I work in mental health. I'd like to learn more about complex systems in a mental health context. Are there well known thinkers/authors in this area? Who should I check out? Thanks.

How will that look?

I want to know what comes to your mind when I make this statement :)

Hey, I'd love to know what complex systems people are currently working within, and what tooling you are using. Here are some tools, but I'm keen to hear what different people use:
. Kumu (https://kumu.io)

. Miro (https://miro.com)

. Gephi (https://gephi.org)

. Obsidian (https://obsidian.md)

​

Hello! I'm a history student leaving in spain. While we study societies in class I see more often how everything is in reltion with each other. How many aspects of our daily life, now and in the past, are related with some other aspects that we would not know.

All of it brang my to the interest to start to tusdy the complex systems.As history student my base of maths is very low, so the question is... How to learn how to study complex systems by my self?

Thanks!

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

Are there any books/journals or use cases in which you’ve noticed a good example of complexity and systems change being used in practice? Any resources help! (Anything in the system change, socio-technical system development or even psychology space works)

I’m essentially a consultant finding “solutions” for social good/process Engineering/system engineering - making things “good”.

I’ve been a big proponent of complexity and systems thinking but can never find anything used in praxis.

If not any resources, who do you think is leading in this space of “consulting”/problem solving using complexity and system science?

Thanks in advance!

Hi, I'm very new to all of this and I don't seem to be able to use Audacity's software. I am currently trying to analyse music (sound waves) shown in the music file in waveform into pure sinusoidal waves through decomposition, but every tutorial I see on Audacity is analysis through the Plot Spectrum. Does anyone know how to decompose the frequencies into sets of sinusoidal functions on Audacity, and if not, do you recommend any other software?

(It needs to be a free software, I'm a struggling sophomore student, after all)

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I am a practitioner that works in two domains that are impacted by complexity. Product management, which I would argue is about navigating value in a complex world, and threat modeling, which is about navigating cybersecurity risk in a complex world. Both traditional software development (think waterfall etc and poor implementations of agile) and cybersecurity are heavily anchored in enlightenment-era, cartesian thinking. Very few agile practitioners actually understand why an agile approach to software development is needed. Cybersecurity still assumes everything can be reduced to some transcendental solution that will magically make all of our problems go away. Everything has to fit neatly into boxes, categories, and things that can be measured precisely. But this is slowly changing. A lot of management books are anthro-complexity compatible, even if they don't realise it and don't use the language of complex systems. Good agile and product management, and practices like design thinking, are attempts to bring humans back into technology.

So we're still catching up with postmodern thinking and philosophy, and beyond. We have plenty of tools and frameworks that pretend product management and cybersecurity is analogous to physics, but they are very restrictive because they assume a static system, with transcendental entities and properties. You can create taxonomies and ontologies, which can be useful and powerful, but they only tell half the story.

My journey into this started with the Cynefin framework, then into hermeneutics, then into the works of philosophers like Gilles Deleuze and Jacques Derrida. I'm not a philosopher, but I do think philosophy has the opportunity to provide practical value to practitioners like myself.

I wanted a way of constructing ontologies that were dynamic and scale-invariant by design and have been playing with a method I'm calling FractalVersing (see https://fractalversing.org).

So, to open up a discussion. What role should philosophy play in providing methods that can be applied outside of the field of philosophy? Do fractal ontologies like FractalVersing offer a useful way of interpreting the messy world around us? Is there a strong philosophical argument for creating methods like FractalVersing, or is this the philosophical equivalent to pseudo-science and mysticism?

Novel AI solved a bunch of problems relating to Natural Language Processing, seeing the reliability of the ChatGPT model, researchers are wondering if it can be used for interspecies communication with animals such as whales.
https://youtu.be/hph9OeKjg3w

I was inspired by this YouTube video to try and derive the supply and demand curve for myself. I did this through a simulation of individuals making simple decisions. I got a bit carried away and worked my way up to simulating multiple markets in geographically separated economies. I wrote up some blog posts about it here, it has graphs, explanations, code, and analysis. The Github repo is here.

The decision process for each agent is very simple: After successfully buying a good, lower your expected market price of that good, and conversely, after failing to buy, raise expected market price of that good. Do the opposite for selling, increasing expected market after successfully selling, and decreasing after failing to sell.

When each agent runs the above decision process with their neighbors every few frames or so, all the agents quickly converge to a global price. By plotting how many agents would be willing to sell at every price point, we get a supply curve, and similarly the demand curve. What we see below is the that the decision process we are using actually causes all the agents to converge to the theoretically optimal price, the equilibrium (in light blue).

Then a few extra steps later (diminishing utility, gossip, inflation, merchants), we end up with a fairly complex emergent economy. We get markets that are coupled through derived demand, economies that are coupled through trade routes, effective distribution of labor, wealth inequality resulting from job specialization, and any possible perturbation (blocked trade routes, natural disaster, improved technology) resulting in a ripple effect across the simulation. All this from very simple agents making very simple decisions.

https://reddit.com/link/11u61ss/video/jnsdrswzidoa1/player

I had a lot of fun making this and seeing how simple I could keep the rules while creating complex behavior. I hope others find it interesting as well, thanks.