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Risk and punishment revisited. Errors in variables and in the lab

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Engel,  Christoph
Max Planck Institute for Research on Collective Goods, Max Planck Society;

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Citation

Engel, C., & Kirchkamp, O. (2016). Risk and punishment revisited. Errors in variables and in the lab.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002A-FF60-C
Abstract
We provide an example for an errors in variables problem which might be often neglected but which is quite common in lab experimental practice: In one task, attitude towards risk is measured, in another task participants behave in a way that can possibly be explained by their risk attitude. How should we deal with inconsistent behaviour in the risk task? Ignoring these observations entails two biases: An errors in variables bias and a selection bias. We argue that inconsistent observations should be exploited to address the errors in variables problem, which can easily be done within a Bayesian framework.