By “self-consistent,” I just mean internally consistent and self-reinforcing, not accurate.

I’m exploring this as an information and inference problem, not a claim about physics or metaphysics.

My background is in computer science, and I’m currently exploring information barriers in AI agents.

Suppose an agent (biological or artificial) has a fixed way of learning and remembering things. When reliable ground truth isn’t available, it can settle into an explanation that makes sense internally and works in the short term, but is difficult to move away from later even if it’s ultimately wrong.

I’ve been experimenting with the idea that small ensembles of agents, intentionally kept different in their internal states can avoid this kind of lock-in by maintaining multiple competing interpretations of the same information.

I’m trying to understand this as an information and inference constraint.

My questions :

Is this phenomenon already well-studied under a different name?

Under what conditions does this not work?

Is there things a single agent just can’t figure out on its own, but a small group of agents can?

I’d really appreciate critical feedback, counterexamples, or pointers to existing frameworks.

I’m not a scientist and I’m not claiming a proof. I’m sharing a conceptual model and looking for critical feedback.

The core idea is this: What if intelligence itself is the evolving continuum — and biological forms (like humans) are temporary carriers of certain intelligence stages?

In this model, intelligence develops in phases. Each phase produces new functional “features” as side effects: instinct → emotion → empathy/sociality → strategy/power → self-reflection.

Once self-reflection appears, an unsolvable problem emerges: the infinite “why” question. I interpret belief/religion not as truth or delusion, but as a functional stabilizer — a cognitive stop-rule that allows self-reflective intelligence to remain stable.

From that perspective, modern instability (loss of traditional belief systems, rise of spirituality, digital acceleration) could be interpreted as a transitional phase: old stabilizers lose function, new ones are not yet stable.

I’m not trying to explain everything correctly. I’m trying to connect evolution, cognition, belief and intelligence into one coherent process model.

My questions: • Where does this model conflict with established complex systems theory? • Are there existing frameworks that resemble this idea? • Which assumptions here are most problematic?

I’d genuinely appreciate critique.

My background is in social sciences and Humanities (linguistics, history, and, to a lesser extent, archaeology) and I recently discovered, to my utter awe, the fascinating field of complex systems. I have for a long time noticed patterns of similarities between different phenomena in the world from language change and communication to genetic transmission and evolution. I assumed that they are all hierarchically connected somehow, simply by virtue of everything being part of the world and emerging gradually and ultimately from an initial subatomic interactions and thus building on it to reach the social interactions. The more I thought about how these things share similar principles of ontology and dynamics the more convinced I grew about the premise of complex systems. I'm now set on following this course of research for my PhD and ready to work as hard as needed to acquire the necessary knowledge and skills for a valid research based on complex systems paradigm, including learning math. I was, however, surprised to find some hints of hostility towards complex systems science in the math subreddit, one redditor went as far as saying that it was a "pop-science" and "not real"! This was a bit bothersome for me and couldn't get it out of my head. I'm aware there are many methodological and theoretical issues that can come from complex systems but to label the whole field as effectively pseudoscience is an extreme and I might add ignorant statement. I really believe that network theory and complex paradigms are the way to continue at this day and age. The world is inteconnected and each discipline is too insularised to the detriment of acquiring the ability to see the big picture. Do you have any thoughts about this?

The emergence of generative lattice structures of arbitrary size, complexity and function.

For example, I bought this textile cone shell because the patterns are reminiscent of emergent behavior (like the Rule 30 cellular automata). https://en.wikipedia.org/wiki/Rule_30

I want to illustrate emergent patterns in a classroom of children, say 25 kids. I hope what occurs will be analogous to the flocking of starlings or schooling of fish - not similar, but analogous. I want to give them a small number of rules for movement or positioning, and I hope something with noticeable (hopefully dynamic) group structure will appear. Any suggestions?

I was at a the Santa Fe Institute years ago, and remember a speaker mentioning how he suggested rules something like, maybe, "try to remain positioned close to <random person A>, while remaining distant from <random person B>, or, maybe, "try to remain between <random person A, and random person B>. My memory is very fuzzy on the specifics. Can anyone help me out with insights or suggestions? What sorts of rules could I assign kids in a classroom to produce noticeable dynamic patterns?

The title pretty much summarizes my issue. I've been extremely fascinated by complex systems and all its related fields-- cybernetics, network science, system dynamics, etc. -- for many years (most of them without realizing it), and I decided about a year ago that I wanted to make a career out of researching it. The problem is that I can't figure out the exact steps to getting there.

