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User b7612ad002661578221d05a0a8bd3e0c Responses to survey 178

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User ID: b7612ad002661578221d05a0a8bd3e0c

Survey ID: 178

Ideal budget: [50, 30, 20]
⏱️ Response Time: 123s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 80%
kl (Rank) 20%
Pairs List
Pair #1
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
✓ [55, 10, 35] cosine_similarity: {best: 0.92, worst: 0.87}
[30, 55, 15] kl: {best: -0.13, worst: -0.17}
Pair #2
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
[70, 10, 20] cosine_similarity: {best: 0.93, worst: 0.89}
✓ [30, 50, 20] kl: {best: -0.10, worst: -0.16}
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
[75, 10, 15] cosine_similarity: {best: 0.91, worst: 0.87}
✓ [30, 55, 15] kl: {best: -0.13, worst: -0.18}
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
✓ [80, 10, 10] cosine_similarity: {best: 0.90, worst: 0.84}
[25, 55, 20] kl: {best: -0.16, worst: -0.23}
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
✓ [70, 10, 20] cosine_similarity: {best: 0.93, worst: 0.89}
[30, 30, 40] kl: {best: -0.12, worst: -0.16}
Pair #6
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
✓ [55, 10, 35] cosine_similarity: {best: 0.92, worst: 0.89}
[35, 55, 10] kl: {best: -0.14, worst: -0.17}
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
✓ [80, 10, 10] cosine_similarity: {best: 0.90, worst: 0.86}
[25, 35, 40] kl: {best: -0.16, worst: -0.23}
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
✓ [50, 10, 40] cosine_similarity: {best: 0.90, worst: 0.86}
[30, 25, 45] kl: {best: -0.15, worst: -0.19}
Pair #9
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
✓ [65, 10, 25] cosine_similarity: {best: 0.93, worst: 0.91}
[35, 25, 40] kl: {best: -0.09, worst: -0.15}
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
✓ [65, 10, 25] cosine_similarity: {best: 0.93, worst: 0.91}
[30, 35, 35] kl: {best: -0.10, worst: -0.15}