I don’t know how I can fail to communicate so consistently.
Yes, you can technically apply “EDT” to any causal model or (more generally) joint probability distribution containing a “EDT agent decision” node. But in practice this freedom is useless, because to derive an accurate model you generally need to take account of a) the fact that the agent is using EDT and b) any observations the agent does or does not make. To be clear, the input EDT requires is a probabilistic model describing the EDT agent’s situation (not describing historical data of “similar” situations).
There are people here trying to argue against EDT by taking a model describing historical data (such as people following dumb decision theories jumping into volcanoes) and feeding this model directly into EDT. Which is simply wrong. A model that describes the historical behaviour of agents using some other decision theory does not in general accurately describe an EDT agent in the same situation.
The fact that this egregious mistake looks perfectly normal is an artifact of the fact that CDT doesn’t care about causal parents of the “CDT decision” node.
I don’t know how I can fail to communicate so consistently.
I suspect it’s because what you are referring to as “EDT” is not what experts in the field use that technical term to mean.
nsheppard-EDT is, as far as I can tell, the second half of CDT. Take a causal model and use the do() operator to create the manipulated subgraph that would result taking possible action (as an intervention). Determine the joint probability distribution from the manipulated subgraph. Condition on observing that action with the joint probability distribution, and calculate the probabilistically-weighted mean utility of the possible outcomes. This is isomorphic to CDT, and so referring to it as EDT leads to confusion.
I don’t know how I can fail to communicate so consistently.
Yes, you can technically apply “EDT” to any causal model or (more generally) joint probability distribution containing a “EDT agent decision” node. But in practice this freedom is useless, because to derive an accurate model you generally need to take account of a) the fact that the agent is using EDT and b) any observations the agent does or does not make. To be clear, the input EDT requires is a probabilistic model describing the EDT agent’s situation (not describing historical data of “similar” situations).
There are people here trying to argue against EDT by taking a model describing historical data (such as people following dumb decision theories jumping into volcanoes) and feeding this model directly into EDT. Which is simply wrong. A model that describes the historical behaviour of agents using some other decision theory does not in general accurately describe an EDT agent in the same situation.
The fact that this egregious mistake looks perfectly normal is an artifact of the fact that CDT doesn’t care about causal parents of the “CDT decision” node.
I suspect it’s because what you are referring to as “EDT” is not what experts in the field use that technical term to mean.
nsheppard-EDT is, as far as I can tell, the second half of CDT. Take a causal model and use the do() operator to create the manipulated subgraph that would result taking possible action (as an intervention). Determine the joint probability distribution from the manipulated subgraph. Condition on observing that action with the joint probability distribution, and calculate the probabilistically-weighted mean utility of the possible outcomes. This is isomorphic to CDT, and so referring to it as EDT leads to confusion.
Whatever. I give up.