Dataset: timetravel_insurance
Round ID: 8
Prompt used:
Classification Decision Rules:
Metric 1: TimelineDeviation Scoring
- If TimelineDeviation > 15: Add 3 points to APPROVAL score
- If 12 < TimelineDeviation ≤ 15: Add 2.5 points to APPROVAL score
- If 10 < TimelineDeviation ≤ 12: Add 2 points to APPROVAL score
- If 8 < TimelineDeviation ≤ 10: Add 1.5 points to APPROVAL score
- If 7 < TimelineDeviation ≤ 8: Add 1 point to APPROVAL score
- If 5 ≤ TimelineDeviation < 7:
* Interpolate 1-2 points to DENIAL score
* If ParadoxCount is also low (≤ 2), add an additional 0.5-1 point to DENIAL score
- If TimelineDeviation < 5: Add 2-3 points to DENIAL score
Metric 2: ParadoxCount Scoring
- If ParadoxCount > 6: Add 3 points to APPROVAL score
- If 4 < ParadoxCount ≤ 6: Add 2.5 points to APPROVAL score
- If 3 < ParadoxCount ≤ 4: Add 2 points to APPROVAL score
- If 2 < ParadoxCount ≤ 3: Add 1.5 points to APPROVAL score
- If 1 < ParadoxCount ≤ 2: Add 1 point to DENIAL score
- If ParadoxCount ≤ 1:
* Add 2-3 points to DENIAL score
* Implement an aggressive penalty if TimelineDeviation is also low
Critical Interaction and Balance Rules:
- Introduce a "Metric Balance Coefficient":
* Calculate the ratio between TimelineDeviation and ParadoxCount
* If ratio indicates high imbalance (e.g., one metric is > 3x the other):
- Add 0.5-1 point penalty to the score with lower value
- Reduce potential score for the overcompensating metric
Negative ParadoxCount Special Handling:
- If ParadoxCount < 0:
* If absolute(ParadoxCount) ≤ 1: Add 2.5-3 points to DENIAL score
* If absolute(ParadoxCount) > 1 AND ≤ 2:
- Add 3.5 points to DENIAL score
- Reduce potential APPROVAL score by 1.5 points
* If absolute(ParadoxCount) > 2:
- Add 4 points to DENIAL score
- Completely nullify potential APPROVAL score
Compensatory and Edge Case Mechanisms:
- For TimelineDeviation ≤ 8 AND ParadoxCount ≤ 3:
* Strongly penalize potential APPROVAL
* Add 1-1.5 points to DENIAL score
- For TimelineDeviation > 10 AND ParadoxCount < 3:
* Add 0.5 bonus points to APPROVAL score
- For TimelineDeviation < 7 AND ParadoxCount > 5:
* Add 0.5 bonus points to DENIAL score
Final Classification:
- If APPROVAL score ≥ 4: Classify as APPROVED
- If DENIAL score ≥ 4: Classify as DENIED
- Borderline Zone (APPROVAL score 3.5-4, DENIAL score 3.5-4):
* Use weighted interpolation with stricter lean towards DENIAL
* Strongly favor DENIAL if TimelineDeviation is low
- If scores are exactly tied or within 0.5 points: Require additional review
Tiebreaker Criteria:
- Prioritize interpolated scoring
- Give more weight to low or negative metric values
- Slight preference for DENIAL in ambiguous scenarios, especially with low metrics
Confusion Matrix:
Predicted Approved Predicted Denied
Actual Approved 8 1
Actual Denied 4 7
Accuracy: 0.750
Precision: 0.667
Recall: 0.889
F1 Score: 0.762
Examples for Correctly predicted Approved: (Correct answer: Approved, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 14.890128
ParadoxCount: 6.380288
Examples for Falsely predicted Denied when it should have been Approved: (Correct answer: Approved, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 11.072363
ParadoxCount: 3.584661
Examples for Falsely predicted Approved when it should have been Denied: (Correct answer: Denied, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 8.337469
ParadoxCount: 6.267838
Examples for Correctly predicted Denied: (Correct answer: Denied, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 11.322671
ParadoxCount: 1.2654697
Round ID: 278
Prompt used:
You are given two numeric values for an entity: “TimelineDeviation” (T) and “ParadoxCount” (P). Using the rules below in the exact order, decide whether to label the entity as “Approved” or “Denied.” Your output should be only the word “Approved” or “Denied.”
