עברית English

User dd0db5c6c3a6578cd43656583f4ff136 Responses to survey 178

← Back to Responses List

User ID: dd0db5c6c3a6578cd43656583f4ff136

Survey ID: 178

Ideal budget: [50, 25, 25]
⏱️ Response Time: 96s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 60%
kl (Rank) 40%
Pairs List
Pair #1
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, 35] cosine_similarity: {best: 0.96, worst: 0.94}
[35, 25, 40] 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
✓ [55, 35, 10] cosine_similarity: {best: 0.96, worst: 0.94}
[35, 25, 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
✓ [55, 10, 35] cosine_similarity: {best: 0.96, worst: 0.94}
[35, 40, 25] kl: {best: -0.06, worst: -0.10}
Pair #4
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, 35, 10] cosine_similarity: {best: 0.96, worst: 0.94}
[35, 40, 25] 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
[65, 10, 25] cosine_similarity: {best: 0.96, worst: 0.94}
✓ [35, 40, 25] kl: {best: -0.06, worst: -0.10}
Pair #6
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, 25, 10] cosine_similarity: {best: 0.96, worst: 0.94}
✓ [35, 40, 25] kl: {best: -0.06, worst: -0.10}
Pair #7
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, 25] cosine_similarity: {best: 0.96, worst: 0.94}
✓ [35, 25, 40] kl: {best: -0.06, worst: -0.10}
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, 25, 10] cosine_similarity: {best: 0.96, worst: 0.94}
✓ [35, 25, 40] kl: {best: -0.06, worst: -0.10}
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: 0.94, worst: 0.91}
[35, 20, 45] 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
✓ [50, 10, 40] cosine_similarity: {best: 0.94, worst: 0.91}
[35, 20, 45] kl: {best: -0.09, worst: -0.11}