(I hope to be clear)

I am applying information theory metrics to the problem of establishing the geographical origin of archaeological objects. I trained a random forest model to do so and calculated the Shannon entropy on the vector of predicted probabilities (3 possible origins or classes) to assess the uncertainty of the results. The results are promising, however, the entropy bias says that the true entropy of a process is underestimated when calculated on the probabilities of a small sample. That is, when applied to a small set of objects, the observed entropy is lower than the actual entropy. However, when comparing sites with many objects and sites with few objects, the latter always have a higher median entropy. I did spearman's test to see if there is any correlation and the result is -0.7 p_0.028, so correlation is significant.

Does my reasoning makes any sense?

I’m exploring a framework I call Active Graphs, which models life and knowledge as a dynamic, evolving web of relationships, rather than as a linear progression.

At its core, it focuses on:

•	Nodes: Representing entities or ideas.

•	Edges: Representing relationships, shaped and expanded by interaction.

•	Purpose: Acting as the medium through which ideas propagate without resistance, akin to how waves transcend amplification in space.

This isn’t just a theoretical construct; it’s an experiment in real time.

By sharing my thoughts as nodes (like this post) and interacting with others’ perspectives (edges), I’m creating a living map of interconnected ideas.

The system evolves with each interaction, revealing emergent patterns.

Here’s my question for this community:

Can frameworks like this, based on dynamic relationships and feedback, help us better understand and map the complexity inherent in scientific knowledge?

I’m particularly interested in how purpose and context might act as forces to unify disparate domains of knowledge, creating a mosaic rather than isolated fragments.

I’d love to hear your thoughts—whether it’s a critique, a refinement, or an entirely new edge to explore!

Despite all the new techs, policies, and investment, planetary indicators continue to decline (warming, extinctions, pollution, etc). How can we be most effective in actually improving?

As title says, I’m looking for books/articles to deepen my understanding of complex systems. My background is in behavioural sciences. Would appreciate any recommendations.

I’m considering getting myself a copy of this book:

Complex Systems in the Social and Behavioral Sciences by Douglas Kiel and Euel Elliot.

Anyone able to tell me if this is a good read or not? Thanks!

I'm kinda new to systems theory, so I'd like to know if anyone could please recommend some texts or papers that discuss the concept of operationality, how it is defined and whether it is closed or open in their views. Thanks in advance!

I recently came across the concept of complex systems and was wondering if it is useful in robotics? Is multi-agent, swarm, behavioural robotics an application of complex systems or am i misinterpreting it? How useful is learning complex systems for robotics i.e. if you want to get a job or maybe work in academia (how useful is it in academia vs industry) ?

P. S. Complete noob here, any insights greatly appreciated.

I remember when I created the sub many years ago — as someone who received their PhD in complex adaptive systems 13 years ago and took their first graduate classes in complexity science 20(!) years ago, it’s extremely gratifying to see the concepts I fell in love with really begin to catch on.

Keep spreading the good word - let’s accelerate the reversion of entropy :)

Hey everyone,

I’ve been curious about the idea of thresholds or tipping points in different types of systems. It seems like many systems—whether physical, biological, ecological, or even social—have some kind of critical threshold where they undergo a major change or breakdown. For example, I know there are population limits in ecosystems, boiling points in physical systems, and carrying capacities in logistics or supply chains.

I’m wondering if this idea of a “threshold” is something that’s been explored as a universal principle. Has anyone come across research, theories, or patterns that suggest these thresholds operate similarly across different fields? Or is this just a superficial similarity without a deeper connection?

Would love to hear your thoughts or get recommendations for reading material if anyone’s come across work that explores thresholds in a cross-disciplinary way.

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 poorly-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 have to read chapter three for homework tomorrow and im so confused if anyone has read this and can help me in the slightest so i can participate in class tomorrow it would be so great, but if you havent read it you should, its a great introduction to thinking about the world through a different lens

I have a BA in urban planning currently. I also have interests in the environment, such as permaculture. I am open to getting a certificate in ecology or complexity science.

