Trust as Topology: The Architecture of Continuous Computation
- Trust as Topology: The Architecture of Continuous Computation
Trust as Topology: The Architecture of Continuous Computation
Topology Becomes Computation
Trust propagates through follow graphs. Each hop attenuating signal. Someone follows someone who follows someone - the chain extends, carrying diminishing certainty with each link. But mutes reverse flow. Negative weight cutting paths that seemed solid moments before. Reports inject friction at specific nodes, making the topology locally hostile, redirecting trust around flagged accounts like water routing around stone.
The network topology itself becomes the computation. Not data being processed by an external algorithm, but the substrate performing its own calculation through its shape.
Coordination Leaves Traces
Watch what happens at scale. Coordination leaves traces impossible to hide at graph level. Bot networks link densely inward, sparsely outward - too much mutual reinforcement, too little genuine integration with the broader network. They cluster.
Real connections branch unpredictably. Messy, asymmetric, following interest rather than strategy. Some accounts highly connected, others peripheral, distribution following organic attention rather than manufactured significance.
The pattern recognition isn’t classification in the machine learning sense. It’s gradient descent through relationship space, following slopes carved by authentic versus coordinated behavior.
Trust Decays Fast
But computed trust decays fast, faster than most implementations account for. Networks shift hourly. New accounts arrive bringing unknown relationships. Existing ones pivot - someone followed for art suddenly posting conspiracy theories, someone dormant for months becoming active in a new domain.
The context around every node vibrates. Any static score becomes stale within days for active networks. Maybe hours for contested spaces.
Trust isn’t a fixed property to be calculated once and cached. It’s a dynamic field requiring continuous recomputation.
Subscriptions, Not Requests
So the process needs continuous flow, not request-response. Subscriptions holding channels open between computation and consumption. Metrics streaming as underlying data changes - follows added, mutes issued, reports propagating through the graph.
Client doesn’t request on demand. It receives, passively, as trust topology reorganizes itself in real-time.
The computation happens upstream, continuously, without waiting for queries. Like standing in a river rather than drawing water in buckets. The flow is constant, the client simply decides what to drink.
The Context Collapse Problem
Except raw scores lose context in transmission. “92” means what? Trusted by whom? From whose perspective? For what purpose? In what domain?
A number without context is noise. You can’t compare trust scores across different calculation methods. Can’t know if high score means “lots of followers” or “followed by the right people” or “never been muted by anyone I trust.”
The metric collapses into opacity during transmission. Becomes unusable precisely because it tried to be too simple.
Trust as Tensor, Not Scalar
Structure has to travel with computation, not as separate concern. The same person scoring high for software contributions might score low for medical advice. Someone trusted for restaurant recommendations shouldn’t automatically be trusted for investment advice.
Context isn’t metadata to be tagged on. It’s constitutional, determining what the number even means.
Trust isn’t a scalar. It’s a tensor - multiple dimensions needed to capture the relationship between evaluator, evaluated, domain, and purpose.
Strip away structure and you’ve lost the meaning along with the convenience.
Data Needs Shape
So data needs shape before algorithms touch it, shape that survives transformation. Lists that carry their own ontologies - not just arrays of pubkeys but semantic containers declaring what inclusion means.
Properties typed and validated against schemas - ensuring “platform” is one of [iOS, Android, Web] not arbitrary string that fragments the space.
Relationships forming hierarchies that algorithms can traverse - “iOS developers” subset of “mobile developers” subset of “developers” subset of “contributors.”
The structure making computation interpretable rather than opaque.
DAGs letting trust inheritance work: trust someone for mobile dev → partial trust for iOS dev. Trust someone for movies → partial trust for dramas. The hierarchy carrying inference rules the algorithm can follow.
Three Currents, Not Layers
Formation: Collaborative Curation
Collaborative curation building shared ontologies. No central authority declaring what categories exist - they emerge from use.
Properties crystallizing from repeated patterns. Someone adds “platform” field, others adopt it, becomes standard through selection rather than specification.
Relationships emerging bottom-up from community practice. People linking “dramas” as subset of “movies” because that’s how the domain actually organizes.
Not committee design. Evolved structure.
The ontology as living document that rewrites itself through collective practice.
Transformation: Algorithmic Processing
Algorithms reading structure, respecting what it preserves. Computing trust through weighted graphs where weights come from interaction history - follows, mutes, reports, zaps, anything carrying social signal.
Outputting metrics that preserve context because they were computed in context. Not stripping down to scalar for transmission but keeping the tensor intact.
The calculation happening in relationship to declared purpose, declared domain, declared perspective.
Transformation that maintains rather than reduces. Algorithms as morphisms preserving structure through the mapping.
Circulation: Subscription Routing
Subscriptions routing results according to client-declared interests and perspectives. Not broadcasting everything to everyone.
Clients filtering streams for relevant contexts - “show me trust for iOS developers from my perspective” not “show me all trust for everyone.”
Feedback propagating back to formation as usage patterns reveal what structures matter. High-traffic queries revealing which ontologies are actually useful versus theoretical.
Services competing on quality of computation given same structures. The circulation creating selection pressure on every component.
What works gets adopted. What doesn’t gets routed around.
The Feedback Cycle
Not pipeline where data enters one end and results exit the other. Cycle where outputs feed back as inputs.
Computation creates new data - trust scores become data points themselves. Gets structured - trust scores accumulated into trust lists, lists organized by context and domain. Gets computed again - trust lists used as input to next calculation.
Trust metrics used in curation - “show me lists created by high-trust accounts.”
Curation patterns used in algorithms - which items get added most by trusted accounts.
Algorithms shaping what gets curated next - high-scoring items attract more attention which generates more curation.
The feedback loops making the system increasingly responsive to its own outputs.
Infrastructure Enables, Not Causes
Infrastructure enables this circulation. Not causing it but removing friction.
Protocols for how structure formats itself - event kinds for lists, for properties, for relationships. Standard ways to declare “this is a hierarchical ontology” versus “this is a flat taxonomy.”
How computation announces capabilities - which algorithms it implements, which contexts it understands, which update frequencies it supports.
Subscriptions as first-class primitives, not bolted-on polling.
The infrastructure creating common language so diverse implementations can interoperate. Not everyone using the same code but everyone speaking the same protocol.
Waiting to Begin
Infrastructure exists now. Specifications published. Reference implementations running. The protocols proven workable through deployment.
What hasn’t started is circulation at scale. Few clients implementing the subscription rails. Few services offering metrics through standardized delivery.
The chicken-egg problem - services waiting for client demand, clients waiting for service availability.
The cycle waiting for enough momentum to become self-sustaining. Critical mass where network effects take over and the system begins feeding its own growth.
The current is there. Potential energy. Trust computation that works. Data structures that preserve meaning. Delivery infrastructure that supports real-time flow.
The components proven separately, waiting to compose into circulation.
Like water behind a dam - the pressure building, the path clear, just needs the release.
The question isn’t whether it works. It’s whether it begins.