Name
Abstract | ||
|---|---|---|
Humans are routinely asked to evaluate the performance of other individuals, separating success from failure and affecting outcomes from science to education and sports. Yet, in many contexts, the metrics driving the human evaluation process remain unclear. Here we analyse a massive dataset capturing playersu0027 evaluations by human judges to explore human perception of performance in soccer, the worldu0027s most popular sport. We use machine learning to design an artificial judge which accurately reproduces human evaluation, allowing us to demonstrate how human observers are biased towards diverse contextual features. By investigating the structure of the artificial judge, we uncover the aspects of the playersu0027 behavior which attract the attention of human judges, demonstrating that human evaluation is based on a noticeability heuristic where only feature values far from the norm are considered to rate an individualu0027s performance. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Physics and Society | Heuristic,Norm (social),Cognitive psychology,Artificial intelligence,Perception,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1712.02224 | 2 |
PageRank | References | Authors |
0.38 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pappalardo, L. | 1 | 43 | 8.77 |
| Paolo Cintia | 2 | 48 | 6.07 |
| Dino Pedreschi | 3 | 3083 | 244.47 |
| Fosca Giannotti | 4 | 2948 | 253.39 |
| Albert-lászló Barabási | 5 | 4649 | 1107.35 |