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User 6671708370803e84fcff4a2a5445ef5e Responses

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Survey Response Breakdown - Survey 178

User ID Submitted At Duration Ideal budget cosine_similaritykl View Response
667170837... 6671708370803e84fcff4a2a5445ef5e 09-07-2026 17:28 103s [30, 35, 35] 80%20% View Response

User ID: 6671708370803e84fcff4a2a5445ef5e

Survey ID: 178

Ideal budget: [30, 35, 35]
⏱️ Response Time: 103s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 80%
kl (Rank) 20%
Pairs List
Pair #1
Original choice: Option 2
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
✓ [10, 45, 45] cosine_similarity: {best: 0.93, worst: 0.91}
[50, 30, 20] kl: {best: -0.10, worst: -0.15}
Pair #2
Original choice: Option 2
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
✓ [10, 45, 45] cosine_similarity: {best: 0.93, worst: 0.91}
[50, 20, 30] kl: {best: -0.10, worst: -0.15}
Pair #3
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
✓ [35, 55, 10] cosine_similarity: {best: 0.87, worst: 0.83}
[60, 20, 20] kl: {best: -0.18, worst: -0.23}
Pair #4
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: 0.87, worst: 0.83}
[60, 20, 20] kl: {best: -0.18, worst: -0.23}
Pair #5
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
✓ [10, 35, 55] cosine_similarity: {best: 0.90, worst: 0.88}
[55, 25, 20] kl: {best: -0.13, worst: -0.17}
Pair #6
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
✓ [10, 35, 55] cosine_similarity: {best: 0.90, worst: 0.88}
[55, 20, 25] kl: {best: -0.13, worst: -0.17}
Pair #7
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
✓ [10, 55, 35] cosine_similarity: {best: 0.90, worst: 0.88}
[55, 20, 25] kl: {best: -0.13, worst: -0.17}
Pair #8
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
✓ [10, 55, 35] cosine_similarity: {best: 0.90, worst: 0.88}
[55, 25, 20] kl: {best: -0.13, worst: -0.17}
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
[50, 40, 10] cosine_similarity: {best: 0.87, worst: 0.83}
✓ [60, 20, 20] kl: {best: -0.18, 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, 40] cosine_similarity: {best: 0.87, worst: 0.83}
✓ [60, 20, 20] kl: {best: -0.18, worst: -0.24}