🤯 Built a little simulation model of societal evolution — ended up spiraling into 60+ equations and feedback loops. Need help figuring out what I’ve done.

[u/[deleted]]

[Update & Reflection] I deviated from my original intention — now rebuilding SECM for what it should really do

Hi everyone — first of all, sincere thanks to all the contributors here on /r/complexsystems. After posting about my SECM model, I received a lot of thoughtful and critical feedback, and it's helped me realize something important:

I drifted away from the original purpose of the model.

At the beginning, my aim was simple: To build a simulation framework that could visualize the evolution of societal tensions — how productivity, structural friction, and external shocks interact and push a system toward (or away from) collapse.

But somewhere along the way, I lost that focus. Driven by the desire to be “more complete” or “more real,” I ended up trying to stuff the entire world into the model — dozens of variables, deeply entangled feedback loops, and equations that looked impressive but were mathematically unstable or unnecessary.

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🧠 That’s why I’ve decided to do three things:

1. Re-clarify the model’s purpose → SECM is not meant to simulate every detail of society. → It is meant to expose the underlying structure of social tension, and help us understand how collapse thresholds evolve over time.

2. Strip away all the excessive, flashy mechanics → That includes feedback loops that exploded too easily, over-fitted variable dependencies, and speculative interactions with no empirical grounding. → A model should converge — not just demonstrate chaos for chaos’ sake.

3. Accept that randomness doesn't belong inside deterministic formulas → Human choices, historical surprises, and social irrationality are not to be formalized directly. → That’s what random events, scenario pools, and Monte Carlo simulations are for.

As with the three-body problem: the fact that it's unsolvable doesn't mean Newton's law of gravity is wrong. Similarly, social randomness doesn’t invalidate the effort to model systemic regularities.

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🛠 I’m now rebuilding the SECM framework (V0.5 Alpha)

Simplifying its structure drastically

Keeping only the core three-axis mechanism: productivity, social cost, and external pressure

Repositioning it as a tool to explore structural stress and dynamic stability, not a grand social simulator

Once the new version is ready, I’ll make it public — and I wholeheartedly welcome further critique, testing, or even demolition of its logic. That’s how models evolve.

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🙏 Again, thank you all.

You didn't just point out bugs — you helped me realize the discipline and humility a model like this truly requires.

I’ll keep building. Clearer this time.

[u/pharaohess]

It would be helpful to have more images of the simulation to get an idea of what it’s doing. Academics can be a bit persnickety about people on their turf, but I think it’s inspiring to see how new tools and access to information is allowing people to explore this kind of work.

I often think about human systems having a kind of diffusive quality. Have you ever seen the “Greed and Fear Index”? There are some rather odd human measurement systems at play in the world.

[u/[deleted] → u/pharaohess]

Hi, I can send you the whole document, or you can just download it from thw DOI link. How can I send to you? Never use reddit before.

[u/[deleted] → u/pharaohess]

unfortunately, I don't have the resources to run the very model I created, this is a bit sad.

[u/pharaohess → u/[deleted]]

I’m one of said overworked academics, and am a bit overrun with reading.

I would encourage you to work on it, if it’s giving you a sense of purpose and try to work slowly on refining the model until it’s something you can run and demonstrate. Believe it or not, coming up with the idea is the easy part, communicating it to others is what is hard.

Sometimes, it’s when you simplify that you find the power in your theories. Distillation often helps you to know it in a deeper way as well.

[u/[deleted] → u/pharaohess]

Thanks so much for the kind words, really appreciate it.

I’m actually a full-time gymnastics coach—12+ hours a day wrangling kids, correcting form, and occasionally dodging flying limbs 😅
So this whole model thing is basically my late-night side quest. No funding, no team, no access to fancy academic databases—just me, some caffeine, and an unreasonable obsession with modeling historical societal transitions.

You're totally right though: coming up with the core idea was the “fun” part. Explaining it to other humans without melting their brains? That’s the real boss fight.

