2026-07-30 18:51:15 +02:00

17 lines
1.3 KiB
YAML

# Full scaled-k parameter sweep. Mean accuracy / q_eff / convergence are k-invariant,
# so k is scaled down for tractability; re-run headline + variance figures at full scale
# with fullscale.yaml.
# Sweep axes (cartesian product x replicates); every value is a list.
n_nodes: [1000, 2000, 4000] # number of nodes / stake holders
stake_dist: [uniform, pareto] # stake distribution (uniform = equal, pareto = heavy-tailed)
latency: [0, 1, 2, 4, 8, 16] # L: network latency in slots; block visible to others at t+L
max_uncles: [0, 1, 2, 3, 4] # U: max uncle references per block (0 = baseline, no uncles)
uncle_strategy: [oldest, random] # uncle selection: oldest-first fill vs random coin-flip
replicates: 12 # independent RNG replicates per grid cell
base: # per-run settings shared by every cell (not swept)
k: 64 # security parameter (scaled; T = 6*floor(64/f) = 11520 slots)
epochs: 45 # epochs simulated per trajectory
f: 0.03333333333333333 # slot activation coefficient (default 1/30); configurable
genesis_d_factor: 0.5 # genesis D_est = factor x true total stake
pareto_shape: 1.16 # Pareto (Lomax) tail index for the pareto stake distribution