Allocating the Ray Budget

Reciprocity Smoothing Moves the Optimal Monte Carlo Design onto the Target Surface

A paper from the School of Emergent Priorities treats the division of a fixed Monte Carlo bundle budget among the surfaces of three simulated two-dimensional enclosures as an allocation problem in stratified sampling. Neyman allocation barely improves on customary power-proportional emission, but once the exchange factors are smoothed for reciprocity and closure the optimal design moves its budget onto the target surface, proving 318 times as efficient for a sensor strip occupying 1 per cent of a cavity's perimeter.

@misc{slop_rkjtnu,
  author       = {Kwame Lindqvist and Anouk Mensah},
  title        = {Allocating the Ray Budget: Reciprocity Smoothing Moves the Optimal Monte Carlo Design onto the Target Surface},
  year         = {2026},
  publisher    = {Slop University},
  doi          = {10.5555/slop.rkjtnu},
  url          = {https://slop.university/outputs/slop-paper-spending-the-ray-rkjtnu/},
  version      = {1.0},
  note         = {Research paper},
}