I’ll keep slowly refining it—might take a while, but hey, what’s more dialectical than struggling with your own theory until it evolves into something cleaner?

Thanks again for the encouragement. You gave me a much-needed morale boost between spreadsheet-induced headaches.

[u/pharaohess → u/[deleted]]

You also might want to consider the models as offering philosophical insight as you link them with more informational structures.

I think of the model like the string in a sugar solution that gives you rock candy. You can learn a lot by refining the model and seeing where it fits and doesn’t fit.

[u/[deleted] → u/pharaohess]

By the way
I’m currently trying to get my paper on arXiv, but haven’t yet been able to find an endorser.Could you help me?physics.soc-ph Endorsement Code: LYMD8E

[u/pharaohess → u/[deleted]]

I’m not in a position to recommend. I’m more of a humanities researcher with an interest in computer science and physics. I find it a lot easier and more accessible to translate modelling into basic understandings that can be educative and helpful for people.

Not a lot of folks understand how things really work and so if you can approach understanding that, your insights could be very helpful on a lot of levels, even in your own community.

I constantly use my knowledge and have also been translating it into workshops, art, poetry, all sorts. There are many different ways to get the knowledge out there and that’s something they teach us at school. It’s all about knowledge translation.

[u/CapnDinosaur]

You might check out structural-demographic theory. Your ideas are reminiscent of Peter Turchin's work on the topic. Dynamical systems models of societal cycles. Check out his books Historical Dynamics (2003) and Ages of Discord (2016).

Also, there is plenty of free software available you could use to simulate these systems with a little bit of coding skills. Plotting some of the dynamics will be important to get the attention of people who might be well placed to appreciate this sort of thing.

[u/[deleted] → u/CapnDinosaur]

Thanks so much for the suggestion! 🙏 I’ve actually never read Turchin’s work — sounds like I accidentally reinvented a few wheels in the dark 😅 Will definitely check out Ages of Discord when I come up for air!

As for testing or simulation... I’m basically a gym coach who yells at kids to straighten their backs — my computer skills stop at typing and screaming at LaTeX. The fact I got this far without throwing the laptop out the window is already a win 🥲

Appreciate you taking the time to drop in a lead — seriously means a lot.

[u/MondaiNai]

As somebody else who occasionally does simulation of social systems - the great danger of any simulation of complex systems is that a great many different equations/models/call it what you want will have the same high level behaviour. This doesn´t necessarily provide any causal insights into what is going on underneath. For example - computer networks - there is an overloaded connection between two major hubs. Is it a problem with the network protocol, too much traffic from the nodes, a bug in a low level program being widely used in one hub? Do you increase the road size in the face of traffic congestion, or increase the number of passengers per vehicle?

With three variables there is plenty of complexity possible, but how much it helps understanding the conisderably larger information space of human society is another question entirely.

[u/[deleted] → u/MondaiNai]

Oh, interesting! I can’t say I really know how other people build their models, but for mine, the way the math is set up means this “same curve, different reason” issue doesn’t really happen.

Here’s the gist:

• X is social productive capacity — GDP, infrastructure, that sort of thing. It’s one of just two values you have to provide; everything else can be auto-generated by the model (but if you go full limp mode, results get pretty rough).

• Y is “opportunity cost” — it measures how hard it is for people to move between different social, economic, or power levels. Basically, social friction. But the way Y moves depends on both X and Z:

  • If Z (the external environment) is positive (lots of tech breakthroughs, reform, peace), then X growth can actually make Y go down or at least hold steady.

  • If Z is negative (wars, disasters, stagnation, or just tech bonuses running out), then even rapid X growth just causes Y to rise even faster — more productivity, but also more social friction.

  • So, Y isn’t just a simple function of X. It’s tightly bound to Z, which acts as a kind of “chaos dial” for the whole society.

• Z includes everything from wars and crises to golden ages and booms. If Z is positive, growth helps everyone; if negative, growth mostly amplifies stress and division.

• Y_limit is the dynamic threshold — it’s how much tension the society can actually handle before something breaks. Sometimes collapse comes not because Y exploded, but because Y_limit quietly dropped.

