Ensemble

Dataset: timetravel_insurance

Models

Model Narratives

anthropic

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: 13.023456
	ParadoxCount: 6.9185414


Examples for Correctly predicted Denied: (Correct answer: Denied, What the previous set of rules predicted: Denied)
  Entity Data:
	TimelineDeviation: 11.29754
	ParadoxCount: 2.2446613


anthropic10

Round ID: 228
Prompt used:
	Classification Rules:
	
	Approve an entity if:
	1. TimelineDeviation > 9 AND
	2. (ParadoxCount > 4 OR 
	    (TimelineDeviation > 14 AND ParadoxCount > 2))
	
	Deny an entity if:
	1. TimelineDeviation < 8 OR
	2. ParadoxCount < 3 OR
	3. (TimelineDeviation < 12 AND ParadoxCount < 5)
	
	Special Considerations:
	- Entities with TimelineDeviation > 14 should be given more lenient scrutiny
	- Negative ParadoxCount values automatically trigger denial
	- Borderline cases between 8-12 TimelineDeviation require careful individual assessment
	
	Rationale:
	- Lowered initial TimelineDeviation threshold to 9
	- Added flexibility for high TimelineDeviation entities
	- Introduced more nuanced ParadoxCount criteria
	- Provided clear rules for edge cases

Confusion Matrix:
                Predicted Approved   Predicted Denied    
Actual Approved                    8                    1
Actual Denied                      2                    9

Accuracy: 0.850
Precision: 0.800
Recall: 0.889
F1 Score: 0.842

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: 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: 13.023456
	ParadoxCount: 6.9185414


Examples for Correctly predicted Denied: (Correct answer: Denied, What the previous set of rules predicted: Denied)
  Entity Data:
	TimelineDeviation: 11.322671
	ParadoxCount: 1.2654697


random

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: 11.653055
	ParadoxCount: 6.0099745


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: 13.023456
	ParadoxCount: 6.9185414


Examples for Correctly predicted Denied: (Correct answer: Denied, What the previous set of rules predicted: Denied)
  Entity Data:
	TimelineDeviation: 9.459619
	ParadoxCount: 6.900848


Ensemble Confusion Matrix

Predicted +Predicted -
Actual +81
Actual -38

Accuracy 0.800, Precision 0.727, Recall 0.889, F1 0.800