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User b9305c1b7430e158313666078e3c4f40 Responses to survey 182

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User ID: b9305c1b7430e158313666078e3c4f40

Survey ID: 182

Ideal budget: [25, 40, 20, 15]
⏱️ Response Time: 113s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 30%
kl (Rank) 70%
Pairs List
Pair #1
Original choice: Option 2
Pair Score: 0.11
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
[10, 50, 10, 30] cosine_similarity: {best: 0.91, worst: 0.86}
✓ [45, 20, 20, 15] kl: {best: -0.13, worst: -0.17}
Pair #2
Original choice: Option 2
Pair Score: 0.11
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
✓ [10, 45, 35, 10] cosine_similarity: {best: 0.92, worst: 0.89}
[45, 25, 15, 15] kl: {best: -0.10, worst: -0.13}
Pair #3
Original choice: Option 1
Pair Score: 0.11
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
[10, 50, 10, 30] cosine_similarity: {best: 0.91, worst: 0.86}
✓ [50, 25, 15, 10] kl: {best: -0.13, worst: -0.17}
Pair #4
Original choice: Option 1
Pair Score: 0.11
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
✓ [10, 55, 10, 25] cosine_similarity: {best: 0.91, worst: 0.88}
[25, 20, 35, 20] kl: {best: -0.12, worst: -0.16}
Pair #5
Original choice: Option 1
Pair Score: 0.11
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
✓ [10, 55, 10, 25] cosine_similarity: {best: 0.91, worst: 0.88}
[20, 25, 20, 35] kl: {best: -0.12, worst: -0.16}
Pair #6
Original choice: Option 2
Pair Score: 0.11
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
[10, 55, 10, 25] cosine_similarity: {best: 0.91, worst: 0.88}
✓ [30, 20, 20, 30] kl: {best: -0.13, worst: -0.16}
Pair #7
Original choice: Option 1
Pair Score: 0.10
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
[10, 70, 10, 10] cosine_similarity: {best: 0.88, worst: 0.83}
✓ [50, 20, 15, 15] kl: {best: -0.16, worst: -0.20}
Pair #8
Original choice: Option 1
Pair Score: 0.10
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
[10, 65, 10, 15] cosine_similarity: {best: 0.90, worst: 0.86}
✓ [50, 25, 15, 10] kl: {best: -0.13, worst: -0.17}
Pair #9
Original choice: Option 2
Pair Score: 0.10
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
[10, 55, 10, 25] cosine_similarity: {best: 0.91, worst: 0.88}
✓ [25, 20, 25, 30] kl: {best: -0.13, worst: -0.16}
Pair #10
Original choice: Option 1
Pair Score: 0.10
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
[10, 55, 10, 25] cosine_similarity: {best: 0.91, worst: 0.86}
✓ [45, 20, 20, 15] kl: {best: -0.13, worst: -0.16}