semantic-spacetime

Semantic Spacetime

Semantic Spacetime (SST) is Mark Burgess's discrete, graph-theoretic model of meaning over time. A semantic element is one autonomous agent plus its scalar promises; a semantic spacetime is a collection of such elements in which a local change in state, promises, or configuration is a local unit of time. Time is proper time — there is no global clock (the precedence view Burgess credits to Lamport). Causality is cooperative: every adjacency requires an offer (+) and an acceptance (−) promise on both ends, so space is made of cooperating nodes and edges. The 2025 γ(3,4) formalism types the graph: three node meta-types (events, things, concepts) connected by four link types (0 = NEAR, ±1 = LEADS TO, ±2 = CONTAINS, ±3 = EXPRESSES). Absorbing states in partial graphs leak information, and intentionality enters at the boundary. SST is built on Promise Theory — for the promise vocabulary, load promise-theory instead of re-deriving it here. This skill is a thin router: load the dense material only when a row in Load By Need matches your task.

When to use

• When you need to design or analyze shared semantic ground between agents — model what "meaning" means in this system (what does a concept, term, or promise mean to whom), producing a γ(3,4) map of the shared semantic ground as the artifact.
• When you need to model intent or meaning over time — trajectories, drift, and convergence of understanding between agents, agents and humans, or agents and their instructions; the artifact is a semantic trajectory with recorded observations.
• When you need to design convergent, self-healing coordination — a loop in which state is continuously measured against a desired meaning and repaired toward it; model the loop as semantic elements whose local change is time.
• When you need to diagnose semantic drift, divergence, or dead-ends — absorbing states, meaning gaps, and non-converging agents; the artifact is a drift finding with the leaking boundary identified.
• When you need to map promises onto spacetime — trajectories, promise propagation, and causality between agents; model each promise as an edge and trace how intent propagates through the graph.
• When you need to analyze temporal blindness in agents — state tracking, event ordering, and causality failures where an agent cannot tell what happened before what; model event order via proper time instead of a shared clock.