Non-strategic Econometrics (for Initial Play)
Daniel Chui, Jason Hartline, James R. Wright

TL;DR
This paper demonstrates that incorporating non-strategic behavior models improves the accuracy of preference estimation in behavioral game theory, outperforming traditional equilibrium models like Nash and QRE.
Contribution
It introduces quantal-linear4 and an augmented QRE+L0 model to better capture non-strategic behavior, enhancing preference estimation accuracy.
Findings
QRE+L0 improves preference estimation over standard QRE.
Non-strategic behavior modeling is crucial for accurate agent preference inference.
Traditional equilibrium models can misestimate preferences when non-strategic actions are ignored.
Abstract
Modelling agent preferences has applications in a range of fields including economics and increasingly, artificial intelligence. These preferences are not always known and thus may need to be estimated from observed behavior, in which case a model is required to map agent preferences to behavior, also known as structural estimation. Traditional models are based on the assumption that agents are perfectly rational: that is, they perfectly optimize and behave in accordance with their own interests. Work in the field of behavioral game theory has shown, however, that human agents often make decisions that are imperfectly rational, and the field has developed models that relax the perfect rationality assumption. We apply models developed for predicting behavior towards estimating preferences and show that they outperform both traditional and commonly used benchmark models on data collected…
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Taxonomy
TopicsComplex Systems and Time Series Analysis