If I could go back in time I would have taken more science/math classes in college, but I am not sure I want to focus on these if I were to go to graduate school because I feel I do not have an undergraduate foundation. So, I am more interested in the ecological area of complex systems theory.

What are some career paths that I could look into? It could also be fun to be some kind of research assistant, but as I am not currently in school I am not sure how I would do this.

Hey, I'm working on a second bachelor's graduation project and this is not my main focus but one of the premises in my theoretical framework is that social media networks (social and semantic interactions) are more complex, change more rapidly, as they scale up their complexity increases (the opposite of broadcast media), and thus are more difficult to perceptually visualise or orientate oneself within about where sources are really coming from sociopolitically, how they're associated and distributed, their histories etc.

It seems like a fairly obvious practical and scientific desiderata to have a metric to directly compare their network structures, dynamics and complexity, and to map the networks of them in a cumulative but updating way, but surprisingly I couldn't find anything like that yet.

I tried doing a quick (unreliable) comparison using chatgpt to estimate assembly indices but unexpectedly they overlapped and I can't check the process closely enough to validate it. Also tried applying Yaneer Ban-Yam's concept of complexity profile, which is basically complexity over scale, but that only gives me 'high/low' 'increasing/decreasing', and omits time.

I tried searching with sociophysics and computational communication or social science. Still nothing comparing their complexities across topics and over time. Plenty of good visualisation tools and some sample datasets of social networks on social media, but those don't really answer my question.

Anyone know where to look? Or does it really not exist yet in the public domain?

TIA!

Hi, everyone. I was wondering what the most useful paradigm is relative to Assembly Theory. I found out about AT itself through this newly published book "Life as no one knows it". I found out that most ideas in the book have already been formulated in some way or another under different paradigms ( computational, biological, logical). As someone interested in the structures of systems (more specifically on the "ordered complexity of the whole" as formulated by Leonard R. Bachman), could you point me toward the most useful paradigm in going about this subject?

Appreciate it.

I've fallen upon bio-subsidiarity as a good political system that could best manage complex systems.

Combined with an iterative form of governance, i.e. assess, plan, implement, asses and repeat; No quantitative goals, no allowing for path dependencies.

What do you guys think?

I've read Stuart Kauffman's A World Beyond Physics and Alicia Juarrero's Context is Everything, but there is much that I don't fully understand.

I get many of the basic ideas, such as:

• The vital role of context.
• Kauffman's useful idea of the "adjacent possible".
• The impossibility of predicting the "phase space" or emergence.
• The consequent impossibility of predicting how novel structures will emerge.
• Kauffman's distinction between causal factors and enabling factors.
• Juarreror's related concept of enabling constraints.
• Why these complexities make it impossible to model emergence with a set of differential equations and their boundary conditions.
• The unstated (yet implicit) linearity in the Cartesian & Newtonian models of the world.

But then there are many things that are completely opaque to me. So, for instance, while listening to an video overview of Complex Adaptive Systems published by Systems Innovators, I heard that complexity theorists believe that the essence of order is actually invariance under certain transformations, and the notion of invariance is, in turn, ultimately based on the notion of symmetry. They offered no explanation of these statements. Now, that clearly refers to a whole body of scientific study with which I am unfamiliar, and I am having trouble finding tutorial material that explains it. I found a book on CAS by a fellow named Gros who talks about symmetry & invariance under "scaling" and I find some mathematical notation in the discussion of these topics but it also appears to assume a lot of prior familiarity.

I want to understand the mathematics of complexity, especially CAS, but I need some good introductory material. I have a bachelors in physics and masters in Computer Science, but zero prior exposure to complexity, except for coursework we did on mathematical grammar in my graduate program which may have some relevance.

I have spent some time reading:
Gregory Chaitin on Omega &
Stephen Wolfram on cellular automata
Jeffrey Campbell on Information Theory

I have Signals & Boundaries and Hidden Order by John H. Holland but have not yet read these works.

