Taking decisions in situations with incomplete data

Daniel Meister is CTO and member of the board at Datahouse. This is a ETH spinoff specialised in Computer Science and Statistics, which is called data science nowadays. Daniel Meister originally trained as physicist and so is very well equipped to work with huge amounts of data.

Usually, human minds are good in reorganising so that we think that we have a situation with complete data. With new and unusual situations, we are confronted with incomplete data so that we explicitly feel that our decisions may not be grounded.

Often, we underestimate how many elements are interconnected in the same way we underestimate the amount of uncertainty about the future. So, Daniel Meister suggest to apply the same principles as agile development should be applied also in every day life decisions. If you have to plan into a more uncertain future, you have to do it in shorter iteration cycle or more different scenarios.