Modelling dataset bias in machine-learned theories of economic decision-making.

ABSTRACT: Normative and descriptive models have long vied to explain and predict human risky choices, such as those between goods or gambles. A recent study reported the discovery of a new, more accurate model of human decision-making by training neural networks on a new online large-scale dataset, choices13k. Here we systematically analyse the relationships between several models and datasets using machine-learning methods and find evidence for dataset bias. Because participants' choices in stochastically dominated gambles were consistently skewed towards equipreference in the choices13k dataset, we hypothesized that this reflected increased decision noise. Indeed, a probabilistic generative model adding structured decision noise to a neural network trained on data from a laboratory study transferr

SUBMITTER: Thomas T 

PROVIDER: S-EPMC11045447 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature