What Actuaries Might Learn About Life

What might we learn from climate forecasts?

Most climate models are large dynamical systems involving a million (or more) variables on big computers. Given that they are nonlinear and not perfect, what can we expect to learn from them about the earth's climate? How can we determine which aspects of their output might be useful and which are noise? And how should we distribute resources between making them "better," estimating variables o...

Developing Thoughts About What Might Have Been

Recent research has changed how developmental psychologists understand counterfactual thinking or thoughts of what might have been. Evidence suggests that counterfactual thinking develops over an extended period into at least middle childhood, depends on domain-general processes including executive function and language, and dissociates from counterfactual emotions such as regret. In this artic...

New Econ for Life Actuaries

In an editorial in ASTIN BULLETIN, Hans Bühlmann (2002) suggests it is time to change the teaching of life insurance theory towards the real life challenges of that industry. The following note is a response to this editorial. In Bergen we have partially taught the NUMAT, or the NUMeraire based Actuarial Teaching since the beginning of the 90ties at the Norwegian School of Economics and Busines...

What might we learn from Climate Fore asts ?

What Do Deep CNNs Learn About Objects?

Deep convolutional neural networks learn extremely powerful image representations, yet most of that power is hidden in the millions of deep-layer parameters. What exactly do these parameters represent? Recent work has started to analyse CNN representations, finding that, e.g., they are invariant to some 2D transformations Fischer et al. (2014), but are confused by particular types of image nois...