Managing limitations of prediction

 

Thrilling question whether “All Predictive Models Are Wrong?” has already second page of proposed answers in the LinkedIn, and probably will be continued for a long time. However, the other question seems to be equally important: “What to do if the predictions are not sufficiently accurate?”

Standard answer is seductive and well known in the security management – it is necessary to built a strategy to cope with uncertainty and following risk. For example, contemporary military strategies for such cases fulfil the goal “to preserve the ability to continue operations”. Narrow-minded business often develops strategy “to minimize loss”, and open-minded business develops organisation able “to benefit most of opportunities and optimize the measures against threat”.

Discussed is the case of hurricanes. No one can predict the loss due to the hurricanes with satisfactory accuracy. Uncertainty is costly. If the risk is overestimated, people and companies bear excessive cost to protect or insure themselves. If the risk is underestimated, the insurers bankrupt…

It is not possible to predict loss of disasters accurately. It does not mean, however, that data analytics has not to do much in this field. Probably instead of tilt with windmills better is to analyse strategies of the response to uncertainty, and to build the models of optimum strategies. Moreover, the variance of strategies can explain a part of the variance of total loss, and contribute to the accuracy of total loss prediction.

The idea of modelling the strategies of reinsurance is not new. It requires some knowledge and models of the behaviour of open dynamic systems. It seems to be reasonable to build a general model of the reinsurance strategy securing insurers against bankruptcy – the strategy “to preserve the ability to continue operations”.