I feel a bit confused reading this. The notion of an expected utility maximiser is standard in game theory and economics, and is (mathematically) defined as having a preference (ordering) over states that is complete, transitive, continuous and independent.
Did you not really know about the concept when you wrote the OP? Perhaps you’ve mostly done set theory and programming, and not run into the game theory and economics models?
Or maybe you find the concept unsatisfactory in some other way? I agree that it can give lease to bring in all of one’s standard intuitions surrounding goals and such, and Rohin Shah has written a post trying to tease those apart. Nonetheless, I hear that the core concept is massively useful in economics and game theory, suggesting it’s a still a very useful abstraction.
Similarly, concepts like ‘environment’ are often specified mathematically. I once attended an ‘intro to AI’ course at a top university, and it repeatedly would define the ‘environment’ (the state space) of a search algorithm in toy examples—the course had me had to code A* search into a pretend ‘mars rover’ to drive around and find its goal. Things like defining a graph, the weights of its edges, etc, or otherwise enumerating the states and how they connect to each other, are ways of defining such concepts.
If you have any examples of people misusing the words—situations where an argument is made by association, and falls if you replace the common word with a technically precise definition—that would also be interesting.
I feel a bit confused reading this. The notion of an expected utility maximiser is standard in game theory and economics. Or maybe you find the concept unsatisfactory in some other way?
The latter. Optimization is more general than expected utility maximization. By applying expected utility theory, one is trying to minimize the expected distance to a set of conditions (goal), rather than distance to a set of conditions (state) in abstract general sense.
The original post (OP) is about refactoring the knowledge tree in order to make the discussions less biased and more accessible across disciplines. For example, the use of abbreviations like “OP” may make it less accessible across audiences. Similarly, using well-defined concepts like “agent” may make discussions less accessible to those who know just informal definitions (similar to how the mathematical abstractions of point and interval may be confusing to the un-initiated).
The concepts of “states” and “processes” may be less confusing, because they are generic, and don’t seem to have other interpretations within similar domains in everyday life, unlike “environments”, “agents”, “intervals”, “points” and “goals” do.
I feel a bit confused reading this. The notion of an expected utility maximiser is standard in game theory and economics, and is (mathematically) defined as having a preference (ordering) over states that is complete, transitive, continuous and independent.
Did you not really know about the concept when you wrote the OP? Perhaps you’ve mostly done set theory and programming, and not run into the game theory and economics models?
Or maybe you find the concept unsatisfactory in some other way? I agree that it can give lease to bring in all of one’s standard intuitions surrounding goals and such, and Rohin Shah has written a post trying to tease those apart. Nonetheless, I hear that the core concept is massively useful in economics and game theory, suggesting it’s a still a very useful abstraction.
Similarly, concepts like ‘environment’ are often specified mathematically. I once attended an ‘intro to AI’ course at a top university, and it repeatedly would define the ‘environment’ (the state space) of a search algorithm in toy examples—the course had me had to code A* search into a pretend ‘mars rover’ to drive around and find its goal. Things like defining a graph, the weights of its edges, etc, or otherwise enumerating the states and how they connect to each other, are ways of defining such concepts.
If you have any examples of people misusing the words—situations where an argument is made by association, and falls if you replace the common word with a technically precise definition—that would also be interesting.
The latter. Optimization is more general than expected utility maximization. By applying expected utility theory, one is trying to minimize the expected distance to a set of conditions (goal), rather than distance to a set of conditions (state) in abstract general sense.
The original post (OP) is about refactoring the knowledge tree in order to make the discussions less biased and more accessible across disciplines. For example, the use of abbreviations like “OP” may make it less accessible across audiences. Similarly, using well-defined concepts like “agent” may make discussions less accessible to those who know just informal definitions (similar to how the mathematical abstractions of point and interval may be confusing to the un-initiated).
The concepts of “states” and “processes” may be less confusing, because they are generic, and don’t seem to have other interpretations within similar domains in everyday life, unlike “environments”, “agents”, “intervals”, “points” and “goals” do.
Are you the same person as the author of the top-level post? (You seem to have a different username)