In his interview, behavorial economist David Laibson emphasizes the importance of understanding estimates of uncertainty as both point estimates and distributions of possibilities. In particular, these distributions may have tail events (i.e. low probability events) that happen more likely than expected by the models. My question is if tails of the distribution on events are fatter (i.e. more likely) than expected, then why are models not being corrected so that the tails are more accurate? For example, tail events in the stock market, such as market crashes or bubbles, may be more likely than expected than in a symmetric Normal distribution of events. Or is the notion of a tail event an example of bias or small sample size? Because we observe that some event with very low probability occurred, we may think it should have higher probability, but our sample size may be too small to compare the empirically observed frequentist probability to the theoretical probability given by the model.

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Hi Vincent, that is a fantastic argument and something I have been thinking about too. I think in the case of the stock market, it might be relevant to distinguish crashes occurring from external shocks (natural disasters (earthquake)/pandemics and/or supply shocks (Suez Canal/Oil Crisis of the 1970s)) and crashes occurring endogenously (08 financial crisis). One can argue that the external shocks are random events but perhaps in a globalized world with rising nationalism and climate change intensifying we have good reason to fatten the tails for such external shocks. However, for financial crisis occurring endogenously because certain companies have taken far too much leverage at inflated asset prices, that we are much better off building a model to identify the risk of a correction leading to a run on the short-term debt markets and thus a subsequent fire sale.