Tomatometer | Total | % |
---|---|---|
Certified-Fresh | 3,259 | 18.4 |
Fresh | 6,844 | 38.7 |
Rotten | 7,565 | 42.8 |
POLS 3220: How to Predict the Future
Learn how to estimate conditional probability
Introduce a common approach used by successful forecasters: balancing Inside View and Outside View.
Show how all this connects to a central problem in statistics: the bias-variance tradeoff.
Will the movie Roofman be “Certified Fresh” by Rotten Tomatoes?
How would you go about this prediction? What information would you research first?
Inside View: Focus on the details of the case at hand.
Outside View: Ignore the details of the case at hand. Focus on what happened previously with similar cases.
Most people naturally gravitate towards the Inside View (Kahneman and Lovallo 1993), but the best forecasters combine both approaches!
Tomatometer | Total | % |
---|---|---|
Certified-Fresh | 3,259 | 18.4 |
Fresh | 6,844 | 38.7 |
Rotten | 7,565 | 42.8 |
This base rate is a useful starting point.
What’s the problem with this approach? Why not stop there?
The pure Outside View yields a biased prediction, because it doesn’t incorporate any information about the case in question.
More details about the movie Roofman should help us refine our prediction.
How far should we take this???
The “Inside View” is very confident about Roofman. R-movies directed by Derek Cianfrance are always Certified Fresh.
What’s the problem with this approach?
Is this strong evidence that Derek Cianfrance is an above-average director? \(P(\text{Certified Fresh}) > 0.25\)?
With only three data points, we cannot claim with confidence that Cianfrance’s next movie has a \(\frac{2}{3}\) probability of achieving Certified Freshness.
It could be that his movies are better-than-average.
But he also could have gotten lucky.
On one end of the spectrum (pure Outside View), we have a biased but precise prediction.
On the opposite end (pure Inside View), we have an less-biased but highly uncertain prediction.
The truth probably lies somewhere in the middle.
The “art” of forecasting is learning how to balance this tradeoff. How much should you weigh the Outside View vs. the Inside View?
Bayes Rule as a method for computing conditional probability, and adjusting your predictions in light of available evidence.