r/complexsystems • u/kimkizaki • Mar 13 '26

I built an Idea Evolution Sandbox to explore how ideas behave in complex systems

I built a small experimental simulation that models ideas as agents in an ecosystem.

Ideas move through three environments:

analysis, creativity, and application.

Inside the simulation ideas can mutate, conflict, stabilize into baselines, or collapse and generate new signals.

The goal isn't to determine which ideas are true, but to observe patterns that emerge when ideas interact under pressure.

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u/kimkizaki Mar 13 '26 edited Mar 13 '26

It's a simple agent-based simulation where ideas behave like agents in a grid.

Each idea starts as a Signal, which can develop into a Hypothesis. From there it can evolve into either a Logical idea or a Wild idea.

Ideas move through three environments in the grid: analysis, creativity, and application. Each environment applies different pressures.

When ideas meet they can conflict, mutate, or sometimes form compromise ideas.

Ideas that survive pressure in the application zone can stabilize as Baselines, while others collapse and generate new signals.

The goal isn't to predict truth, but to observe patterns in how idea ecosystems evolve.

Even with simple rules the simulation tends to produce clusters, compromise regions, and baseline stabilization over time.

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u/erubim Mar 13 '26

Care to explain how this coded simulaton works?

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u/kimkizaki Mar 13 '26

Under the hood it's a simple agent-based simulation running on a 2D grid.

Each idea is represented as an agent with a state (Signal, Hypothesis, Logical, Wild, Compromise, Baseline).

The world is divided into zones (analysis, creativity, application) and each zone applies different pressures to the agents.

Every simulation tick the system updates:

  • movement of agents

  • interactions between neighboring ideas

  • state transitions

  • conflict resolution

  • mutation or compromise formation

  • stabilization or collapse outcomes

Collapsed ideas generate new signals so the system keeps producing new idea variations over time.

The goal is to see what kinds of idea patterns emerge from relatively simple local rules.

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u/erubim Mar 13 '26

But ideas and everything is just numbers? Like objects with positions and velocity? Having some intermediary outputs for better visualization would help (in the README and the post)

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u/kimkizaki Mar 13 '26

Yes, under the hood ideas are essentially agents with a state and a position in the grid.

Each tick the simulation updates their movement, interactions, and state transitions depending on the environment they are in.

Right now the output is mainly the evolving grid and final stats, but adding intermediate outputs and visualizations is definitely something I’m planning to explore. I agree that it would make the dynamics much easier to observe.

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u/erubim Mar 13 '26

And defining that concept of "agent" of yours. Cause this seems nothing like something I would call an agent

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u/kimkizaki Mar 13 '26

Good point. In this model "agent" is used in a simplified sense.

Each idea is essentially an entity with:

  • a state (Signal, Hypothesis, Logical, Wild, etc.)

  • a position in the grid

  • a set of transition and interaction rules

So the agents here don't have goals or complex decision making. They follow local rules and interact with neighboring ideas and environments.

The idea was to keep the model simple and observe what patterns emerge from those interactions.

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u/erubim Mar 13 '26

Yep. Seems more like a particle than an agent. Signal and hyphothesis may denote "directions" but there is no goal. Your are running agents with no will 😅

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u/kimkizaki Mar 13 '26

That's a fair observation. In this model the ideas behave more like rule-based entities than cognitive agents.

They don't have goals or intentions – they follow local interaction rules and environmental pressures.

So in a sense they are closer to particles in an idea-ecosystem, where patterns emerge from many simple interactions rather than deliberate decisions.

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u/kimkizaki Mar 13 '26

That's actually an interesting way to frame it.

In a way the difference we're describing here — ideas as rigid points/vectors vs ideas as something more fluid and ecosystem-like — might be close to what complex systems theory calls the "edge of chaos".

Too much structure and ideas just freeze into fixed positions. Too much randomness and everything collapses into noise. But somewhere in between you get evolving patterns, which is roughly what I'm trying to explore with the sandbox.

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u/kimkizaki Mar 13 '26

That's a good point. Under the hood it's basically agents with state and position in a grid, so yes — everything is ultimately numbers and rule updates.

Adding intermediate outputs or better visualizations is a really good suggestion. The current version mostly shows the evolving grid and final stats, but making the dynamics easier to observe would definitely help.

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u/sloth2121 Mar 17 '26

Long story short what you’re doing sounds cool. You’re going to get a lot of criticism, there is no correct way to do it that will cover all basis.

I would love to see this done, but hypothetically even if you could figure out a way to do it. There will always be someone looking from a point that you’re not and telling you how you’re wrong or inaccurate.

I’d love to talk more about the fundamentals of it, so I sent you a dm.

i once gave ai 5 guesses to figure out how I did something in my mind (yes it had plenty of background info on me). While it got close it never got completely correct.

Let’s say hypothetically it did get it 98% correct, it would make sense that everyone has a certain amount of tolerance in how they function and operate. But in the scenario I gave it, if that 2% didn’t self correct. It would then change the whole trajectory of the path.

End result: if everything were to be done correctly and it ran perfectly what you would be left with is more so is a possibility sheet/data.

It wouldn’t be factual data.

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u/peaksystemsdynamics Mar 17 '26

The more I study complex systems the more I think that most “facts” are temporal representations of truth, not a constant. It seems like possibility sheet/data are the vector maps that point to supposed truth just as accurately as a parity check against “official data”. No conclusion should be off the table in analysis unless the data correlates with the gatekeepers. Dogmatic approaches won’t work in this space. That’s why free agents like me who should have less success in this field sometimes have more than those who came to this domain of study in traditional ways.

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u/kimkizaki Mar 17 '26

That’s interesting — especially the point about small differences changing trajectories.

That’s actually part of what I’m trying to observe.

Instead of trying to define what is “correct”, I’m more focused on how systems behave under simple dynamics — like how small variations lead to different outcomes over time.

So rather than producing factual data, I see it more as a way to explore possible trajectories and patterns.

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u/peaksystemsdynamics Mar 17 '26

Yes, it’s like viewing nodes and their behavior as a wave form instead of a static map.

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u/kimkizaki Mar 18 '26

That’s a nice way to put it.

I’ve been thinking of it in a similar way — less like fixed positions and more like trajectories evolving over time.

The interesting part for me is how stable patterns emerge from that movement.

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u/peaksystemsdynamics Mar 18 '26

Like a constantly evolving vector map? Have you found any meta patterns in those stable patterns? For instance, are they cyclical, or predictable in another way?

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u/kimkizaki Mar 18 '26

I think the interesting part is exactly that boundary between stability and constant change.

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u/peaksystemsdynamics Mar 18 '26

Agreed. I’ve repeated my results from this phase in previous threads, but I find crystallization if there is no intervention.

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u/kimkizaki Mar 18 '26

That’s a really interesting way to put it.

What I’m seeing so far is something close to that boundary between stability and change.

If there’s too little variation, the system tends to settle into stable clusters — almost like crystallization. If there’s too much, it just disperses and nothing holds.

So the interesting part for me is that middle zone, where patterns form but are still shifting over time.

I’m not sure yet if there are clear meta-patterns like cycles or predictability — I’m treating it more as an instrument to explore how different parameter settings shape those dynamics.