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User c71f8dfeccc4d9448089f016336a777c Responses to survey 178

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User ID: c71f8dfeccc4d9448089f016336a777c

Survey ID: 178

Ideal budget: [20, 30, 50]
⏱️ Response Time: 62s
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
✓ [20, 10, 70] cosine_similarity: {best: 0.93, worst: 0.89}
[20, 50, 30] kl: {best: -0.10, worst: -0.16}
Pair #2
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, 10, 55] cosine_similarity: {best: 0.92, worst: 0.87}
[15, 55, 30] kl: {best: -0.13, worst: -0.17}
Pair #3
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
✓ [15, 10, 75] cosine_similarity: {best: 0.91, worst: 0.87}
[15, 55, 30] 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
✓ [10, 10, 80] cosine_similarity: {best: 0.90, worst: 0.84}
[20, 55, 25] kl: {best: -0.16, worst: -0.23}
Pair #5
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
✓ [20, 10, 70] cosine_similarity: {best: 0.93, worst: 0.89}
[40, 30, 30] 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
✓ [35, 10, 55] cosine_similarity: {best: 0.92, worst: 0.89}
[10, 55, 35] kl: {best: -0.14, worst: -0.17}
Pair #7
Original choice: Option 2
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
✓ [10, 10, 80] cosine_similarity: {best: 0.90, worst: 0.86}
[40, 35, 25] kl: {best: -0.16, worst: -0.23}
Pair #8
Original choice: Option 2
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
✓ [40, 10, 50] cosine_similarity: {best: 0.90, worst: 0.86}
[45, 25, 30] 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
✓ [35, 10, 55] cosine_similarity: {best: 0.92, worst: 0.89}
[40, 30, 30] kl: {best: -0.12, worst: -0.17}
Pair #10
Original choice: Option 2
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
✓ [25, 10, 65] cosine_similarity: {best: 0.93, worst: 0.91}
[40, 25, 35] kl: {best: -0.09, worst: -0.15}