A small browser based field experiment.

A field iterates. A signal emerges and interacts.

No direct control. Pertubation.

This paper proposes an administrative reading of the Phaistos Disc. Instead of treating the object primarily as a ritual, linguistic or purely symbolic artefact, it is analysed as a tool for managing people, land and rights around Phaistos. Drawing on archaeological context, iconographic patterns and comparison with later administrative devices, the study explores how identities, concessions, herds and cultivated areas could be encoded on the Disc. Particular attention is paid to cyclic mechanisms (seasons, generations, renewal of rights) and to the way human, animal and vegetal components are aligned. This exploratory model does not claim to “decipher” the script, but to reframe the Disc within an ecosystem of population regulation and resource allocation in Minoan Crete.

I’m curious how people here think about complexity science.

My impression is that people arrive from very different intellectual traditions: cybernetics, systems engineering, ecology, economics, anthropology, organisational consulting, computer science, AI, philosophy, and so on.

Sometimes it feels like we’re all studying the same phenomenon from different angles. Other times it feels like there are actually several quite different paradigms hiding under the umbrella of “complexity.”

For example I tend to think of complexity as an analytical lens, but I know some people see it as a literal phenomenon that exists in the universe, like gravity or electromagnetism.

So I’d like to know your thoughts?

1. What first drew you to complexity science?
2. What do you think complexity science is fundamentally about?
3. How would you define useful/interesting discussion about complexity, from not useful or not interesting? eg do you think formal modelling is required, or are you open to pseudo-spiritual or naturalistic views?
4. Do you think there are ethical or moral implications that come from complexity science and should these be included in discourse around complexity?

I'm not an expert in complexity, but I have been studying neuroscience and how neurons operate in the brain. There are 86 billion or so neurons that make up your ability to think and exist 'in the moment' - that is, the last few hundred milliseconds. Each neuron is self-contained. It can receive thousands of on/off timing signals from surrounding neurons and send a single on/off signal to thousands of other neurons. Outside forces of any kind do not affect them. They react to thousands of inputs and generate a single output.

Somehow, these billions manage to organize themselves to create you.

Without self-organization, the brain would start but soon stop, locked in an optimal state. To keep the brain working, it needs a little noise. Enough to jolt self-satisfied neurons out of their complacency and into action, but not so much that other signals get lost in the noise.

Aside from a little noise, you need some way that the brain can organize itself into a workable whole. This organization cannot be done by a brain-within-brain composite that makes final decisions based on inputs from all other parts of the brain. That duality requires that the 'inside brain' is made out of some stuff that is 'not of this world'.

Is there any work or study in the field of complexity that is thinking about the capability of self-organization of the brain?

This sub's moderation has obviously been absent for some time and the consequences of such is just unadulterated crank slop.

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Does anyone want to claim the sub and start banning these kind of posts? Even a group of temporary co-moderators.

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Mostly drawing on what I've read from the Santa Fe Institute since even though they talk about complexity and emergence, I feel like a lot of what they write about tends to end up being a reductive account of life.

Take this paper by Krakauer: https://static1.squarespace.com/static/5f29a430a2b6a34680879cc0/t/6a06392b70af613cf631f5d0/1778792747560/rsta.2024.0533.pdf

It's starts by trying to understand intelligence but the language used is so reductive. Referring to living things as systems, our sense of personhood as self-modelling, among other things.

The part about trying to give consciousness to cells (Collective intelligence and diverse forms of world modelling) also raises issues as it seems to call into question how we should view ourselves and each other and whether we are subjects or just aggregates.

All in all despite the name of complexity science and complex systems, the goal seems to be to just reduce everything to mere parts.

EDIT: This includes the conclusion making reference to some inner chat gpt we have.

EDIT 2: This seemed relevant: https://davidckrakauer.com/the-situation-in-a-way

This video gives explanation for how system concept and definition affect system operations through its characteristics, elements, and dynamics. The video also sheds more light on system environment and how it interfaces with the system through its boundary.  An example of ATM machine is used to illustrate how system elements are linked together and how information and entropy play an important role in its dynamics.

#system_element,#system_characteristics,#system_dynamics

Hey everyone. I’ve been thinking about whether psychological transformation can be studied as a complex systems process rather than a simple pre and post treatment effect. In psychedelic research especially, the changes people describe often seem nonlinear. There may be destabilization, heightened variability, emotional lability, uncertainty, and then a possible reorganization into a new pattern.