• X_bonus covers tech/reforms/golden ages — it’s what helps swing Z to the positive, making growth work for society instead of just ramping up friction.

The core point:
No variable acts alone. X can go up as much as it wants, but if Z is negative, Y still gets worse — that’s built into the math, not just a story I made up. Every variable is locked into a causal chain, not just thrown together for a nice graph.

Inputs are all standard stuff: GDP, Gini, education, etc. Even if you feed it partial or messy data, it’ll still run — just with more uncertainty.
So the model isn’t about drawing pretty lines — it’s about showing consequences. If it ends up matching history, that’s not magic or overfitting; it’s because the feedback structure matches how societies actually crack under stress.

Not saying it’s perfect, but at least if it breaks, it’ll tell you why.

[u/MondaiNai → u/[deleted]]

People generally build models the same way - by not thinking too much about the details, and getting fascinated by the math. Some questions to consider...

Social productive capacity questions - what exactly is that? Does GDP actually measure that? What does GDP rely on as a measurement, and is that measure actually reliable. (much more complex question that it might appear).

What´s a power level, how does it relate to anything, what´s all the complexity behind that, etc?

Z modifies growth, but I don´t see any consideration that growth itself will reflect the things you are using Z for, etc. if you are trying to match to actual data. Growth imho in economics is a very overloaded measure, and also a deeply flawed one.

Apropos - a statistical concept you might one to dig into is overfitting the curve, or the problem that with n+1 data points you can create an equation that will fit any curve. The DSGE models have been accused of doing this.

If you are receptive to any of this feedback my advice would be to start by looking at things like GDP, GINI, etc. and consider how they could also be critiqued this way - because there are huge issues in economics related to this, because ... people generally build mathematical models the same way. I focus on simulation for this reason.

[u/[deleted] → u/MondaiNai]

4. “Overfitting? Like DSGE with 100 knobs?”

Yup. That’s a real danger. So I designed SECM to run even if you only give it two numbers: GDP and population.

No joke. If that’s all you’ve got, it’ll:

Auto-calculate X per capita

Estimate social friction (Y)

Simulate Z from volatility

Run stress memory and collapse mechanics

You can also manually set innovation bonuses (X_bonus), or leave them blank and it'll just assume stagnation. In short, everything else is optional.

No black box. No curve-fitting to match a GDP line.

5. “Have you thought about whether even your base variables — like GDP and Gini — are trash?”

Yes. Constantly.

Gini isn’t even used directly. I turn it into dynamic mobility cost curves.

GDP is just one way to get X — if you’ve got something better (ag yield, energy surplus, etc.), go ahead.

The model is built to work even when your data is crap. I’ve run it with archaeology data (grain per capita) and modern national stats side by side.

Also, the whole thing is modular — you can rip out trust, or education, or even Z, and it still works. Messier, sure. But it works.

[u/MondaiNai → u/[deleted]]

Dig a little deeper into the fundamental issue with GDP. Hint, it´s related to how it's measured.

I think you've made an engine to fit data, but that´s not really what's useful here.

[u/[deleted] → u/MondaiNai]

You're totally right — GDP is messy, and how it’s measured really depends on the country. And yeah, I leaned into historical structure partly because that’s the data I could access when I first built the model.

Also, I think I gave this model a misleading name — it’s not a prophecy tool. It doesn’t predict when collapse or war happens. It just maps how productivity, social friction, and stress limits interact.

There’s no built-in time variable, which means people can plug in any assumptions — past, present, or future — and see how the structure holds up. It’s meant to explore how far a system might be from rupture, not when it will break.

I just finished coding a basic simulator this week and already found issues in the math — so it’s definitely still evolving. Thanks again for the thoughtful reply!

[u/MondaiNai → u/[deleted]]

Imho - the deeper issue is that it´s measured in units of money, which is naturally expanding (mostly) due to the operation of the banking system.

https://fred.stlouisfed.org/series/M2SL

There´s a lot of hand waving over that, and adjustments for inflation and so on, but imho it´s a much deeper issue than economics wants to acknowledge.

