrote the rules. However, in the case of random number generation (especially for pseudo-random numbers), decisions (on which random number to use) are made by the computer, not the artist. Say, you wrote a program that uses total randomness to generate 100px by 100px images. Most of the time the resulting image is an image of white noise. However, it is still possible for the computer to generate something meaningful by small chance. This problem is magnified with artwork that involves latent space (typically in GANs) as this probability becomes larger. A computer can discover interesting random input to the latent space to “discover” an interesting artwork. At this point, we should attribute some authorship to that computer in choosing the right input. The “amount” of authorship we attribute to the executor should be proportional to the search space. This link to computational complexity is intuitive: as search space shrinks, the rule becomes more restrictive, and therefore more percentage of authorship should be rewarded to the rule-writer instead of the executor. In summary, for computer-generated art with uncertainties, I think the authorship should be split to both the rule-writer and the executor based on how restrictive the rule is.

Table of Content




Question 1A: Fried eggs exhibit effective complexity, and I believe they sit in the middle of total order and total randomness. They have orders in the way they are cooked–“removed from their shells and placed into a frying pan and fry with minimal accompaniment” (from Wikipedia)–but the shape of each fried egg has certain randomness.

Question 1B: I believe the authorship of digital generative artworks belongs to humans. From what I see in digital generative artworks, it is humans who write the original code and make the final decision on the result generated by machines. Computers and other machines are only tools used by human artists. Besides, assigning authorship to machines will lead to legal issues when it comes to copyright protection.


Question 1A. The spotted lake in Canada exhibits effective complexity as its appearance falls under total randomness but its creation stems from order. The lake becomes spotted when the water level is low, and the walkways are caused by high deposits of minerals like magnesium sulfate, calcium, and sodium sulfate. Each of the pools has a unique hue, which is also due to the dissolving of minerals. I would say that the spotted lake falls in between order and disorder. The science behind the spots are caused by minerals and crystals, but the patterns they create on a macro scale look random and disordered. The complexity of the system is unknown to me since I am unsure of the specifics as to why these circular patterns form. There is not much research that describes this. I assume that many complex factors cause the formations, but it could be attributed to a simple scientific phenomenon regarding mineral deposition.

Question 1B. The Problem of Uniqueness
I think that the issue with uniqueness in digital generative art and digital art in general does diminish the “aura” and uniqueness of the piece. Once exclusivity is diminished, the appeal of uniqueness and something being special because of scarcity also decreases. This is not necessarily the most important aspect to an artwork as there are many other factors that make art “good art” that don’t rely on uniqueness. Although digital generative art does create a unique and original artifact rather than copies of digital work, the amount of uniqueness of the artwork is limited. Usually some small parameters are changed, some things are moved around, and colors or movement are altered, but the basic premise is the same. The limited range of the uniqueness of digital generative art makes the original and unique generated artifacts almost the same as copies of an artwork. I think that uniqueness is indeed an issue with digital generative art, but in our increasingly digital world, it is not a large issue.


Jorge Luis Borges’ Library of Babel (which inspired the project by Jonathan Basile, pictured below) has stuck in my mind as an example of effective complexity. His concept of the generated book leverages the order, rules, and familiarity of books as pages of paragraphs, sentences, and words made up of letters and spaces while imposing additional constraints that each book has 410 pages, 40 lines per page, and ~80 letters per line – which teasingly seems to promise meaning. He introduces disorder through the characters in the book being random. What I find compelling about his concept is that it appears to follow total order in terms of meaning (taking form in the book-medium that is typically equivalent with meaning/knowledge) but in actuality is total randomness: a white noise of letters and spaces. It also illustrates the “10,000 Bowls of Oatmeal” problem well in that the resulting books are so random that they are meaningless and effectively indiscernable from each other. The story that plays out the concept is predicated on that exact false promise of meaning.

(sorry that was > 50 words)

The “Problem of Meaning” with generative art is especially challenging to me. From what I see of the generative art world, much of it tends towards being “unapologetically abstract and formal in [its] generative practice, seeking only to reinvigorate the sublime and instill a sense of awe” (173) which in a sense is appealing to me as a humble goal that demonstrates and celebrates beauty in its “truth to process” or truth to system. At the same time, I can’t help but look at all the generative art on Twitter or sold as NFTs and feel some skepticism and clash with my (debilitating) idealism: it shouldn’t be so “easy” to capitalize on generative art, it feeds a little too seamlessly into fast-consumption platforms, and at worst falls short at being visual eye candy.


The game Noita has a relatively low effective complexity. The game is a single physical map but it is separated into different areas that follow different sets of rules. Though every pixel in the game is simulated and can have an enormous quantity of physical effects applied to it, little of that can happen without some sort of direct or indirect action from the player. Therefore the environment itself, while topographically randomized is still relatively consistent from one generation to the next.


The Problem of Creativity:

I think that generative art pieces are absolutely creative. Far more so than much of photography in my opinion. With generative art pieces, you have to start with a completely blank slate and come up with something entirely new, while with much of photography you are already given the infinity varied masterpiece that is the world at large. Even if you are not drawing the art itself, you are still responsible for the machine, the virtual robot, that creates that art and you have an intent for what that art should look like. Therefore, generative art pieces are creative as without someone with vision to create the piece, there would be nothing to look at.


Question 1A: Clouds exhibit effective complexity. They are created by water vapor that has turned into liquid water droplets. While they are formed by the same molecules, they appear random in the sky and are influenced by factors such as temporature and altitude. Hence, there is order in how they are formed and but randomness in their formations.

Question 1B: I have always felt conflicted about the problem of authorship when it comes to generative art. When I was learning about generative real-time visuals for the first time using Touchdesigner, I felt I had little control over what the real-time visuals would look like and since I was a beginner, I would just play around with different parameters and functions and end up becoming pleasantly surprised by the product of my “creation”. Looking back at it, I would say the machine was more of a creator than myself because I had no control over the end product. However, now that I have more experience with the application, I can effectively create my ideas instead of letting the computer determine them. In this case, I believe I have then gained authorship over the work I create because the computer has become a medium, just as paint on a canvas, to help me create my art.

DrMario – Reading03

Question 1A:

Something that exhibits effective complexity is the procedural Minecraft landscape, I’d say its on the left hand side of the graph shown on page 13. It’s quite random but at the same time there are rules that it must follow so you can almost guess what it will somewhat look like.


Question 1B: I have a conflict with the problem of intent. I understand that making a computer do the work for you instead of specifically hand crafting it to be your perfect piece draws away the intent of the piece, but in some cases, like in Dead Cells, the computer generated landscape adds to the piece and the re-playability of the game which goes great with the Roguelike Genre. I think that generative art can have just as much intent as normal art as long as it fits in with the genre and ideas behind the piece.