Research map / Large language models / Language, safety and interpretability
Deep Learning for Predicting Human Strategic Behavior
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add pooling units (row/column max aggregates) to the matrix-unit feature layers (ablation) | Deep matrix-unit network (50,50) · Human strategic behavior dataset | NLL (Test Loss) | Helped |
| Increase action response layers from one to multiple (iterative reasoning depth) (ablation) | Deep matrix-unit network · Human strategic behavior dataset | NLL (Test Loss) | Hurt |
| Add a second hidden layer (3-layer networks vs. single hidden layer) (ablation) | Deep matrix-unit network · Human strategic behavior dataset | NLL (Test Loss) | Helped |
| Increase to four hidden layers (50,50,50 or 100,100,100) (ablation) | Deep matrix-unit network · Human strategic behavior dataset | NLL (Test Loss) | Hurt |
| Replace hand-crafted features with feature-free deep matrix-unit architecture (vs QCH with hand-crafted features) | QCH model · Human strategic behavior dataset | NLL (Test Loss) | Helped |
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