AI News, Inspiring Imagination In Data Science: Qualitative Profiling

Inspiring Imagination In Data Science: Qualitative Profiling

Overview “The only function of economic forecasting is to make astrology look respectable” John Kenneth Galbraith Predictive modeling and traditional ratemaking is an exercise of forecasting the future, whether directly or indirectly (indirectly as generalizing historical lessons to the future).

instead, they only feature an unbiased ignorance of real-world issues facing the insurance landscape.[2] Complexity science is given specific important as for ratemaking, it is important to learn the dynamics first before predicting these dynamics to hold under future.

This means, strangely, that the scientific method as we normally use it no longer works.” Unfortunately for this big data insight, the scientific method as we normally use it never did work well for even normal whole system change recognition, and especially not for rare event foresight, for the simple reason that just because something formerly couldn’t be measured didn’t make it irrelevant.

Likewise, Werther makes an amazing assertion when he says that lacking the human inputs of correct intuition, imagination and understanding, technical knowledge management approaches like “big data” is only about getting better paint and brushes (tools).

Actuaries like to build their models on the Gaussian distribution we are perhaps avoiding professional expertise by fooling ourselves by retreating to the comfort and safety of the womb of Mediocristan instead of facing Extremistan in all its unknown mystery and ambiguity.[5] To avoid being ambiguity averse, we can train ourselves to explore the unexplored.

It is not only our results that can be absurd, but our risk-averse and ambiguity-averse mentalities as well.[6] As Voltaire said “doubt is not a pleasant condition but certainty is absurd.” Aristotle explains this further: “It is the mark of an instructed mind to rest satisfied with that degree of precision which the nature of the subject limits, and not to seek exactness where only an approximation of the truth is possible.” This teaches us that we should be aware that precision implies confidence.

While point estimates are often required (we have to quote and file a specific premium), there are many cases where ranges of estimates are more appropriate.  While statistical techniques can sometimes be used to generate precise confidence intervals, mostly statistical rigor is not possible or even necessary for emerging risks.

By discussing a range of estimates, actuaries can provide more value to their stakeholders by painting a more complete picture of the potential impacts of decisions related to emerging liabilities.[7] Finally, we must ensure that actuarial output highlights fundamental questions at hand to stakeholders instead of confusing them with complicated numbers and lack of decisiveness.

The proliferation of big data, machine learning techniques and evolving of emerging risks at lightning speed has resulted in problem-solving means and aptitude which we have been previously unable to tackle, but the same advances have brought its own share of technical and mentality challenges.[9]

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