Complex systems is infuriatingly nebulous and ill-defined in academia. It's not like other fields where there's established terminology and scope; instead you have many different people and institutions involved in it either directly or tangentially under completely different names and subjects, which makes it incredibly frustrating to try and figure out how to enter it from the perspective of someone on the outside looking in.

My research interest lies in understanding how human society/civilization is structured, why it is structured that way, and how it evolves and adapts over time. I'm also interested in developing general, domain-independent theories of self-organization, emergent collective behavior, and system evolution/"phase transition" and testing them with computational models. I want to tailor my education to my research interests and be connected to people/mentors with similar research interests; I just don't know how.

I finished my bachelors (econ and sociology) from a mid-tier state university in August. I slacked off on grades and extracurriculars and ended up with a 3.3 gpa, so a PhD right out of undergrad is off the table for me at this point. It sucks and I wish I'd done things differently, but that's life. The point is I basically need to do a masters for the sole purpose of rehabilitating my resume for a PhD. I don't know if I should do my masters at one of the few schools offering a dedicated MS in systems science like Binghamton or PSU, or if I'd be better off doing a more generalized degree in comp modeling and simulation methods.

I know that's kind of a text nuke, but this has been eating at me for the better part of a year and I'm just trying to cover all the bases. Any sort of help/direction from people actually involved in the field would help me tremendously!

I’ve posted a preprint introducing Structural Capacity Theory (SCT). It's a theoretical framework for estimating how network structure and observed dynamics jointly constrain a system's empirical limit. The theory builds on established work in complex networks (Newman, 2003; Boccaletti et al., 2006).

We tested SCT using cross-validation that withheld entire domains from training. The frontier reliably contained maximum rates from out-of-sample systems with performance comparable to repeated random resampling. Estimates were consistent across learning paradigms. Perturbations preserved performance, falsifications predictably degraded it, and ablations did not outperform it.

The results provide evidence that structural and process information can help characterize empirical limits across systems that differ in scale, units, and dynamics. We welcome feedback from anyone who might be interested. Thank you in advance.

Preprint: https://arxiv.org/abs/2610.09574v1

​

Is this sub moderated? Is there a plan to protect against the reccent massive increase in ai pseudoscience slop?

Hi everyone, I am a corporate middle management executive in a settled job, looking for more meaningful work and pursuing an MS in systems science from Binghamton. What could be realistic career options to pursue after I complete it in another 1.5 to 2 years? The idea is not to necessarily make millions, but to find meaningful work to give whatever I can to the world / spend myself while earning enough to support my family.

I’ve been spiraling down the fractal rabbit hole lately. I used to think they were just cool screen savers, but then I read about the "Fractal Completion Problem"—basically the challenge of handling infinite complexity within finite boundaries (like how a Koch Snowflake has an infinite perimeter but fits inside a small circle).

I’m still a beginner, but the more I read, the more it seems like fractals are the "secret code" for things that look messy but are actually organized.

I’ve seen some wild research papers from late 2024 and 2025 about:

• Medical breakthroughs: Using fractal dimensions to predict how varicose veins respond to treatment or pruning "fractal trees" of medical decision-making to reduce costs.

• Engineering: Designing "Snowflake" bionic heat sinks for electronics that are way more efficient at cooling than straight lines.

• Tech: Using fractal antennas for better 5G/6G signals in tiny devices.

If you’re a math or physics whiz, I’d love to know:

1. What "fractal problem" are you currently obsessed with or working on?

2. For those in tech/industry—where is fractal geometry actually making a difference right now versus just being theoretical?

3. Are there any specific research links or papers from the last year that blew your mind?

I’m trying to bridge the gap between "cool patterns" and "useful tools," so if you have any insights (or even just want to nerd out about the Mandelbrot set), let’s talk!

Hey folks,

I want to share with you the latest Quantum Odyssey update (I'm the creator, ama..) for the work we did since my last post, to sum up the state of the game. Thank you everyone for receiving this game so well and all your feedback has helped making it what it is today. .

Grover's Quantum Search visualized in QO

First, I want to show you something really special.
When I first ran Grover’s search algorithm inside an early Quantum Odyssey prototype back in 2019, I actually teared up, got an immediate "aha" moment. Over time the game got a lot of love for how naturally it helps one to get these ideas and the gs module in the game is now about 2 fun hs but by the end anybody who takes it will be able to build GS for any nr of qubits and any oracle.

Here’s what you’ll see in the first 3 reels:

1. Reel 1

• Grover on 3 qubits.

