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User 06f751aa4028a0ebb1e5fbf2c88c77d1 Responses

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Survey Response Breakdown - Survey 178

User ID Submitted At Duration Ideal budget cosine_similaritykl View Response
06f751aa4... 06f751aa4028a0ebb1e5fbf2c88c77d1 09-07-2026 18:34 60s [25, 50, 25] 100%0% View Response

User ID: 06f751aa4028a0ebb1e5fbf2c88c77d1

Survey ID: 178

Ideal budget: [25, 50, 25]
⏱️ Response Time: 60s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 100%
kl (Rank) 0%
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
✓ [35, 55, 10] cosine_similarity: {best: 0.96, worst: 0.94}
[40, 35, 25] kl: {best: -0.06, worst: -0.10}
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
✓ [10, 55, 35] cosine_similarity: {best: 0.96, worst: 0.94}
[25, 35, 40] kl: {best: -0.06, worst: -0.10}
Pair #3
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
✓ [10, 55, 35] cosine_similarity: {best: 0.96, worst: 0.94}
[40, 35, 25] kl: {best: -0.06, worst: -0.10}
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
✓ [35, 55, 10] cosine_similarity: {best: 0.96, worst: 0.94}
[25, 35, 40] kl: {best: -0.06, worst: -0.10}
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
✓ [10, 50, 40] cosine_similarity: {best: 0.94, worst: 0.91}
[20, 35, 45] kl: {best: -0.09, worst: -0.11}
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
✓ [25, 65, 10] cosine_similarity: {best: 0.96, worst: 0.94}
[40, 35, 25] kl: {best: -0.06, worst: -0.10}
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
✓ [40, 50, 10] cosine_similarity: {best: 0.94, worst: 0.91}
[45, 35, 20] kl: {best: -0.09, worst: -0.11}
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
✓ [25, 65, 10] cosine_similarity: {best: 0.96, worst: 0.94}
[25, 35, 40] kl: {best: -0.06, worst: -0.10}
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
✓ [10, 50, 40] cosine_similarity: {best: 0.94, worst: 0.91}
[45, 35, 20] kl: {best: -0.09, worst: -0.11}
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
✓ [10, 65, 25] cosine_similarity: {best: 0.96, worst: 0.94}
[40, 35, 25] kl: {best: -0.06, worst: -0.10}