Real-place simulation

A method of using existing, similar streets or places to simulate and judge the dimensions and qualities of a proposed space by standing there, using markers, and walking through.

Neighborhood — ranked by edge-count

Frameworks (1)

framework

Stepwise Process for Forming Hulls
uses
A 12-step sequence of structure-preserving steps proposed in this chapter for shaping urban space as positive hulls, beginning with identifying main spaces and ending with subdividing interiors.

Chapters (1)

chapter

How Living Process Lays The Groundwork For Coherence Of A City Through The Hulls Of Public Space
introduces
Chapter 3 of A Vision of a Living World, introducing the concept of hulls of public space as positive, living spaces shaped by structure-preserving transformations in urban design.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Using real-place simulation, one can reliably determine the optimal dimensions and orientations of urban spaces.claim0.787
Assertion that the Oakland experiments show the method's reliability.
Simulatorconcept0.782
The underlying LLM with autoregressive sampling; a passive entity capable of generating an infinity of simulacra but lacking its own beliefs or goals
real-world geometryconcept0.781
The structured geometry present in the external world (e.g., circles, spatial manifolds) that networks learn to mirror.
Process simulation with drawingsmethod0.759
The use of hand-drawn simulations to visualize step-by-step unfolding of the four-fold pattern over time.
Realnessconcept0.750
The representation of something currently being the case; a variable feature dimension of conscious contents distinguishing ideas from hallucinations of factuality
Simulation objectiveconcept0.746
The objective of minimizing predictive error on a self-supervised distribution, leading to Bayes-optimal conditional inference.
Simulation-Instantiation Problemconcept0.745
Simulating biological computation on digital hardware is fundamentally different from instantiating it; SpiNNaker example shows digital neuromorphic systems miss consciousness-relevant properties.
Sim-to-real gapconcept0.737
The challenge of transferring controllers from simulation to physical robots; multi-scale competency reduces this gap by allowing subsystems to compensate.