Dispatch Desk 路 costly-decision risk
Predicts, when a crisis scene opens, the probability that this player will pick the costly option, from how they played so far. The game uses it to offer extra support.
Intended use
- Use: inside the Dispatch Desk teaching game, to decide whether to show a support card before a crisis decision.
- Not for: grading students, judging real dispatchers, or any decision about a person outside the game.
- Users: students playing the game in a browser; the model runs on their device (onnxruntime-web).
Model
- scikit-learn
Pipeline(StandardScaler -> LogisticRegression), seed 13 - Trained with Python 3.13.17, scikit-learn 1.9.1
- Exported to ONNX (opset 17, zipmap off). Input
inputfloat32[n, 8]in the order below, outputprobabilities[n, 2](column 1 = costly). - Decision threshold tau = 0.5 (fixed in the spec, not tuned on the test split)
Features
| name | meaning | example |
|---|---|---|
seviye |
Level (0 Just starting, 1 Know a bit, 2 Experienced) | 1.0 |
sahne_no |
Crisis number (1-3) | 2.0 |
canli_bekleme_ort_dk |
Average call wait in the previous live phase (sim. minutes) | 3.11 |
canli_en_yakin_orani |
Share of assignments to the closest crew (0-1) | 0.6 |
canli_bekleyen |
Calls still waiting at the alarm | 1.0 |
gorev_yanlis |
Field tasks answered wrongly so far | 1.0 |
onceki_maliyetli |
Costly choices in earlier crises (0-2) | 0.0 |
onceki_karar_suresi_sn |
Seconds spent on the previous crisis (0 for Crisis 1) | 3.5 |
Coefficients (standardized features)
| feature | weight |
|---|---|
seviye |
-0.146 |
sahne_no |
-0.138 |
canli_bekleme_ort_dk |
+0.754 |
canli_en_yakin_orani |
+0.198 |
canli_bekleyen |
+0.294 |
gorev_yanlis |
+0.563 |
onceki_maliyetli |
+0.452 |
onceki_karar_suresi_sn |
-0.710 |
intercept |
+0.491 |
Training data
data/oturumlar.csv from the GitHub repository: 648 rows, 16 real sessions (48 rows) and 600 synthetic rows from a seeded rule (data/generate_synthetic.py, seed 13). No personal data: the session ID is random and no name, location or device field is collected.
Evaluation (stratified 30 % hold-out, seed 13)
Acceptance gate: ROC AUC >= 0.7 (committed in spec.md before training).
| metric | value |
|---|---|
| roc_auc | 0.761 |
| accuracy | 0.7179 |
| f1 | 0.7737 |
| brier | 0.1926 |
| roc_auc_real_only | 0.2188 |
| n_test_real | 16 |
| n_rows | 648 |
| n_rows_real | 48 |
| n_sessions_real | 16 |
| positive_rate | 0.5802 |
| onnx_max_abs_diff | 8.49e-08 |
Limitations and risks
- Most rows are synthetic, so the weights partly reflect the generator's rule. The real-only AUC above is the honest number, and it rests on few sessions.
- Real players were friends of the author, which is a narrow and self-selected group.
- Players who know the model exists could play differently. The model only adds support; it never removes help or changes the outcome.
- The weights are public in this file, so anyone can read them. That is fine here: nothing about the model is secret (see
RELEASE.md).
Use
https://huggingface.co/<user>/<repo>/resolve/<commit>/model.onnx
Always pin a commit, never main, so a new upload cannot change a running app.
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