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FA-83596 / Card game rule engines / Open access

Redoubled overtricks score as doubled · case 01

Each redoubled overtrick earns 100/200 instead of 200/400.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The double level multiplier is missing from overtricks.

THE FAILURE

The double level multiplier is missing from overtricks.

Unsuccessful approach: Scaling the trick value by the doubling multiplier gives 60 or 120 per trick instead of 100/200.

Case contract

Input [level, strain, doubled(0/1/2), vulnerable, tricks]. Trick score C/D 20, H/S 30, NT 40+30; x2/x4 doubled/redoubled. Game bonus 300/500 when trick score >= 100 else 50 partscore; slam 500/750 (6) or 1000/1500 (7); overtricks trick value undoubled, 100/200 per double level; insult 50 per double level. Undertricks: 50/100 undoubled; doubled NV 100, 200, 200, then 300; doubled V 200 then 300; redoubled x2.

Why this case matters

Card-game engines, scoring apps and online tables apply this rule automatically on every hand.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    level, strain, dbl, vul, tricks = x
    need = 6 + level
    if tricks < need:
        down = need - tricks
        if dbl == 0:
            return -(100 if vul else 50) * down
        pen = 0
        for k in range(1, down + 1):
            if vul:
                pen += 200 if k == 1 else 300
            else:
                pen += 100 if k == 1 else 200 if k <= 3 else 300
        return -pen * dbl
    per = 20 if strain in 'CD' else 30
    base = per * level + (10 if strain == 'N' else 0)
    mult = [1, 2, 4][dbl]
    trick_score = base * mult
    score = trick_score
    score += (500 if vul else 300) if trick_score >= 100 else 50
    if level == 6:
        score += 750 if vul else 500
    elif level == 7:
        score += 1500 if vul else 1000
    over = tricks - need
    if dbl == 0:
        score += over * per
    else:
        score += over * (200 if vul else 100)
    score += 50 * dbl
    return score
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[[4, 'C', 1, False, 10], 510], [[7, 'S', 0, True, 12], -100], [[6, 'N', 0, True, 12], 1440], [[2, 'H', 1, True, 9], 870], [[3, 'H', 2, False, 11], 1160], [[5, 'N', 0, False, 11], 460], [[2, 'D', 0, True, 8], 90], [[1, 'H', 1, True, 8], 360]], [[[6, 'S', 0, False, 12], 980], [[7, 'D', 0, True, 8], -500], [[6, 'H', 0, False, 12], 980], [[6, 'H', 0, False, 11], -50], [[6, 'N', 0, False, 12], 990], [[6, 'D', 2, True, 13], 2230], [[4, 'N', 2, True, 11], 1520], [[1, 'D', 2, True, 8], 630]], [[[6, 'H', 0, True, 13], 1460], [[2, 'C', 0, True, 9], 110], [[4, 'N', 0, False, 12], 490], [[7, 'C', 0, True, 13], 2140], [[3, 'S', 1, False, 5], -800], [[5, 'C', 2, False, 13], 1200], [[2, 'D', 2, False, 10], 960], [[4, 'D', 2, False, 11], 920]], [[[4, 'N', 2, True, 11], 1520], [[2, 'C', 2, False, 7], -200], [[2, 'H', 2, False, 5], -1000], [[7, 'H', 2, False, 11], -600], [[3, 'N', 1, True, 7], -500], [[6, 'S', 0, True, 12], 1430], [[6, 'D', 2, True, 13], 2230], [[1, 'C', 1, False, 8], 240]], [[[1, 'D', 2, True, 6], -400], [[5, 'H', 0, True, 11], 650], [[4, 'D', 0, True, 7], -300], [[7, 'D', 1, True, 10], -800], [[4, 'C', 1, True, 9], -200], [[3, 'S', 2, True, 9], 960], [[4, 'D', 2, False, 11], 920], [[4, 'C', 1, True, 12], 1110]]]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("duplicate score case %d" % i, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
duplicate score case 0510510Passed
duplicate score case 1-100-100Passed
duplicate score case 214401440Passed
duplicate score case 3870870Passed
duplicate score case 49601160Failed
duplicate score case 5460460Passed
duplicate score case 69090Passed
duplicate score case 7360360Passed