Are there any on-line courses that my help me understand Juarrero better and / or help me understand the mathematics of complexity better?

MIT does not appear to have a course in CAS. The Udemy course appears to be superficial, but I could be wrong about that. The Coursera course seems to be all over the map. I'm mostly interested in CAS for philosophical reasons, and less so for its engineering applications. I am an engineer, but I am also retired.

Any help or advice would be much appreciated.

I’ve been listening to the David Krakauer episode of Seam Carrol’s Mindscape. David argues there, without much depth, or at least not in ways I understand, that a Hurricane is not an example of a complex system. This, despite it being a nearly canonical example of a complex system throughout texts/literature etc.

Anyone with the same view that could try to explain this view?

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?

Hi!

I recently learned of the Graduate Program in Complexity Science from the Complexity Science Hub in Vienna and wanted to ask you whether you would recommend the hub, as well as whether there is any specific program you would like to recommend.

I am interested in analyzing social media, as well as markets from a network science approach, but I am quite new to the idea of complexity specific programs. While I am still considering some PhD programs, I'd rather do mine in Europe (+ I already hold 2 master's degrees)

At the moment I have been considering applying to:

• Some Computer Science programs (including Aalto's, which I have read is usually recommended)
• Oxford's DPhil Social Data Science (mostly due to the institute of new economic thinking and their economic networks summer school)
• Complexity Science Hub in Vienna (it looks very interesting, but I am afraid that it might be too new)

Thank you in advance!

Hi everyone! I have a very especific question.

I am in the penultimate year of my physics degree and I want to do my final work on sociophysis. At my university there are two professors who do this, one of them is specialized in stochastic processes and hierarchically organized structures and the other one is specialized in complex networks. I have to decide with whom to do my final paper and I don't know what to decide. Does anyone know what is the most used in sociophysics and what I should study? I am particularly interested in studying econophysics.

NetWorks is a music-generating algorithm, based on complex systems science, that seeks to tap into the ceaseless creativity, and organic coherence, found in nature through fine-tuning the connectivity of networks, which channels how information flows through them, and the rules that transform the information as it interacts via their nodes.

Constraints on the connections and interactions between the parts of systems are central to their coherence. Alicia Juarrero in her book, Context Changes Everything writes: “Coherence-making by constraints takes place in physical and biological complex systems small and large, from Bénard cells to human organizations and institutions, from family units to entire cultures. Entities and events in economic and ecosystems are defined by such covarying relations generated by enabling constraints.”

In NetWorks, the transformation of information via the nodes is extremely simple, nodes send and receive simple values (negative and positive integers) that are added/subtracted together. 

Michael Levin, in his groundbreaking work on developmental bioelectricity, points out the important ability for cells to coarse grain their inputs. Cells track and respond to voltage and, as a general rule, are not concerned with the details, specifically, the individual ions, ion channels or molecules, that contributed to their voltage. It is the voltage patterns across cells which control cellular differentiation during morphogenesis and ontogeny.

In discussing the role of the observer, Stepen Wolfram points out the importance of equivalence in human thought and technology. He uses gas molecules and a piston as an example: the huge number of possible configurations of the gas is not important so long as they are equivalent in determining pressure. All that matters is the aggregate of all the molecular impacts. Equivalence is a key aspect on how we as observers make sense of the world, in that many different configurations of systems contribute to their aggregate features that we recognize while we, like our cells, can ignore most of the underlying details. 

Similarly, in the NetWork algorithm, nodes aggregate their inputs which are feedback into the network through their links. It is the network’s unfolding pattern of values that are sonified.  

The pieces in NetWorks 11: Unfamiliar Order consist of eight interacting voices. Voices can interact such that, for example, the depth of vibrato performed by one voice can influence the timbral characteristics and movement through 3D (ambisonic) space of a note played by another voice. The covarying relationship between musical attributes result in expressive context dependent performances. 

Headphone listening is recommended as the piece was mixed using ambisonic techniques.

https://shawnbell.bandcamp.com/album/unfamiliar-order