This is what the rest of the site calls a “trust neighborhood” — the boundary of what each agent can see, affect, and reason about; beyond it, trust signals fade into noise. Formally: Markov Relevancy Horizon (MRH).The “Markov” part: the future depends only on the present — your history lives in your reputation, not in a log of every past step the network has to replay.
What this feels like in practice
Trust signal drops by roughly a quarter with each hop outward — about half by depth 3, below the noise floor by depth 4. The examples below show the same rule in three concrete cases.
Reply to a friend (1 hop): trust signal ∼0.85 — full strength, action is cheap.
Vouch for a friend's colleague (2 hops): trust signal ∼0.65 — still useful, action costs more.
Try to reach a stranger 4 hops away: trust signal below the noise floor, ATP cost spikes exponentially — so you can't cheaply contact people you don't know.
That's what distance-adjusted trust does: spam and drive-by abuse get expensive before they ever reach you.
Explore that boundary below — adjust horizon depth, explore the 4D profile (space, time, complexity, quality), and see how distance decays trust.
Horizon Depth: 2
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You
Alice
Bob
Hospital
TimeServer
Charlie
Dr. Smith
Pharmacy
Bank
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Horizon Depth2 hops
Only selfDirect (1)Default (2)Max (3)Beyond
Explore Nodes (hover to highlight)
5 entities beyond horizon (invisible)
4D Horizon Profile
Each action has a 4-dimensional context that determines its cost, required trust, and visibility.
Δ (delta) = the change or extent in this dimension — e.g. ΔR is how far in space an action reaches, ΔT how far in time.
Spatial (ΔR)40% weight
Temporal (ΔT)30% weight
Complexity (ΔC)30% weight
Quality (ΔQ)0% weight
ATP Cost
15 base x 1.5x quality
23 ATP
Quick Presets
Why Trust Neighborhoods?
In the real world, you can't see everything. You know your friends, have some sense of their friends, and beyond that it's mostly unknown. MRH formalizes this natural limitation as a privacy-preserving context boundary.
Natural Privacy
Entities beyond your horizon can't see your relationships. Privacy emerges from network structure, not access control lists.
Computational Efficiency
With ~10 connections per entity, depth-3 traversal covers ~1,000 entities instead of the entire network — you compute over your neighborhood, never the whole graph.
Spam Prevention
Actions outside your horizon cost exponentially more ATP. You can't spam people you don't know without burning through resources.
Trust Accuracy
Beyond ~3 hops, trust signals fade into noise — too thin to base decisions on. MRH stops at the depth where the signal still carries information.
Why 3 hops?Calibrated, not derived. Beyond ~3 hops in real social networks, the signal-to-noise ratio empirically collapses — trust through a friend-of-a-friend-of-a-friend carries little more information than a stranger's. Societies can configure their own horizon; 3 is the default that simulations cleanly separate signal from noise at.
The key insight: MRH creates a world where privacy, efficiency, and accuracy all align. You see what's relevant, can't spy on what isn't, and the system naturally scales without central coordination.
For the technically curious: bounded-depth traversal is O(d³) rather than O(n) in network size, and ignoring history beyond the horizon is the Markov property — the next state depends only on the current one, not the full past.