Predicting human decisions with behavioral theories and machine learning
š Description
1 Predicting human decisions with behavioral theories and machine learning Plonsky, Ori1a; Apel, Reuta; Ert, Eyalb; Tennenholtz, Moshea; Bourgin, Davidc; Peterson , Joshua , C.c; Reichman, Danielc; Griffiths , Thomas L.c; Russell , Stuart , J.d; Carter, Evan, C.e; Cavanagh , James , F.f; Erev, Idoa a Technion - Israel Institute of Technology ; b The Hebrew University of Jerusalem ; c Princeton University ; d University of California, Berkeley ; e United States Army Research Laboratory ; f The University of New Mexico . Abstract Behavioral decision theories aim to explain human behavior. Can they help predict it? An open tournament for prediction of human choices in fundamental economic decision tasks is presented. The results suggest that integration of certain behavioral theorie s as features in machine learning systems provides the best predictions. Surprisingly, the most useful theories for prediction build on basic properties of human and animal lea...
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Not verified in gameWhich variables this event was proposed to move, and in which direction. The magnitudes are held in the corpus but are not shown here, because they have not been verified against the shipped game. They come from pdoom-data. They describe what an event was proposed to do, not what the shipped game does with it. Most events in the corpus are flavour: they are shown for colour and do not move any game variable. Only a small minority reach the systems below, and several of the variables listed here are not read by the game at all yet. Treat this table as a design proposal under review, not as a measurement of play. Corrections and arguments are welcome — the suggestion links at the foot of this page go straight to the data repo.
| Variable | Direction | Condition |
|---|---|---|
| Research | proposed: up | Always |
| Papers | proposed: up | Always |
| Vibey Doom | proposed: up | Always |
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