Beyond the Black Box: Interpretable Models of Human Randomisation Failures
Quick summary
arXiv:2608.07220v1 Announce Type: new Abstract: Mixed strategy equilibrium predicts i.i.d play: past actions should not help predict future decisions. Human players, however, systematically depart from this benchmark, and in O'Neill's zero sum card game, these departures can be predicted by black box sequence models such as LSTMs. This paper asks whether that predictive power can be achieved by transparent alternatives that also reveal the behavioural structure behind it. Using 84,060 decisions from 2,802 pairs, the analysis first benchmarks naive and behavioral models against interpretable ma
Key takeaways
- arXiv:2608.07220v1 Announce Type: new Abstract: Mixed strategy equilibrium predicts i.i.d play: past actions should not help predict future decisions.
- Human players, however, systematically depart from this benchmark, and in O'Neill's zero sum card game, these departures can be predicted by black box sequence models such as LSTMs.
- This paper asks whether that predictive power can be achieved by transparent alternatives that also reveal the behavioural structure behind it.
Why it matters
“Beyond the Black Box: Interpretable Models of Human Randomisation Failures” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
Member comments