 The reference class problem can be thought of as a particular case of  the model selection problem (Cheng 2009).  The model should capture the data to some extent, but not overfit the data. Random variation in the data should not be built into the model. In statistics, different criteria for model selection exists, most notably the Akaike’s Information Criterion (AIC), which is a popular measure of the trade-off between model’s fit with the data and its complexity. Consider the Shonubi case again. In order to predict the total amount of heroin Shonubi transported, a simple linear model could be used, where each reference class comes with an empirically established multiplier \(\beta\) for the expected total amount of drugs carried based on how much drugs one carried on a single trip. Picking too generic a class (say, “airline passenger”) would lower the empirical adequacy of the model, but relying on too narrow a class would incorporate random noise. A model using the class “toll collector” would clearly perform (in terms of statistical measures such as AIC) worse than, say a model based on the class “Nigerian drug courier at JFK”.