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User a0892877f7326ce1acab586f43028433 Responses to survey 178

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User ID: a0892877f7326ce1acab586f43028433

Survey ID: 178

Ideal budget: [40, 30, 30]
⏱️ Response Time: 224s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 90%
kl (Rank) 10%
Pairs List
Pair #1
Original choice: Option 1
Pair Score: 0.13
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
[50, 10, 40] cosine_similarity: {best: 0.93, worst: 0.91}
✓ [25, 25, 50] kl: {best: -0.09, worst: -0.15}
Pair #2
Original choice: Option 2
Pair Score: 0.13
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
✓ [50, 40, 10] cosine_similarity: {best: 0.93, worst: 0.91}
[25, 50, 25] kl: {best: -0.09, worst: -0.15}
Pair #3
Original choice: Option 2
Pair Score: 0.13
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
✓ [50, 10, 40] cosine_similarity: {best: 0.93, worst: 0.91}
[25, 50, 25] kl: {best: -0.09, worst: -0.15}
Pair #4
Original choice: Option 1
Pair Score: 0.13
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
✓ [50, 40, 10] cosine_similarity: {best: 0.93, worst: 0.91}
[25, 25, 50] kl: {best: -0.09, worst: -0.15}
Pair #5
Original choice: Option 2
Pair Score: 0.12
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
✓ [45, 10, 45] cosine_similarity: {best: 0.92, worst: 0.89}
[20, 30, 50] kl: {best: -0.12, worst: -0.16}
Pair #6
Original choice: Option 2
Pair Score: 0.12
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
✓ [45, 10, 45] cosine_similarity: {best: 0.92, worst: 0.89}
[20, 50, 30] kl: {best: -0.12, worst: -0.16}
Pair #7
Original choice: Option 1
Pair Score: 0.12
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
✓ [60, 10, 30] cosine_similarity: {best: 0.91, worst: 0.88}
[25, 20, 55] kl: {best: -0.13, worst: -0.17}
Pair #8
Original choice: Option 2
Pair Score: 0.12
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
✓ [60, 30, 10] cosine_similarity: {best: 0.91, worst: 0.88}
[25, 55, 20] kl: {best: -0.13, worst: -0.17}
Pair #9
Original choice: Option 1
Pair Score: 0.12
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
✓ [60, 10, 30] cosine_similarity: {best: 0.91, worst: 0.88}
[25, 55, 20] kl: {best: -0.13, worst: -0.17}
Pair #10
Original choice: Option 2
Pair Score: 0.12
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
✓ [60, 30, 10] cosine_similarity: {best: 0.91, worst: 0.88}
[25, 20, 55] kl: {best: -0.13, worst: -0.17}