clay-borg/games/ground/examples/difficulty-baseline.rs
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CB-WP-0025 T02: the review withdrew the finding, and the fifth wrong
premise never left the repo

Separate agent, second tier-L review in this project. Six of seven
challenges conceded. The survey's headline finding is WITHDRAWN, not
softened.

C4 kills it, and the reviewer ranked it fourth. A FirstLegal policy --
take legal[0], no heuristic at all -- scores 0% at five and six seats
where GreedyPolicy scores 100%, and 77.5% at two seats where greedy scores
66%. Two unsophisticated agents span the entire range at the same seat
count. "The game is too easy at 5-6 seats" is therefore a statement about
GreedyPolicy, not about GROUND. The rescue the reviewer offered -- greedy
hits the 12-point ceiling in 200/200 deals, so the 6p row is a rules claim
-- dies on the same data: FirstLegal reaches that ceiling never.

C1: the per-node cost was wrong by 30-50x. The timer started before the
seed loop, so "us/node" included two setups, an entire greedy game and a
full validate+fold replay, divided by player-decision count. The tell was
in my own published output and I did not look at it: the figure FELL
(161/139/112) as branching ROSE (4.7/7.4/9.1), which no per-enumeration
cost can do. Re-measured with the clock around legal_commands alone:
3.0/3.5/4.1 us, now rising with branching. The reviewer measured 15.6-20.4
by a different isolation; we disagree by ~5x and neither has established
which is right, so T04 must benchmark it with criterion rather than adopt
either number.

C6: "exhaustive search is out at any seat count" is false -- ~3 seconds
over the last two rounds at 3p. With C1's correction the budget is
~10^5-10^6 nodes and bounded endgame search fits, so ADR-0013 cannot open
with "exhaustive is impossible, therefore determinized sampling" --
especially as sampling carries strategy fusion that exhaustive search does
not.

C3: the finding failed the admissibility rule this project wrote nine
hours earlier. 6/9/12 are sums where GROUND-WP-0004 T02 requires
per-priority rows, and the harness has no assertions, no --self-test and
no make target, so nothing can turn it red -- a `default` artifact wearing
a `counterexample` label, by CB-WP-0022 T05's own distinction.

C2: the ratio story explains nothing; 3p and 4p share deal, threshold and
ratio and differ by 12.5 points of win rate. C5: "explains the
maintainer's report" is contradicted by lib.rs:2487, which records his
losses as 3-player games on the pre-ruling deal, arithmetically unwinnable
at 6 against 7.

T06 exists to report to GROUND-WP-0005, which is BLOCKED waiting on a
difficulty baseline. Had this proceeded they would have been invited to
move thresholds on the strength of one bot's behaviour. That is the fifth
wrong premise this project would have sent them, and the second stopped by
an adversarial review rather than by a control. Both tier-L reviews here
have now caught a false headline that every gate passed.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-05 18:06:03 +02:00

