wk6 10.28/10.30: Emergence, Self-Organization, Augmented Reality, Virtual Reality | Methodology Guidelines

[xuegao]

This week, I would like to discuss Cellular Automata (CA) and how they demonstrate emergence and self-organization. CA can be described as a system made of manycells that follow three basic principles: 1.The cells exist on some kind of grid, 2. Each cell has a state, 3. A cell’s future state is determined by a rule that depends on the states of its neighbors.
A well-known example is Wolfram’s Elementary Cellular Automata, the simplest form of 1-dimensional CA. Each cell has only two states (0 or 1), and its update rule depends on three cells, the cell itself and its two neighbors. Because there are 2³ = 8 possible neighborhood configurations, the system has 2⁸ = 256 possible rules. Even with these minimal ingredients, the system can generate surprisingly rich global patterns, from simple repetition to complex, chaotic structures. This illustrates the idea of emergence: global behaviors arise from very simple local instructions.


All 256 elementary CA rules

In 2-dimensional CA, these ideas extend into a plane. Conway’s Game of Life is the classic example, where each cell is either alive or dead and updates based on the number of live neighbors in the 8-cell Moore neighborhood. Two simple rules—birth and survival—lead to dynamic behaviors such as oscillators, stable structures, and “spaceships” that appear to move across the grid. Again, these forms were not programmed into the system. They emerged from local interactions, showing how simple agents can self-organize into complex patterns.



Across these different models, Cellular Automata reveal how complexity does not need complex rules. Whether the system is a simple 1D binary CA or a continuous, radius-based 2D model like SmoothLife, the key phenomenon is the same:
local interactions accumulate into global order.
This makes CA a powerful way to study emergence, self-organization, and how simple rule-based processes can generate lifelike dynamics.

Reference: https://natureofcode.com/cellular-autom ... r-automata

[lpfreiburg]

Both *A New Kind of Science* (NKS) and the self-organization chapter from *Self-Organization in Biological Systems* pose the same key question: how can complex global order emerge without a central designer or blueprint? They approach this question from different angles. Camazine and his coauthors focus on physical and biological examples like sand dunes, convection cells, fish schools, ant trails, and termite mounds. They define self-organization as the emergence of global patterns from many components that follow simple local rules, using only local information. No ant, fish, or grain of sand knows the final pattern; the complexity comes from how simple behaviors interact and influence one another. In living systems, evolution adjusts these local rules so that the resulting patterns are functional, enhancing foraging, defense, or structural stability.

In contrast, Wolfram looks at self-organization in the abstract realm of simple programs, particularly cellular automata. He treats these rules as experimental objects: choose a rule, start with a random or straightforward initial condition, and see what happens. His main argument is that even straightforward rules can create unexpectedly rich behaviors—stable structures, nested patterns, and seemingly random textures—without any added complexity in the basic rule. With the Principle of Computational Equivalence, he claims that once a system reaches a certain point, its behavior is as computationally powerful as a universal computer and often computationally irreducible; there is no shortcut to predicting its actions other than running it step by step.
Despite the differences in style and focus, there is significant overlap between the two texts. Both highlight that local interactions, rather than top-down plans, create global order. Both take emergence seriously: the behavior of the whole cannot be understood solely from isolated components; it depends on their interactions. Both assert that complexity can arise from simple parts and rules; intricate patterns do not indicate a complex designer. The main difference lies in their emphasis. The biological account is mechanistic and empirical, examining how specific rules and constraints in ants, fish, or fluids lead to particular patterns and why those patterns are adaptive. NKS is more universal and philosophical, using simple programs to explore complexity itself and accepting that in many systems, "the computation is the explanation," even if that limits prediction and control.

When read together, these works provide complementary viewpoints. The self-organization chapter grounds the concept in real systems, illustrating how local rules, feedback, and evolution create the patterns we see in nature. Wolfram's NKS extends this idea, suggesting that complex, self-organizing behavior is a common trait of simple rule-based systems, not just a unique aspect of biology. For anyone interested in design, art, or technology, this combination is powerful. We can adopt biological strategies such as local rules, stigmergy, and feedback loops to create self-organizing systems, while also recognizing, as Wolfram notes, that at a certain point, we should expect these systems to behave in ways only fully understood through running and observing them, rather than trying to solve them on paper.

