Semantic Spacetimes

Formalizing the semantics of space and time, for cognition and measurement (a route to knowledge representation)

This project started in June 2014, to document puzzles that have bugged me since I started thinking about promises around 2002. The chosen language here is Promise Theory (2004-2014), and it touches on cross disciplinary problems around how we measure and explain behaviour in distributed systems.

A semantic spacetime is a discrete graph, which evolves, and whose properties vary from point to point. They can be described both in terms of their topological, dynamical, and semantic functional properties.

Popular motivational essays:

Video of my talks about semantic spaces:

  1. Functional spaces - 25 yr anniversary keynote (see list selectionon page)
  2. Brains, Societies, and Semantic Spaces (related to blog post).
  3. Thinking in Promises for the Cyborg Age - Percolate Transition conference NYC 2015
  4. Video of Papers We Love talks by Paul Borrill and Adrian Cockcroft about spacetime in IT.

The need for a semantic understanding of spacetime comes from the intersection between spacetime and information science. Does matter fill space, or replace space? Physics sees spacetime through the lens of symmetry i.e. invariance (as a tool for description). But information science sees spacetime through the lenses of change and boundaries. One points us to ensemble interpretations of continuity, the other points us to event-based (cognitive) evolution.

Semantic spacetime can be applied to both physical spaces like supermarkets, as well as virtual spaces like computer clusters or cloud computing. A unified description of subjects like shared tenancy, intentional and functional layout, automated learning, and artificial reasoning. Applications are many and the unification of pervasive fixed and mobile computing into smart spaces (Internet of Things meets cloud computing) as well as insight into fundamental questions about space and time in nature.

Cognition and observation as spacetime

We measure the world according to either timelike and spacelike models:

  • Timelike is what we call cognition: there is a continual stream of input events, which alter our understanding and semantics framework in real time. This is a non-equilibrium view of observation.
  • Spacelike is what we call ensemble statistics: we try to divorce experiments from time by averaging over repeated experiments under invariant conditions, where time plays no role. Reversibility or indistinguishability

These are spacetime concepts related to information. Only by understanding these ideas in a discrete intentional framework can we make sense of what we interpret by space and time.