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Open item 11 listed "a random (rather than oldest-first) uncle-selection draw" as an untested spec sensitivity. The spec does not leave it open: Uncle Selection in cryptarchia-v1-protocol.md has the proposer take the oldest candidates first, deterministically, because an uncle expires w_u slots after its own slot. That is exactly what every result in the report already uses, so the item is a conformance match, not a gap -- and the simulator comment calling uncle_random_p "the spec's unbiased coin" cites text the spec no longer has. What is genuinely open is deviation FROM that rule: selection is proposer-local and the uncles field is never validated. configs/uncle-selection.yaml measures the cost. A proposer that includes each candidate on a fair coin instead loses up to 0.10 in D-hat/D, and 0.063 at the recommended W = 10 once rho ~ 1 (0.902 vs 0.965, t = -8.6). At the design point the margin survives but is spent: 0.980 vs 0.997 against a 0.98 bar. The loss does not close as W grows, because a coin wastes opportunities rather than queue capacity and a well-sized window is precisely what keeps the queue short enough for that to bite. This matters for the sec 8.5 reward recommendation: the spec argues a proposer has no incentive to deviate BECAUSE uncles grant no reward, and paying them removes that argument. Also adds adversary_selection=whale (the largest holders at matched stake, for the untested concentration case). The marker is appended to key() only when non-default so every historical run's seed stays byte-identical, guarded by a test alongside the paired_streams one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
153 lines
7.2 KiB
Python
153 lines
7.2 KiB
Python
import numpy as np
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from tsi_sim.config import SimConfig
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from tsi_sim.engine import run_trajectory
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from tsi_sim.rng import rng_for, seedseq_for
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def test_seedseq_and_rng_deterministic():
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cfg = SimConfig(k=8, epochs=3)
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a = np.random.default_rng(seedseq_for(cfg)).random(5)
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b = rng_for(cfg).random(5)
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np.testing.assert_array_equal(a, b)
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def test_distinct_configs_get_distinct_streams():
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c0 = SimConfig(k=8, epochs=3, latency=0)
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c1 = SimConfig(k=8, epochs=3, latency=1)
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assert not np.array_equal(rng_for(c0).random(4), rng_for(c1).random(4))
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def test_trajectory_is_order_independent_and_reproducible():
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cfg = SimConfig(n_nodes=300, topology="regular", degree=8, k=8, epochs=6,
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link_latency_mean=2.0, max_uncles=2)
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r1 = run_trajectory(cfg)
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r2 = run_trajectory(cfg)
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assert [row["mean_ratio"] for row in r1] == [row["mean_ratio"] for row in r2]
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def test_replicates_differ():
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a = run_trajectory(SimConfig(n_nodes=300, topology="regular", k=8, epochs=6,
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link_latency_mean=2.0, replicate=0))
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b = run_trajectory(SimConfig(n_nodes=300, topology="regular", k=8, epochs=6,
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link_latency_mean=2.0, replicate=1))
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assert a[-1]["mean_ratio"] != b[-1]["mean_ratio"]
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def test_paired_streams_shares_the_root_across_uncle_models():
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"""Common random numbers: with paired_streams the two arms draw the SAME root seed."""
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from tsi_sim.rng import seedseq_for
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kw = dict(n_nodes=50, max_uncles=2, blend_delay_max=5.0, topology="blend",
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k=32, epochs=2, replicate=3, paired_streams=True)
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c = SimConfig(uncle_model="countable", **kw)
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o = SimConfig(uncle_model="old", **kw)
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assert c.seed_key() == o.seed_key() # the marker is dropped
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assert c.key() != o.key() # ...but identity still distinguishes them
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assert seedseq_for(c).entropy == seedseq_for(o).entropy
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def test_unpaired_is_the_default_and_separates_the_models():
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kw = dict(n_nodes=50, max_uncles=2, blend_delay_max=5.0, topology="blend",
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k=32, epochs=2, replicate=3)
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c, o = SimConfig(uncle_model="countable", **kw), SimConfig(uncle_model="old", **kw)
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assert c.paired_streams is False and o.paired_streams is False
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assert c.seed_key() == c.key() and o.seed_key() == o.key()
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assert seedseq_for(c).entropy != seedseq_for(o).entropy
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def test_paired_streams_does_not_perturb_unpaired_seeds():
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"""The flag must not enter key(): every historical seed stays byte-identical.
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This is what protects --old bit-reproduction of the pre-redesign runs (report §9).
