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User cba1024cd31b574251e7e001d39d79e4 Responses to survey 178

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User ID: cba1024cd31b574251e7e001d39d79e4

Survey ID: 178

Ideal budget: [25, 35, 40]
⏱️ Response Time: 117s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 30%
kl (Rank) 70%
Pairs List
Pair #1
Original choice: Option 1
Pair Score: 0.14
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: 0.87, worst: 0.82}
✓ [55, 20, 25] kl: {best: -0.19, worst: -0.24}
Pair #2
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
✓ [25, 10, 65] cosine_similarity: {best: 0.87, worst: 0.82}
[55, 20, 25] kl: {best: -0.19, worst: -0.24}
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
[35, 10, 55] cosine_similarity: {best: 0.88, worst: 0.86}
✓ [50, 30, 20] kl: {best: -0.16, worst: -0.23}
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
✓ [40, 10, 50] cosine_similarity: {best: 0.88, worst: 0.82}
[55, 20, 25] kl: {best: -0.19, worst: -0.23}
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
[40, 10, 50] cosine_similarity: {best: 0.88, worst: 0.86}
✓ [50, 30, 20] kl: {best: -0.16, worst: -0.23}
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
[30, 10, 60] cosine_similarity: {best: 0.88, worst: 0.82}
✓ [55, 20, 25] kl: {best: -0.19, worst: -0.23}
Pair #7
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
[40, 10, 50] cosine_similarity: {best: 0.88, worst: 0.84}
✓ [15, 65, 20] kl: {best: -0.19, worst: -0.23}
Pair #8
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
[30, 10, 60] cosine_similarity: {best: 0.88, worst: 0.86}
✓ [50, 30, 20] kl: {best: -0.16, worst: -0.23}
Pair #9
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
✓ [35, 10, 55] cosine_similarity: {best: 0.88, worst: 0.86}
[50, 15, 35] kl: {best: -0.18, worst: -0.23}
Pair #10
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
[45, 10, 45] cosine_similarity: {best: 0.87, worst: 0.82}
✓ [55, 25, 20] kl: {best: -0.20, worst: -0.24}