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//! CB-RES-0008's runnable baseline (CB-WP-0025 T01).
//!
//! Two numbers the survey needs and cannot cite from anyone else, because
//! they are about **our** game on **our** machine:
//!
//! 1. **What the bots actually achieve** — win rate by seat count over a
//! seed range, which is the difficulty denominator `ground-game`'s
//! GROUND-WP-0005 is blocked on.
//! 2. **What a search would cost** — the branching factor of
//! `legal_commands` and the price of enumerating it, which decides
//! whether the honest version of "could we have won" is affordable.
//!
//! **This measures, it does not conclude.** Whether a bot win rate *is* a
//! difficulty is exactly what the survey and the review have to argue
//! about; this only makes the number exist.
//!
//! ```text
//! cargo run --release -p games-ground --example difficulty-baseline
//! ```
use cb_game_runtime::{ScenarioGame, Setup};
use cb_kernel::Aggregate;
use games_ground::bot::{legal_commands, play, GreedyPolicy, Policy, RandomPolicy};
use games_ground::GroundState;
const SEEDS: u64 = 200;
fn setup(players: u8, seed: u64) -> Option<GroundState> {
GroundState::setup(
&Setup {
players,
preset: format!("standard-{players}p"),
patch: Default::default(),
},
seed,
)
.ok()
}
fn policies(kind: &str, players: u8, seed: u64) -> Vec<Box<dyn Policy>> {
(0..players)
.map(|i| -> Box<dyn Policy> {
match kind {
"random" => Box::new(RandomPolicy::new(seed ^ u64::from(i))),
_ => Box::new(GreedyPolicy),
}
})
.collect()
}
/// Win rate, and the margin — because "we lost" and "we lost by one point"
/// are different games, and a rate alone hides which one this is.
fn win_rate(kind: &str, players: u8) {
let (mut wins, mut played, mut total_pts, mut total_thr) = (0u32, 0u32, 0u64, 0u64);
let mut margins: Vec<i64> = Vec::new();
for seed in 0..SEEDS {
let Some(state) = setup(players, seed) else {
continue;
};
let mut ps = policies(kind, players, seed);
let Ok(game) = play(state, &mut ps) else {
continue;
};
let Some(o) = &game.state.outcome else {
continue;
};
played += 1;
if o.group_success {
wins += 1;
}
total_pts += u64::from(o.total);
total_thr += u64::from(o.threshold);
margins.push(i64::from(o.total) - i64::from(o.threshold));
}
if played == 0 {
println!(" {players}p {kind:>6} no games completed");
return;
}
margins.sort_unstable();
let median = margins[margins.len() / 2];
// Wilson-free: report the count, not a confidence interval we have not
// argued for. The spec (T04) decides what interval is claimed.
println!(
" {players}p {kind:>6} {wins:>3}/{played:<3} won = {rate:>5.1}% \
mean total {mt:>4.1} of {th:>4.1} median margin {median:+}",
rate = 100.0 * f64::from(wins) / f64::from(played),
mt = total_pts as f64 / f64::from(played),
th = total_thr as f64 / f64::from(played),
);
}
/// What one node of a search costs, and how wide it is.
///
/// Measured on real mid-game states rather than on a fresh deal: at deal
/// time most of the interesting branches do not exist yet, and a
/// branching factor taken there would flatter any search proposal.
fn search_cost(players: u8) {
let mut widths: Vec<usize> = Vec::new();
let mut nodes = 0u64;
// **The clock brackets `legal_commands` AND NOTHING ELSE.**
//
// The first version started it before the seed loop, so it timed two
// `setup`s, a whole greedy game and a full validate+fold replay, then
// divided by the number of player decisions — reporting 112161 µs
// for a call that costs ~1620. The adversarial review caught it (C1),
// and the tell was in the published output: the figure FELL as seat
// count rose while branching rose, which is backwards for a
// per-enumeration cost. Accumulate only the call.
let mut spent = std::time::Duration::ZERO;
for seed in 0..40u64 {
let Some(state) = setup(players, seed) else {
continue;
};
// Walk a real game and sample the branching at every decision.
let mut ps = policies("greedy", players, seed);
let Ok(game) = play(state, &mut ps) else {
continue;
};
// Re-run the recorded commands, enumerating legality at each step.
let Some(mut replay) = setup(players, seed) else {
continue;
};
for (actor, cmd) in &game.steps {
if let cb_kernel::Actor::Player(seat) = actor {
let t0 = std::time::Instant::now();
let legal = legal_commands(&replay, *seat);
spent += t0.elapsed();
widths.push(legal.len());
nodes += 1;
}
if let Ok(events) = replay.validate(*actor, cmd) {
for e in &events {
replay.fold(e);
}
}
}
}
if widths.is_empty() {
println!(" {players}p no decisions sampled");
return;
}
widths.sort_unstable();
let sum: usize = widths.iter().sum();
println!(
" {players}p {n} decisions branching mean {mean:.1} median {med} max {max} \
{per:.1} us/call",
n = widths.len(),
mean = sum as f64 / widths.len() as f64,
med = widths[widths.len() / 2],
max = widths[widths.len() - 1],
per = spent.as_nanos() as f64 / nodes as f64 / 1000.0,
);
}
fn main() {
println!("CB-RES-0008 baseline — measured, not concluded\n");
println!("bot win rate over {SEEDS} seeds (GR-E01 group success):");
for players in [2u8, 3, 4, 5, 6] {
win_rate("greedy", players);
}
for players in [2u8, 3, 4] {
win_rate("random", players);
}
println!("\nsearch cost — legal_commands at every real decision point:");
for players in [2u8, 3, 4] {
search_cost(players);
}
println!(
"\nNOTE: a win rate is this POLICY's win rate over THIS seed range.\n\
Whether that is 'the difficulty' is T02's argument, not this tool's claim."
);
}