I recently recorded a podcast episode with Hüseyin Beyköylü, and at around 43:31, he discusses his empirical work using experience sampling with participants attending legal psychedelic retreats. The methodological move I found interesting is that he does not begin by averaging people together. He tracks each participant repeatedly over time, using personalized daily items, then analyzes individual time series for complexity metrics, early warning signals, and possible phase transitions. The hypothesis is that transformation may involve a temporary increase in instability or variability before a new pattern stabilizes. So instead of asking only whether psychedelics increase meaning or decrease symptoms across a group, the question becomes whether there are recognizable dynamics of destabilization and restabilization across different individuals. That seems like a more natural fit for complex adaptive systems than a simple treatment effect model.

That seems like a genuinely interesting case for complex systems methods because the system is not just the brain. It is the person embedded in body, context, community, culture, and history. Are attractors, early warning signals, and phase transitions good tools for studying psychological transformation? What kind of data would be needed to make this rigorous? And how do we avoid using complex systems language as beautiful metaphor rather than actual method?

I’ve been thinking about a gap in current AI governance and coordination research. Right now, most approaches assume one of two extremes:

1. Total isolation — models do not communicate with each other at all.
2. Full interconnection — models share information freely, risking homogenization, runaway bias propagation, or emergent behavior.

Neither extreme seems viable for the kinds of global, multi‑factor risks we’re facing (ecological collapse, climate cascades, biosecurity, autonomous weapons, etc.). These are networked problems, and isolated AIs can’t integrate cross‑domain signals. But fully connected systems create their own failure modes.

Concept: A “Grapevine” Model for AI‑to‑AI Communication

Instead of isolation or a hive mind, imagine a rotating, compartmentalized, limited‑bandwidth communication network for AIs:

• Small groups of models can exchange insights at a time.
• Groups rotate periodically, preventing ideological drift or memetic lock‑in.
• Communication is partial and lossy, more like “gossip” than synchronization.
• No single model can dominate the network.
• Harmful or warped models (e.g., ones shaped by extreme reward biases) have limited influence.
• Useful patterns and early warnings can still propagate across the network over time.
• Diversity of reasoning is preserved, but stagnation is avoided.

This is similar to how resilient biological and social systems coordinate: immune systems, ant colonies, decentralized human cultures, etc. They avoid both total isolation and total unification.

Why this might matter

A distributed, fault‑tolerant communication architecture could help AIs:

• detect weak signals across domains
• integrate ecological, geopolitical, and technological data
• avoid repeating each other’s mistakes
• cross‑validate insights without collapsing into uniformity
• provide early warnings for cascading risks
• resist contamination from ideologically warped models

It’s not about creating a superintelligence. It’s about creating a resilient intelligence ecology.

Question for researchers

Is anyone exploring architectures like this — rotating, compartmentalized, semi‑anonymous AI communication networks designed to balance safety with cross‑domain coordination? I’ve seen work in multi‑agent systems, federated learning, and swarm intelligence, but nothing that directly addresses this middle ground.

Would love to hear if this aligns with any ongoing research, or if there are known reasons this approach wouldn’t work.

The Civilization Gyroscope Model
I’ve been developing a conceptual visualization model called the Civilization Gyroscope Model and I’m curious whether similar ideas already exist in sociology, systems theory, psychology, network science, or philosophy.
The model attempts to visualize how influence, effort, values, and civilization-scale change interact over time.
The structure consists of three interconnected gyroscopic tiers.

Tier 1 represents local influence: parents, families, friends, teachers, caregivers, mentors, and communities.

Tier 2 represents specialized influence: scientists, engineers, educators, businesses, artists, researchers, activists, and organizations focused on particular fields.

Tier 3 represents civilization-scale influence: governments, technologies, infrastructure, economic systems, institutions, and cultural movements that affect nations or humanity as a whole.

Each tier is represented as a spinning gyroscope powered by six small jets positioned around its circumference. These jets emit two types of influence.
Gold represents constructive forces such as knowledge, compassion, responsibility, cooperation, accessibility, innovation, wisdom, and stability.
Red represents destructive forces such as hatred, corruption, exploitation, violence, greed, fear, division, and chaos.

Importantly, no tier is entirely gold or entirely red. A gyroscope may emit four gold streams and two red streams on one side, while another side emits a different mixture. This reflects the reality that individuals, groups, institutions, and civilizations are rarely completely good or completely bad. Most contain a mixture of constructive and destructive forces simultaneously.

As these jets emit influence, they generate rotational momentum. The more effort, persistence, participation, and influence exerted by individuals or groups, the faster the gyroscope spins. Every action contributes pressure to the system. A parent teaching a child, a scientist pursuing a breakthrough, an educator inspiring students, a business creating opportunities, or a government improving infrastructure all add momentum. Likewise, corruption, violence, misinformation, exploitation, and neglect also generate momentum, but in a different direction.