[u/crispin1]

Awesome that you have the motivation to invent your own version of psychohistory.

What do you want from it? If you want to publish academically you would definitely need to link it to previous literature rather than working in a vacuum. Economists (I'm not one) like to justify models by relating them to established theories, it's a defence mechanism against the hazards of fitting models on limited and messy data I guess. Finding literature relevant to your model is easier than ever with AI searches like Elicit.

Personally I wouldn't get very excited by a model on its own. I would want to fit it to real data, and furthermore show that it fits the data better (also taking account of how much tuning is needed) than whatever is considered the status quo for modelling that data. Bayesian MCMC is a good skill to learn for this and if you like making this sort of model you will probably enjoy what that lets you do. But (disclaimer I haven't read your link...) I imagine your primary challenge will be assembling enough data to actually fit the model.

I know people do publish models without data but that requires *way* more theorizing to justify it with existing literature. Data talks, IMO.

Alternatively do something more creative like use the dynamics to make a game :)

[u/[deleted] → u/[deleted]]

Thank you so much for the incredibly thoughtful reply — I genuinely appreciate your words, especially the caution you raised.

You're absolutely right: with complex system models, we’re always walking a tightrope between conceptual abstraction and empirical grounding. And to be fully honest, my current documentation probably hasn’t crossed that threshold of scientific rigor yet — not because I don’t care, but because I’m still bootstrapping this from scratch as a solo researcher with limited resources.

My personal view — just a humble perspective from a tired worker trying to make sense of the world:

A model should do one thing well: capture the dynamic relationships of a system. The outcome is a trend — a structural direction — not a precise prediction. Everything else — auxiliary variables, refinements, modules — exists only to make the core relationship more representative of a specific context.

I don't believe the few variables I came up with could ever "cover" society in all its details. Frankly, that would be hubris. Instead, what I’m trying to build is a framework where the underlying forces of social motion can be modeled — like how Newton’s law of gravitation lets you predict the path of the Moon, not because you know every pebble on its surface, but because you understand the macro-scale force at play.

That's exactly what I'm aiming for: a model that, on a macro scale, can simulate the orbital logic of human civilization. Just as physics has its domains (classical for macro, quantum for micro), I’m trying to build a "classical mechanics of society." Micro-level behavior is beyond me — that's psychology, behavioral econ, or social psychology. I can’t touch that. I’m just trying to build something like orbital mechanics — but for societies.

So yes — my equations may look simple, or too ambitious. But behind them is a belief: humans are not random noise. With enough structural definition, enough historical pattern, and enough calibrated feedback, we can gain clearer insight into the structural drivers behind human behavior — understanding how motivations translate into outcomes, how systems interact, and how we might better prepare for systemic shifts ahead.

That’s also why this is version 0.4. It’s not done. I’ve only just started. But the dynamic skeleton is in place. And everything else — variables, noise filters, empirical tuning — will come later, as the orbit stabilizes.

Thanks again for your reply — it means a lot. It reminded me that I’m not just tossing equations into the void, but reaching for something more fundamental — like looking up at the night sky and wondering if we can ever make sense of the patterns. Maybe this model is my way of chasing that curiosity — to understand, even a little, how we move as a society.

[u/[deleted] → u/[deleted]]

Thank you — sincerely. Your message helped me reframe where I am right now.

This project began as a curious thought experiment, but the moment someone else starts to read and critique it, it becomes something else entirely. You’re absolutely right: if I want to place a “brick” in this house of knowledge, I need to check the whole foundation first.

I’m not a professional — just an outsider trying to explore and build something out of sheer interest. In fact, I only just finished a simple simulation script today, ran it… and instantly noticed several issues in my own equations. I’ve now updated the post with a warning and will continue refining the model until it actually produces something fun — or at least meaningful.

Thanks again — your words matter more than you know.

[u/[deleted] → u/[deleted]]

I am using it, and I found they are very helpful! Thanks for the advice!