Complexity Oriented Programming
"Complexity Oriented Programming" doesn't exist as a formal paradigm yet, which makes it worth thinking through carefully. Here's how I'd frame it:
The core intuition
Most paradigms are reduction strategies. OOP (object oriented programming) encapsulates. FP (functional programming) eliminates side effects. Procedural linearizes. They all try to make complexity disappear through abstraction. Complexity Oriented Programming (COP) would invert this: instead of hiding complexity, it would model it faithfully and work with it as the primary material.
To give full credit here, this explicit framing of p2p as complexity economics was sparked by Guillaume Rouyer during a discussion with Tibi, before the summer of 2025, when he introduced Edgar Morin (La Methode). After reading Morin things started to come together. Our use of bio-mimicry in the OVN model from the early days, the use of terms like stigmergy, self-organization, etc. were useful frames. Now complexity becomes THE framework. Note that Holochain uses the same type of terminology / metaphores: DNA, zomes, cells, ... Sensorica chose Holochain and not blockchain precisely because it allowed us to build "organic" infrastructures for OVNs. Now we see Holochain as a beginning of what we call Complexity Oriented Programming, a concept that will certainly evolve in the future.
A complex world runs on a complexity economy, which runs on a complexity infrastructure, which relies on generative grammars. Arthur Brock was talking about language in some of his presentations...
Complexity Oriented Programming doesn't try to reduce complexity, to help us manage complexity, as some wrongfully describe it. We need to reclaime this expression! It allows us to embrace complexity. Computer programs need to be designed not as mechanical, linear, finite, but as generative of infinite states, as generative of attractors of behavior. They don't need to control or to predict. They don't need to precisely model. They need to allow humans to iterate and adjust.
Complexity cannot be understood, analyzed, controlled or predicted. It can be experienced and ridden like we ride a bicycle or serf a wave.
Yes, there are deterministic processes, causes and effects, at some levels of our economy or socia interactions, that part will be taken care of by state machines. The unpredictable and uncontrollable will be taken care of by complexity programming.
That's in short our initial thoughts... still half baked.
This is, according to us, the new tech paradigm that we need in order to support complex socio-economic processes. That is what we need to build new institutions for a increasingly complex world.
And all that rests on a new way of knowing, a new epistemology, the epistemology of complexity proposed by Morin. Full circle. Full stop.
What it would draw from
Complexity science offers a clear vocabulary: emergence, feedback loops, attractors, phase transitions, edge of chaos, adaptive agents. COP would translate these into first-class programming concepts. You wouldn't fight emergence, you'd design for it. You wouldn't isolate components from their environment, you'd model the coupling.
Key principles that could define it
Rather than single sources of truth, you'd have distributed state with coherence protocols. Rather than deterministic pipelines, you'd have adaptive feedback architectures. Side effects wouldn't be contained, they'd be typed as relational events flowing through a graph. Time and context would be primary, not afterthoughts.
Where it already exists implicitly
Some things already approximate COP without naming it:
- Holochain's agent-centric model, where global coherence emerges from local validation rules and the NDO hApp.
- Reactive/dataflow systems (signals, FRP)
- Actor model concurrency
- Cellular automata as computational substrate
- Valueflows/REA, which models economic reality as flowing events rather than static entities
The epistemological shift
In classical paradigms, the programmer is a god who designs a closed system. In COP, the programmer is more like an ecologist who designs conditions for emergence. You write rules, thresholds, feedback sensitivity, and then you observe what arises. The system is never fully knowable from the source code alone.
The honest tension
The hard challenge is tooling. Debuggers, type systems, tests, all assume reducibility. COP would need new verification paradigms, probably closer to simulation and formal methods than unit tests.
Given your work on NDO and the recognition architecture framework, there's a strong argument that what Sensorica is building is COP in practice: protocol-level rules that produce economic coordination as emergent behavior, without a central orchestrator. The Valueflows ontology isn't modeling a machine, it's modeling a living economic ecosystem.