Essentially, our forecasts reflect our expectations about what the future holds. What makes accurate forecasting so difficult reflects the complexity of potential outcomes. For instance, who would have predicted that a single Tunisian street vendor, Mohamed Bouazizi, would instigate the Arab Spring after he set himself on fire in late December 2010.The public anger and protest that followed his death led to the resignation of the Tunisian president and inspired protests in several other Arab countries. The authors insist that Superforecasting should not be restricted to computer algorithms. It is a learnable skill-set that involves rigorous evidence-gathering, score-keeping, discipline to remain updated about new information and patience.

Chaos Theory

Also known as the “butterfly effect”, Chaos theory is a branch of mathematics focusing on the study of chaos — dynamical systems whose apparently random states of disorder and irregularities are actually governed by underlying patterns and deterministic laws that are highly sensitive to initial conditions. An expert forecaster approaches the task by breaking down seemingly impossible problems into manageable sub-problems. Through a process of “Fermi-style” thinking, forecasters should separate the “knowable” from the “unknowable”. Superforecasters, according to Tetlock & Gardner distinguish between the “outside” and the “inside” view. They utilise a simple example of an Italian family living in the US. The father works as a book-keeper and the mother works part-time in a daycare centre. They have a daughter and the child’s grandmother also lives in the home. A simple question is posed – what is the chance that this family owns a pet? A Superforecaster would start by researching the percentages or base rate first. They would begin by researching what percentage, or base rate, of American households own a pet. This the “outside” view. Studying the “inside view” then allows you to adjust the base rate accordingly.  

How do we improve our Forecasting?

  • We need to follow the approach of meteorologists who regularly review their initial forecasts to analyse the accuracy of their forecasts after the fact
  • Forecast measurement is key to improving the accuracy of future predictions
  • To ascertain the accuracy of a forecast, you must first understand the meaning of the original forecast
  • Track forecasting performance to aid in measurement rigour
  • Compartmentalise the forecasting problem into manageable pieces prior to commencing the analysis
  • Forecasts should be fluid and modified if “new” information surfaces

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