I may as well repeat my thoughts on Newcomb’s, decision theory, and so on. I come to this from a background in decision analysis, which is the practical version of decision theory.
You can see decision-making as a two-step, three-state problem: the problem statement is interpreted to make a problem model, which is optimized to make a decision.
If you look at the wikipedia definitions of EDT and CDT, you’ll see they primarily discuss the optimization process that turns a problem model into a decision. But the two accept different types of problem models; EDT operates on joint probability distributions and CDT operates on causal models. Since the type of the interior state is different, the two imply different procedures to interpret problem statements and optimize those models into decisions.
To compare the two simply, causal models are just more powerful than joint probability distributions, and the pathway that uses the more powerful language is going to be better. A short attempt to explain the difference: in a Bayes net (i.e. just a joint probability distribution that has been factorized in an acyclic fashion), the arrows have no physical meaning—they just express which part of the map is ‘up’ and which is ‘down.’ In a causal model, the arrows have physical meaning—causal influence flows along those arrows only in directions with arrows, and so the arrows represent which direction gravity pulls in. One can turn a map upside down without changing its correspondence to the territory; one cannot reverse gravity without changing the territory.
Because there are additional restrictions on how the model can be written, one can get additional information out of reading the model.
I may as well repeat my thoughts on Newcomb’s, decision theory, and so on. I come to this from a background in decision analysis, which is the practical version of decision theory.
You can see decision-making as a two-step, three-state problem: the problem statement is interpreted to make a problem model, which is optimized to make a decision.
If you look at the wikipedia definitions of EDT and CDT, you’ll see they primarily discuss the optimization process that turns a problem model into a decision. But the two accept different types of problem models; EDT operates on joint probability distributions and CDT operates on causal models. Since the type of the interior state is different, the two imply different procedures to interpret problem statements and optimize those models into decisions.
To compare the two simply, causal models are just more powerful than joint probability distributions, and the pathway that uses the more powerful language is going to be better. A short attempt to explain the difference: in a Bayes net (i.e. just a joint probability distribution that has been factorized in an acyclic fashion), the arrows have no physical meaning—they just express which part of the map is ‘up’ and which is ‘down.’ In a causal model, the arrows have physical meaning—causal influence flows along those arrows only in directions with arrows, and so the arrows represent which direction gravity pulls in. One can turn a map upside down without changing its correspondence to the territory; one cannot reverse gravity without changing the territory.
Because there are additional restrictions on how the model can be written, one can get additional information out of reading the model.