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User f57773f831ddc0443c577c56c01d1f1e Responses to survey 182

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User ID: f57773f831ddc0443c577c56c01d1f1e

Survey ID: 182

Ideal budget: [35, 25, 25, 15]
⏱️ Response Time: 180s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 90%
kl (Rank) 10%
Pairs List
Pair #1
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
✓ [70, 10, 10, 10] cosine_similarity: {best: 0.83, worst: 0.78}
[20, 20, 15, 45] kl: {best: -0.21, worst: -0.28}
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
✓ [70, 10, 10, 10] cosine_similarity: {best: 0.83, worst: 0.78}
[20, 15, 20, 45] kl: {best: -0.21, worst: -0.28}
Pair #3
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
[45, 10, 10, 35] cosine_similarity: {best: 0.85, worst: 0.82}
✓ [20, 15, 55, 10] kl: {best: -0.19, worst: -0.24}
Pair #4
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
✓ [45, 10, 10, 35] cosine_similarity: {best: 0.85, worst: 0.82}
[20, 55, 15, 10] kl: {best: -0.19, worst: -0.24}
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
✓ [55, 10, 10, 25] cosine_similarity: {best: 0.87, worst: 0.84}
[20, 20, 20, 40] kl: {best: -0.16, worst: -0.22}
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
✓ [50, 10, 10, 30] cosine_similarity: {best: 0.87, worst: 0.83}
[20, 15, 25, 40] kl: {best: -0.18, worst: -0.23}
Pair #7
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
✓ [50, 10, 10, 30] cosine_similarity: {best: 0.87, worst: 0.83}
[20, 25, 15, 40] kl: {best: -0.18, 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
✓ [65, 10, 10, 15] cosine_similarity: {best: 0.85, worst: 0.82}
[20, 55, 15, 10] kl: {best: -0.19, worst: -0.24}
Pair #9
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
✓ [65, 10, 10, 15] cosine_similarity: {best: 0.85, worst: 0.82}
[20, 15, 55, 10] kl: {best: -0.19, worst: -0.24}
Pair #10
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
✓ [50, 10, 10, 30] cosine_similarity: {best: 0.87, worst: 0.83}
[15, 20, 50, 15] kl: {best: -0.18, worst: -0.23}