= 27d567bc2d48d9f40e9ccc20ea370b15)
Bugmaster comments on AI Risk & Opportunity: Strategic Analysis Via Probability Tree - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (11)
This is kind of a weird article... It explains how to use decision trees, but then it just stops, without telling me what to expect, why I should care, or, assuming I did care, how to assign probabilities to the nodes. So, the only feeling I'm left with at the end is, "all righty then, time for tea".
In addition, instead of saying "X | private push" and "X | no private push", it might be clearer to add the nodes "private push" and "no private push" explicitly, and then connect them to "FAI", "uFAI", etc. An even better transformation would be to convert the tree into a graph; this way, you won't need to duplicate the terminal nodes all the time.
Moving to a graph makes elicitation of the parameters a lot more difficult (to the extent that you have to start specifying clique potentials instead of conditional probabilities). Global tasks like marginalization or conditioning also become a lot harder.
I think you can still get away with using conditional probabilities if you make the graph directed and acyclical, as I should've specified (my bad). The graph is still more complex than the tree, as you said, but if we're using software for the tree, we might as well use one for the graph...