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User 0a36c70d3f3a6818ec21db502b7123ad Responses to survey 182

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User ID: 0a36c70d3f3a6818ec21db502b7123ad

Survey ID: 182

Ideal budget: [50, 0, 0, 50]
⏱️ Response Time: 26s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 0%
kl (Rank) 100%
Pairs List
Pair #1
Original choice: Option 2
Pair Score: 0.33
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[0, 5, 10, 85] cosine_similarity: {best: 0.70, worst: 0.43}
✓ [20, 0, 60, 20] kl: {best: -0.92, worst: -10.90}
Pair #2
Original choice: Option 1
Pair Score: 0.33
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[85, 10, 5, 0] cosine_similarity: {best: 0.70, worst: 0.43}
✓ [20, 0, 60, 20] kl: {best: -0.92, worst: -10.90}
Pair #3
Original choice: Option 1
Pair Score: 0.33
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[85, 5, 10, 0] cosine_similarity: {best: 0.70, worst: 0.43}
✓ [20, 0, 60, 20] kl: {best: -0.92, worst: -10.90}
Pair #4
Original choice: Option 2
Pair Score: 0.33
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[0, 10, 5, 85] cosine_similarity: {best: 0.70, worst: 0.43}
✓ [20, 60, 0, 20] kl: {best: -0.92, worst: -10.90}
Pair #5
Original choice: Option 2
Pair Score: 0.33
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[0, 5, 10, 85] cosine_similarity: {best: 0.70, worst: 0.43}
✓ [20, 60, 0, 20] kl: {best: -0.92, worst: -10.90}
Pair #6
Original choice: Option 2
Pair Score: 0.33
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[0, 10, 5, 85] cosine_similarity: {best: 0.70, worst: 0.43}
✓ [20, 0, 60, 20] kl: {best: -0.92, worst: -10.90}
Pair #7
Original choice: Option 1
Pair Score: 0.33
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[85, 10, 5, 0] cosine_similarity: {best: 0.70, worst: 0.43}
✓ [20, 60, 0, 20] kl: {best: -0.92, worst: -10.90}
Pair #8
Original choice: Option 2
Pair Score: 0.33
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[85, 5, 10, 0] cosine_similarity: {best: 0.70, worst: 0.43}
✓ [20, 60, 0, 20] kl: {best: -0.92, worst: -10.90}
Pair #9
Original choice: Option 2
Pair Score: 0.33
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[90, 10, 0, 0] cosine_similarity: {best: 0.70, worst: 0.43}
✓ [20, 0, 60, 20] kl: {best: -0.92, worst: -10.87}
Pair #10
Original choice: Option 1
Pair Score: 0.33
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[0, 10, 0, 90] cosine_similarity: {best: 0.70, worst: 0.43}
✓ [20, 60, 0, 20] kl: {best: -0.92, worst: -10.87}