It is prediction time. Around about this time of year, pundits love to draw on their specialist expertise to predict significant events for the year ahead. The honest ones also revisit their previous yearly predictions to see how they did.
Philip Tetlock is an expert on expert judgement. He conducted a series of experiments between 1984-2003 to uncover whether experts were better than average at predicting outcomes in their given specialism. Predictions had to be specific and quantifiable, in areas ranging from economics to politics and internatonal relations. He found that many so-called experts were pretty bad at telling the future, and some were even worse than the man or woman on the street. Most worryingly, there was an inverse relationship between an experts fame and the accuracy of their predictions.
But he also discovered certain factors that make some experts better predictors than others. He found that training in probabilistic reasoning, avoidance of common cognitive biases, and evaluating previous guesses enabled experts to make more accurate forecasts. Predictions based on the combined judgements of multiple individuals also proved helpful.
This work has continued in the Good Judgement Project, a research project involving several thousand volunteer forecasters, which now has a commercial spin-off, Good Judgement Inc. The project has experimented with various methods of training, forming forecasters into complementary teams, and aggregation algorithms. By rigourously and systematically testing these and other factors, the system aims to uncover the determinants of accurate forecasts. It has already proven highly successful, winning a CIA-funded competition several years in a row (‘Intelligence Advanced Research Projects Activity – Aggregative Contingent Estimation’ (IARPA-ACE)).
The commercial spin-off began as an invite-only scheme, but now it has a new part called ‘Good Judgement Open’ which allows anyone to sign up and have a go. I’ve just signed up and made my first prediction in response to the following question:
“Before the end of 2016, will a North American country, the EU, or an EU member state impose sanctions on another country in response to a cyber attack or cyber espionage?”
You can view the question and the current forecast from users of the site. Users compete to be the most prescient in their chosen areas of expertise.
It’s an interesting concept. I expect it will also prove to be a pretty shrewd way of harvest intelligence and recruit superforecasters for Good Judgement Inc. In this sense the business model is like Facebook and Google, i.e. monetising user data, although not in order to sell targeted advertising.
I can think of a number of ways the site could be improved further. I’d like to be given a tool which helps me break down my prediction into various necessary and jointly sufficient elements and allow me to place a probability estimate on each. For instance, let’s say the question about international sanctions in response to a cyberattack depends on several factors; the likelihood of severe attacks, the ability of digital forensics to determine the origin of the attack, and the likelihood of sanctions as a response. I have more certainty about some of these factors than others, so a system which split them into parts, and periodically revise them, would be helpful (in Bayesian terms, I’d like to be explicit about my priors and posteriors).
One could also experiment with keeping predictions secret until after the outcome of some event. This would mean one forecaster’s prediction wouldn’t contaminate the predictions of others (perhaps if they were well known as a reliable forecaster). This would allow for forecastors to say ‘I knew it!’ without saying ‘I told you so’ (we could call it the ‘Reverse Cassandra’). Of course you’d need some way to prove that you didn’t just write the prediction after the event and back-date it. Or create a prediction for every possible outcome and then selectively reveal the correct one, a classic con illustrated by TV magician Derren Brown. If you wanted to get really fancy, you could do that kind of thing with cryptography and the blockchain.
After looking into this a bit more, I came across this blog by someone called gwern, who appears to be incredibly knowledgeable about prediction markets (and much else besides).