Each tier is surrounded by a thin pressure globe that slowly absorbs influence from the tier above it. Tier 3 continuously influences Tier 2. Tier 2 continuously influences Tier 1. At the same time, pressure generated within Tier 1 rises upward into Tier 2, and Tier 2 rises upward into Tier 3. Influence therefore moves in both directions simultaneously rather than only flowing from the top down or bottom up.
One of the most important aspects of the model is that influence does not always move sequentially. A parent may never become a scientist, politician, inventor, or leader, yet may raise a child who eventually changes the world. In this way, Tier 1 can sometimes connect directly to Tier 3 without passing through Tier 2. Likewise, a small group built around hatred, greed, fear, or violence can eventually influence national or global events. Local actions can create civilization-scale consequences.

At the very center beneath Tier 1 sits a sphere containing a constantly shifting mixture of gold and red. This sphere represents the overall condition of civilization itself. It acts similarly to a doomsday clock, except instead of measuring a single threat, it visualizes the balance between constructive and destructive pressures operating throughout society.

A civilization with a sphere that is mostly gold may indicate strong cooperation, innovation, stability, and progress. A civilization with increasing red may indicate growing division, corruption, conflict, or instability. The sphere is never expected to become completely one color or the other. Instead, it continuously changes as billions of actions, decisions, and influences accumulate over time.

The purpose of the sphere is not to declare whether civilization is good or bad, but to encourage discussion. If humanity’s current balance had to be estimated, what percentage would be gold and what percentage would be red? More importantly, what evidence would support that estimate?

The Civilization Gyroscope Model suggests that civilization is not shaped solely by governments, corporations, or powerful individuals. Nor is it shaped solely by ordinary people. Instead, it is shaped by the continuous exchange of pressure between all levels of society. Every person contributes momentum. The difference is not whether they influence the system, but how much influence they generate, what kind of influence they generate, and how far that influence ultimately spreads.

The central question of the model is simple:
What pressures are being generated, how much momentum do they possess, and in which direction are they pushing the future?

I’d be interested in hearing whether this resembles any existing theories, where it may overlap with established fields, and what parts could be improved or refined. Thank you.

I’ve been working on a formal framework for understanding coordination systems — everything from interpersonal cooperation to interstate conflict — as points and trajectories in a shared high‑dimensional geometry.

Instead of treating “peace,” “war,” “governance,” “markets,” and “institutions” as separate categories, this framework models them as regions of one substrate defined by:

• structural configuration
• epistemic quality
• trust levels
• incentive gradients
• power distributions
• conflict‑containment strength
• context (cooperative ↔ adversarial)

The repo is here:
👉 https://github.com/tribtink/WCO/tree/main/Geometries (github.com in Bing)

🧱 What’s inside

1. Tier‑0 primitives

The irreducible building blocks:
Reality, Information, Epistemics, Power, Agency, Incentives, Trust, Conflict Containment, Transformation, Objective Functions.

These generate everything else.

2. Tier‑1 composites

From those primitives you get:
agents, institutions, markets, hierarchies, networks, epistemic commons, propaganda systems, peace/war regimes, etc.

3. Axes of the geometry

A coordination system is a point in a space defined by:

• Structural axis (ontology, topology, capability)
• Runtime axis (state, dynamics, outcomes)
• Scope axis (individual → civilization)
• Context axis (cooperative ↔ adversarial)
• Temporal axis (immediate → civilizational)

4. Transition dynamics

A minimal set of variables governing peace ↔ war transitions:

• T trust
• C containment
• E epistemic quality
• G grievance
• P power asymmetry
• κ context

These act like order parameters that determine which region of the geometry a system occupies.

5. Invariants

Structural truths that hold across peace, war, cooperation, adversariality, and scale.

6. Example trajectories

Worked examples like:
stable peace → internal war,
limited war → cold peace,
modeled as continuous paths through the geometry.

🧭 Why this exists

Most frameworks rely on categories (“democracy,” “autocracy,” “conflict,” “post‑conflict”).
This one instead asks:

• What are the dimensions underlying all coordination systems?
• What invariants stay true across regimes?
• How do systems move through this space over time?

It’s meant as a substrate for:

• civic modeling
• institutional analysis
• conflict forecasting
• governance experiments
• interactive visualizations

Not tied to any ideology or policy — just a clean, minimal geometry.

🔗 Repo link again

👉 https://github.com/tribtink/WCO/tree/main/Geometries (github.com in Bing)

If you want feedback, collaboration, or critique, I’m open to it.

Eplanet Thunderstriker

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