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

Economics

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

T. Thomas, D. Straub, et al.

This exciting research by Tobias Thomas, Dominik Straub, Fabian Tatai, Megan Shene, Tümer Tosik, Kristian Kersting, and Constantin A. Rothkopf delves into dataset bias in economic decision-making theories. They reveal intriguing findings about how online data may introduce greater decision noise than laboratory studies, leading to enhanced predictions through a new probabilistic generative model.

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Playback language: English
Abstract
This paper investigates dataset bias in machine-learned theories of economic decision-making. The authors analyze the relationships between several models and datasets using machine-learning methods and find evidence of dataset bias stemming from increased decision noise in online datasets compared to laboratory studies. A probabilistic generative model incorporating structured decision noise improves prediction accuracy, highlighting the importance of considering data collection context in model development.
Publisher
Nature Human Behaviour
Published On
Apr 01, 2024
Authors
Tobias Thomas, Dominik Straub, Fabian Tatai, Megan Shene, Tümer Tosik, Kristian Kersting, Constantin A. Rothkopf
Tags
dataset bias
economic decision-making
machine learning
decision noise
prediction accuracy
data collection context

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