SHA-256 / 081cbcf85f6cde70e8af2e6cbee2b7372030023879fd5ace879589089e84144f

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    level, strain, dbl, vul, tricks = x
    need = 6 + level
    if tricks < need:
        down = need - tricks
        if dbl == 0:
            return -(100 if vul else 50) * down
        pen = 0
        for k in range(1, down + 1):
            if vul:
                pen += 200 if k == 1 else 300
            else:
                pen += 100 if k == 1 else 200 if k <= 3 else 300
        return -pen * dbl
    per = 20 if strain in 'CD' else 30
    base = per * level + (10 if strain == 'N' else 0)
    mult = [1, 2, 4][dbl]
    trick_score = base * mult
    score = trick_score
    score += (500 if vul else 300) if trick_score >= 100 else 50
    if level == 6:
        score += 750 if vul else 500
    elif level == 7:
        score += 1500 if vul else 1000
    over = tricks - need
    if dbl == 0:
        score += over * per
    else:
        score += over * per * mult
    score += 50 * dbl
    return score
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[[4, 'C', 1, False, 10], 510], [[7, 'S', 0, True, 12], -100], [[6, 'N', 0, True, 12], 1440], [[2, 'H', 1, True, 9], 870], [[3, 'H', 2, False, 11], 1160], [[5, 'N', 0, False, 11], 460], [[2, 'D', 0, True, 8], 90], [[1, 'H', 1, True, 8], 360]], [[[6, 'S', 0, False, 12], 980], [[7, 'D', 0, True, 8], -500], [[6, 'H', 0, False, 12], 980], [[6, 'H', 0, False, 11], -50], [[6, 'N', 0, False, 12], 990], [[6, 'D', 2, True, 13], 2230], [[4, 'N', 2, True, 11], 1520], [[1, 'D', 2, True, 8], 630]], [[[6, 'H', 0, True, 13], 1460], [[2, 'C', 0, True, 9], 110], [[4, 'N', 0, False, 12], 490], [[7, 'C', 0, True, 13], 2140], [[3, 'S', 1, False, 5], -800], [[5, 'C', 2, False, 13], 1200], [[2, 'D', 2, False, 10], 960], [[4, 'D', 2, False, 11], 920]], [[[4, 'N', 2, True, 11], 1520], [[2, 'C', 2, False, 7], -200], [[2, 'H', 2, False, 5], -1000], [[7, 'H', 2, False, 11], -600], [[3, 'N', 1, True, 7], -500], [[6, 'S', 0, True, 12], 1430], [[6, 'D', 2, True, 13], 2230], [[1, 'C', 1, False, 8], 240]], [[[1, 'D', 2, True, 6], -400], [[5, 'H', 0, True, 11], 650], [[4, 'D', 0, True, 7], -300], [[7, 'D', 1, True, 10], -800], [[4, 'C', 1, True, 9], -200], [[3, 'S', 2, True, 9], 960], [[4, 'D', 2, False, 11], 920], [[4, 'C', 1, True, 12], 1110]]]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("duplicate score case %d" % i, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
duplicate score case 0510510Passed
duplicate score case 1-100-100Passed
duplicate score case 214401440Passed
duplicate score case 3730870Failed
duplicate score case 410001160Failed
duplicate score case 5460460Passed
duplicate score case 69090Passed
duplicate score case 7220360Failed

SHA-256 / 7ce7ede8080297179b1b7d7fe66fbd17c67e41446128e9ad4b383526726abe0a

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 8 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

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Verification & scope

A bounded toy rule contract stated explicitly in the contract field; cards are two-character codes (rank, suit). Not a complete implementation of any published rulebook or casino table rules. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:50:23.108723+00:00.

Case digest / 7b334e386665ed2052e679bc684a0d56a6f73c7f82c2728cf615d1f1d01aa444