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"""
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from tsi_sim.rng import seedseq_for
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for model in ("countable", "old"):
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base = SimConfig(uncle_model=model, n_nodes=50, max_uncles=2, k=32, epochs=2)
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flagged = SimConfig(uncle_model=model, n_nodes=50, max_uncles=2, k=32, epochs=2,
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paired_streams=False)
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assert base.key() == flagged.key()
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assert seedseq_for(base).entropy == seedseq_for(flagged).entropy
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# and the old model's key is still exactly the base tuple (no marker appended)
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o = SimConfig(uncle_model="old", n_nodes=50, k=32, epochs=2)
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assert o.key() == o._base_key()
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def test_paired_streams_gives_both_arms_the_same_stake_and_graph():
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"""Pairing must reach the actual shared inputs, not just the root seed."""
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import numpy as np
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from tsi_sim import topology
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from tsi_sim.rng import seedseq_for
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from tsi_sim.stake import make_stake
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kw = dict(n_nodes=60, degree=4, topology="blend", max_uncles=2, k=32, epochs=2,
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blend_delay_max=5.0, replicate=1)
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c = SimConfig(uncle_model="countable", paired_streams=True, **kw)
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o = SimConfig(uncle_model="old", paired_streams=True, **kw)
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kids = {n: seedseq_for(cfg).spawn(cfg.epochs + 3)
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for n, cfg in (("c", c), ("o", o))}
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s_c = make_stake(c, np.random.default_rng(kids["c"][0]))
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s_o = make_stake(o, np.random.default_rng(kids["o"][0]))
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np.testing.assert_array_equal(s_c, s_o) # same stake draw
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g_c = topology.build_path_latency(c, np.random.default_rng(kids["c"][1]))
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g_o = topology.build_path_latency(o, np.random.default_rng(kids["o"][1]))
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np.testing.assert_array_equal(g_c, g_o) # same peering graph
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def test_paired_streams_is_recorded_in_the_output_row():
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"""A paired run must be identifiable from its parquet alone.
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scripts/plot_fine_delay.py picks the paired test only when both arms report
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paired_streams; if the flag were missing from the recorded config it would silently fall
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back to the unpaired test and quietly discard the whole point of the paired sweep.
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"""
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from tsi_sim.metrics import _CONFIG_FIELDS
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assert "paired_streams" in _CONFIG_FIELDS
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def test_whale_selection_does_not_perturb_random_coalition_seeds():
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"""A default ("random") coalition's key must stay byte-identical to every historical run's.
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Same protection as test_paired_streams_does_not_perturb_unpaired_seeds gives the uncle model:
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the marker is appended only when non-default, so adding the knob rewrites no existing seed.
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"""
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from tsi_sim.rng import seedseq_for
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for model in ("countable", "old"):
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base = SimConfig(uncle_model=model, n_nodes=50, k=32, epochs=2, adversary_frac=0.3)
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explicit = SimConfig(uncle_model=model, n_nodes=50, k=32, epochs=2, adversary_frac=0.3,
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adversary_selection="random")
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assert base.key() == explicit.key()
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assert seedseq_for(base).entropy == seedseq_for(explicit).entropy
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o = SimConfig(uncle_model="old", n_nodes=50, k=32, epochs=2, adversary_frac=0.3)
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assert o.key() == o._base_key() # still exactly the historical tuple
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whale = SimConfig(uncle_model="old", n_nodes=50, k=32, epochs=2, adversary_frac=0.3,
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adversary_selection="whale")
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assert whale.key() != o.key() # ...but the whale arm is its own stream
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def test_whale_coalition_takes_the_largest_holders_at_the_same_stake():
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from tsi_sim.engine import _adversary_mask
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from tsi_sim.stake import make_stake
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cfg_r = SimConfig(n_nodes=400, stake_dist="pareto", k=32, epochs=2, adversary_frac=0.3)
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cfg_w = SimConfig(n_nodes=400, stake_dist="pareto", k=32, epochs=2, adversary_frac=0.3,
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adversary_selection="whale")
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stake = make_stake(cfg_r, rng_for(cfg_r))
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m_r, m_w = _adversary_mask(cfg_r, stake), _adversary_mask(cfg_w, stake)
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total = stake.sum()
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# The point of the knob is concentration at MATCHED stake, so the realised shares must agree
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# closely — a whale arm holding visibly more stake would confound the two.
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assert abs(stake[m_r].sum() / total - 0.3) < 0.01
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assert abs(stake[m_w].sum() / total - 0.3) < 0.01
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# ...and the whales get there with far fewer nodes: that is the variable under test.
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assert m_w.sum() * 5 < m_r.sum()
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# The coalition is drawn from the top of the distribution and dominated by it: the biggest
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# holders are all in, and the small top-up nodes contribute almost none of its stake.
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top3 = np.argsort(-stake)[:3]
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assert m_w[top3].all()
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assert stake[top3].sum() / stake[m_w].sum() > 0.9
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