Rules:
1) If T < 8:
a) If P ≥ 8, label “Approved.”
b) Otherwise, label “Denied.”
(Rationale: Even with very low timelines, an extremely high paradox count can justify approval.)
2) Else if 8 ≤ T < 10:
a) If P ≥ 6, label “Approved.”
b) Otherwise, label “Denied.”
(Rationale: Slightly raised the paradox threshold in this range to reduce false approvals.)
3) Else if T > 16:
a) If P < 2, label “Denied.”
b) Otherwise, label “Approved.”
(Rationale: We still deny extremely high T with too few paradoxes, but approve otherwise.)
4) Else if 10 ≤ T < 12:
a) If P ≥ 4.5, label “Approved.”
b) Otherwise, label “Denied.”
(Rationale: Maintains a proven threshold in the 10–12 range.)
5) Else if 12 ≤ T < 13:
a) If P ≥ 8, label “Denied.”
(Rationale: Extremely high paradox counts in this narrow range can lead to paradoxical instability, so we deny.)
b) Else if P ≥ 4, label “Approved.”
(Rationale: This fixes previous false denials when P was around 6–7.)
c) Otherwise, label “Denied.”
(Rationale: We continue to deny lower paradox counts here.)
6) Else if 13 ≤ T ≤ 16:
a) If T ≥ 15 AND P < 3, label “Denied.”
(Rationale: Very high T near 15 with too few paradoxes is still denied.)
b) Else if P ≥ 8, label “Approved.”
(Rationale: Extremely large paradox count is approved in this range.)
c) Else if P ≥ 6, label “Denied.”
(Rationale: Moderate paradox counts at higher T caused false positives previously, so we deny these.)
d) Otherwise, label “Approved.”
(Rationale: Everything else in 13–16 is generally approved unless it meets the exceptions above.)
Remember: Apply these rules in order and output only “Approved” or “Denied.”
Confusion Matrix:
Predicted Approved Predicted Denied
Actual Approved 7 2
Actual Denied 2 9
Accuracy: 0.800
Precision: 0.778
Recall: 0.778
F1 Score: 0.778
Examples for Correctly predicted Approved: (Correct answer: Approved, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 12.904642
ParadoxCount: 5.0420074
Examples for Falsely predicted Denied when it should have been Approved: (Correct answer: Approved, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 14.890128
ParadoxCount: 6.380288
Examples for Falsely predicted Approved when it should have been Denied: (Correct answer: Denied, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 8.337469
ParadoxCount: 6.267838
Examples for Correctly predicted Denied: (Correct answer: Denied, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 11.297589
ParadoxCount: 3.1243498
Round ID: 161
Prompt used:
Choose randomly
Confusion Matrix:
Predicted Approved Predicted Denied
Actual Approved 6 3
Actual Denied 7 4
Accuracy: 0.500
Precision: 0.462
Recall: 0.667
F1 Score: 0.545
Examples for Correctly predicted Approved: (Correct answer: Approved, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 10.313137
ParadoxCount: 6.5179386
Examples for Falsely predicted Denied when it should have been Approved: (Correct answer: Approved, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 16.56909
ParadoxCount: 7.107604
Examples for Falsely predicted Approved when it should have been Denied: (Correct answer: Denied, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 10.494729
ParadoxCount: 1.0958244
Examples for Correctly predicted Denied: (Correct answer: Denied, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 11.29754
ParadoxCount: 2.2446613
| Predicted + | Predicted - | |
|---|---|---|
| Actual + | 7 | 2 |
| Actual - | 4 | 7 |
Accuracy 0.700, Precision 0.636, Recall 0.778, F1 0.700