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‘Homo economicus’ as an intuitive statistician (2): Bayesian diagnostic learning

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Abstract

The bounded rationality programme views the economy as a society of intuitive statisticians. The key for the success of this programme is the existence of a ‘tight enough’ theory of statistical inference. We have so far shown that there is no entirely data-driven algorithm that receives a finite sample of data and yields the model that best approximates the process generating the data. Learning an interpretable model of a choice situation requires starting with a parametric probability model. To analyse the programme further, we now examine the possibility of a ‘tight enough’ theory of learning within the general framework of the Bayesian theory, which is primarily a theory of parametric inference.

Keywords

Predictive Distribution Bounded Rationality Prior Density Empirical Adequacy Distribution Family 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Reza Salehnejad 2007

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