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User d29d86dd1f38475cb14d1f8f40a5043f Responses

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Survey Response Breakdown - Survey 178

User ID Submitted At Duration Ideal budget cosine_similaritykl View Response
d29d86dd1... d29d86dd1f38475cb14d1f8f40a5043f 09-07-2026 11:42 226s [60, 20, 20] 70%30% View Response

User ID: d29d86dd1f38475cb14d1f8f40a5043f

Survey ID: 178

Ideal budget: [60, 20, 20]
⏱️ Response Time: 226s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 70%
kl (Rank) 30%
Pairs List
Pair #1
Original choice: Option 1
Pair Score: 0.06
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: 0.96, worst: 0.95}
[45, 20, 35] kl: {best: -0.06, worst: -0.10}
Pair #2
Original choice: Option 1
Pair Score: 0.06
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: 0.96, worst: 0.95}
[45, 35, 20] kl: {best: -0.06, worst: -0.10}
Pair #3
Original choice: Option 1
Pair Score: 0.05
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: 0.98, worst: 0.96}
[50, 35, 15] kl: {best: -0.06, worst: -0.06}
Pair #4
Original choice: Option 1
Pair Score: 0.05
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, 15, 10] cosine_similarity: {best: 0.98, worst: 0.96}
[50, 35, 15] kl: {best: -0.06, worst: -0.06}
Pair #5
Original choice: Option 1
Pair Score: 0.05
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: 0.96, worst: 0.96}
✓ [50, 15, 35] kl: {best: -0.06, worst: -0.10}
Pair #6
Original choice: Option 2
Pair Score: 0.05
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: 0.98, worst: 0.96}
[50, 15, 35] kl: {best: -0.06, worst: -0.06}
Pair #7
Original choice: Option 1
Pair Score: 0.05
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, 15, 10] cosine_similarity: {best: 0.98, worst: 0.96}
[50, 15, 35] kl: {best: -0.06, worst: -0.06}
Pair #8
Original choice: Option 1
Pair Score: 0.05
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: 0.96, worst: 0.96}
[50, 35, 15] kl: {best: -0.06, worst: -0.10}
Pair #9
Original choice: Option 2
Pair Score: 0.04
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: 0.55, worst: 0.51}
✓ [15, 10, 75] kl: {best: -0.71, worst: -0.76}
Pair #10
Original choice: Option 2
Pair Score: 0.04
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: 0.55, worst: 0.51}
✓ [15, 75, 10] kl: {best: -0.71, worst: -0.76}