[italo]

I really liked the project Mappemonde by Nicolas Baier because they show how digital media art can move beyond the screen and become part of real, physical space. The way this work translates data and digital structures into something architectural and tangible feels especially elegant. It made me realize how powerfully media art can blend with architecture and design, and it’s something I would love to explore in my own work one day.



Another interesting work for me is Osmose (1995) by Char Davies. Even though it was created almost 30 years ago, it still feels incredibly advanced. It’s an immersive virtual-reality environment where you navigate using your breath and your balance, moving through poetic, nature-inspired digital spaces. Rather than simulating reality, Osmose focuses on perception, presence, and the relationship between the body and virtual space.



These two works show me different but connected ways of making digital art something that is felt physically and emotionally. This is the kind of direction I hope to pursue in my own projects, not losing the human experience in the digital realm.

[lucianparisi]

What interested me most this week was how different artists use VR and AR to shape how we inhabit images, not just what we see. I kept circling back to Char Davies’s Osmose, Jeffrey Shaw’s Golden Calf, and Chun Hua Catherine Dong’s AR/VR work.

Davies’s Osmose feels almost like the opposite of most commercial VR. Instead of hand controllers and shooting mechanics, navigation is based on breath and subtle shifts of balance. You literally float through semi-transparent trees and data-like strata by inhaling and exhaling. What I take from this is a very different model of immersion: VR as a somatic, slow, inward experience, where the headset amplifies awareness of your own body rather than erasing it.

Shaw’s Golden Calf approaches “virtual” from the other side, using AR to stage a kind of skeptical encounter with images. The physical setup is minimal but when you look through the device, a golden calf appears, only existing in the mediated view. I read this as a quiet joke about spectacle and belief: the sacred object is literally nothing without the technological frame.

Chun Hua Catherine Dong’s recent exhibitions add a more explicitly political layer. Her use of AR, VR and 3D-printed figures extends earlier performance and photography about diaspora, shame, and “saving face.” I’m interested in how she treats virtual avatars and overlays not as escapist identities, but as ways of negotiating cultural expectations: who is allowed to be visible, and under what conditions.

[felix_yuan]

Self-Organization in Biological Systems

The first chapter of Self-Organization in Biological Systems (https://assets.press.princeton.edu/chapters/s7104.pdf) gave a brief but clear discussion on the definition and properties of emergent system. In a self-organizing, or emergent system, the system is formed upon the relations and interactions of the inner part of the system, i.e. the subsystem, instead of following a given rule in a top-down structure. The top-down structure, is intuitively more familiar with humankind as we have been using this view all through out the history from drawing blueprints for architectures, writing outlines for stories to designing the management structure for corporations, the golden rule of top-down design plus bottom structure, to even trying to shape the structure of the society, which is actually a more self-organizing system. Such top-down view work effectively in serval scenarios but when it comes to a system with both top-down structure and emergent properties, or dominantly emergent, would potentially cause disasters. This is because even with simple rules or patterns of the interaction of the subsystems, as the system grows, it could create a very complex system. And for system like human society with the inner interactions that is already complex enough, and the result of the whole system would become nearly impossible for creating a simple top-down view for the system, which is essentially the reason human use top-down structure.

To understand the high complexity of emergent system, the book used biological system as an example. The two key factors contributing to complex biological system is the inner unit and the outer nature environment. As creatures have more capability than a smallest unit in physical system, for example a sand in the dune, the interactions happening in biological systems are inherently more complex.

The second factor is the nature’s outer force, the nature selection. As species evolve they adapt to the environmental change and creates new behaviors, and the they code these information inside the DNA and pass it to the next generation. So as time goes by the inner behaviors of the units become a result of the whole group’s adaption to the complex dynamic nature, and the unit’s behavior is now a reflection of the whole group, and they interact with it to form new group, which again pass the new group behavior to the next units. So this co-emergent between subsystem and system multiplies the speed of complexity growth, and finally resulted in the gigantic species group on the planet earth.

The emergent system provides such an interesting and sometimes surprisingly neglected view, and it would be amazing to view artwork not as just a form to realize the intent of artists, but as a system the artists build, and continues to evolve in a self-organizing way.

UCSB Library book source

(https://search.library.ucsb.edu/discove ... s&offset=0)


Self-organizing Artwork

Casey Reas

Casey Reas has been exploring self-organizing form as artworks for a long time. By applying simple rules, he created artwork that emerge from minimal behaviors and interactions of simple geometric shape.

In Path, 2001, he experimented with lines.

Then in process, the behaviors of shapes become more complex.

The design of interaction of process.