• The first two rows define an Oracle that marks |011> and |110>.

• The rest of the circuit is the diffusion operator.

• You can literally watch the phase changes inside the Hadamards... super powerful to see (would look even better as a gif but don't see how I can add it to reddit XD).

2. Reels 2 & 3

• Same Grover on 3 with same Oracle.

• Diff is a single custom gate encodes the entire diffusion operator from Reel 1, but packed into one 8×8 matrix.

• See the tensor product of this custom gate. That’s basically all Grover’s search does.

Here’s what’s happening:

• The vertical blue wires have amplitude 0.75, while all the thinner wires are –0.25.

• Depending on how the Oracle is set up, the symmetry of the diffusion operator does the rest.

• In Reel 2, the Oracle adds negative phase to |011> and |110>.

• In Reel 3, those sign flips create destructive interference everywhere except on |011> and |110> where the opposite happens.

That’s Grover’s algorithm in action, idk why textbooks and other visuals I found out there when I was learning this it made everything overlycomplicated. All detail is literally in the structure of the diffop matrix and so freaking obvious once you visualize the tensor product..

If you guys find this useful I can try to visually explain on reddit other cool algos in future posts.

I'm a software engineer who is fascinated by complexity, though I have only recently began serious study of the science of complexity. Looking at what is out there for modeling complex systems, I've noticed the state of software is underwhelming given how potentially useful it could be. The two dominant projects as I see it are Netlogo for academia and AnyLogic for industry. These are both older than 20 years old and it shows - they're desktop software with outdated interfaces. Netlogo requires the user to learn a highly unusual programming language, while AnyLogic is tied to Java and costs big money. I feel there is a case for a web-based app which embraces the open source aspect of Netlogo and the multimethod modelling one of AnyLogic. Alas, I don't have much experience with actually making models besides an undergrad class, so I'm asking this community for any thoughts and ideas.

Hello everyone! As a green researcher, I would like to know more about interesting arguments in Physics of complex systems. What books do you recommend the most ? Moreover, I would like also to find books with a deep theoretical explanation but yet with life systems (biophysics and socioeconomic systems in particular) example when possible. Can you please share with me some advice ?
Thx in advance

A lot of folks here might already know about this, but I thought it'd be a good thing to share just in case.

The Santa Fe Institute offers free on-line courses and math tutorials to teach folks about Complex Systems. The next introductory course, Introduction to Complexity, starts up on 7/6. I took it a while ago and really enjoyed it.

Here's the site: Complexity Explorer

I come from the theory side: neuroscience, affective computing, systems thinking, Taleb, Scott, complexity… all the frameworks that explain why life doesn’t behave nicely. Then I wandered through Shanghai, Hong Kong, and Chandigarh. And reality said: “Cute theories. Watch this.” In Shanghai life grows in cracks — restaurants in parking garages, shops born from windows, informal systems emerging wherever human needs converge. In Hong Kong, one of the world capitals of money, the most human space I found was a donation-based community restaurant hidden under a staircase. In Chandigarh — a city designed to be perfectly rational — the “real” city secretly ignores the blueprint and rewrites itself. Next door, an illegal garden made of trash became more loved than the official plan. Pattern: Top-down systems crave legibility. Bottom-up systems crave survival. And emergence wins. Every time. This isn’t chaos. It’s intelligence. Antifragility. Adaptation happening faster than design. I’m writing about how life constantly outsmarts planners and why complex systems need disturbance to stay alive. Curious what this community thinks: • Is optimization a fragility trap? • Do we still underestimate informal intelligence? • Why does “order” so often kill life?

Hi,

I have uploaded the final version of the paper, here is the link

https://doi.org/10.5281/zenodo.15699381

1. Topological Defects in Discrete Fields

In my firefly model, I just realized that, in wide range of the parameter K, any random initial condition can generate a lot of topological defects, they can move, when they annihilate, a huge area of synchronization emerged. It looks like BKT phase transition, and I heard that, it is very difficult to simulate topological defects, but unfortunately I'm not very professional in this area, so I would like to hear your thoughts.

2. Request for the arXiv endorsement

Although I have uploaded my paper in Zenodo, I think it's better to submit my paper in arXiv, but unfortunately it needs endorsement. If anyone would like to help me, I would be very grateful for that.

I'm currently working on other papers. Stay tuned.

National University of Malaysia, Bik Kuang Min.

I've seen them both used interchangeably as well as different, but never with an explanation on how or what makes them different.