Last week, we looked at how quantum physics can teach us a thing or two about problem solving in small data environments. Some of the most path breaking work in Physics started with a theory, refined by mathematical models and then proved (or disproved) with experiments. This week, let’s take a look at Biology –... Continue Reading →

Richard Feynman Richard Feynman (he doesn’t need an introduction) was a consummate problem solver. When asked about his problem-solving techniques, his colleague Murray Gell-Mann (a Nobel Laureate himself) defined the ‘Feynman Problem Solving Algorithm’: Write down the problemThink very hardWrite down the answer While this was partly in jest, this to me captures why it... Continue Reading →

Over the last few posts, I had explored the overall idea of how to approach the problem of decision making in a world of ‘tail events’ and insufficient data. And increasingly, it is clear that most Analytics teams in organizations are not very well equipped to navigate this order of complexity. Over the next couple... Continue Reading →

I started this mini-series with the need to create models that could work with tail events,  working with small data and inferential thinking. In our final installment (and probably the densest!), I want to dip our toes into the world of simulations to help us navigate the business problem landscape in this ‘new normal’. As... Continue Reading →

It would be no understatement to say that Thomas Bayes was an obscure figure in the much-storied annals of mathematics and logic for a better part of the last 100 years. And then something happening in the last 10 years and now, he is everywhere – and has even entered mainstream culture by the eponymous... Continue Reading →