Incidentally, the best way to make conditional predictions is to convert them to explicit disjunctions. For example, in November I wanted to predict that “If Mitt Romney loses the primary election, Barack Obama will win the general election.” This is actually logically equivalent to “Either Mitt Romney or Barack Obama will win the 2012 Presidential Election,” barring some very unlikely events, so I posted that instead, and so I won’t have to withdraw the prediction when Romney wins the primary.
While that may be best with current PB, I think conditional predictions are useful.
If you are only interested in truth values and not the strength of the prediction, then it is logically equivalent, but the number of points you get is not the same. The purpose of a conditional probability is to take a conditional risk. If Romney is nominated, you get a gratuitous point for this prediction. Of course, simply counting predictions is easy to game, which is why we like to indicate the strength of the prediction, as you do with this one on PB. But turning a conditional prediction into an absolute prediction changes its probability and thus its effect on your calibration score. To a certain extent, it amounts to double counting the prediction about the hypothesis.
The first version doesn’t have that part either- he’s predicting that if Romney gets eliminated in the primaries, ie Gingrich, Santorum, or Paul is the Republican nominee, then Obama will win.
Incidentally, the best way to make conditional predictions is to convert them to explicit disjunctions. For example, in November I wanted to predict that “If Mitt Romney loses the primary election, Barack Obama will win the general election.” This is actually logically equivalent to “Either Mitt Romney or Barack Obama will win the 2012 Presidential Election,” barring some very unlikely events, so I posted that instead, and so I won’t have to withdraw the prediction when Romney wins the primary.
While that may be best with current PB, I think conditional predictions are useful.
If you are only interested in truth values and not the strength of the prediction, then it is logically equivalent, but the number of points you get is not the same. The purpose of a conditional probability is to take a conditional risk. If Romney is nominated, you get a gratuitous point for this prediction. Of course, simply counting predictions is easy to game, which is why we like to indicate the strength of the prediction, as you do with this one on PB. But turning a conditional prediction into an absolute prediction changes its probability and thus its effect on your calibration score. To a certain extent, it amounts to double counting the prediction about the hypothesis.
This is less specific than the first prediction. The second version loses the part where you predict obama will beat romney
The first version doesn’t have that part either- he’s predicting that if Romney gets eliminated in the primaries, ie Gingrich, Santorum, or Paul is the Republican nominee, then Obama will win.
you’re right, I misread.