There’s a LOT of detail that the word “compatible” obscures. Obviously, they’re not identical, so they must differ in some ways. This will always and intentionally make them incompatible on some dimensions. “compatible for what purpose” is the key question here.
I’d argue that your character-traits example is very illustrative of this. To the extent that you use the same clustering of trait definitions, that’s very compatible for many predictions of someone’s behavior. Because the traits are attached differently in your model, that’s probably NOT compatible for how traits change over time. There are probably semi-compatible elements in there, as well, such as how you picture uncertainty about or correlation among different trait-clusters.
There’s a LOT of detail that the word “compatible” obscures. Obviously, they’re not identical, so they must differ in some ways. This will always and intentionally make them incompatible on some dimensions. “compatible for what purpose” is the key question here.
I’d argue that your character-traits example is very illustrative of this. To the extent that you use the same clustering of trait definitions, that’s very compatible for many predictions of someone’s behavior. Because the traits are attached differently in your model, that’s probably NOT compatible for how traits change over time. There are probably semi-compatible elements in there, as well, such as how you picture uncertainty about or correlation among different trait-clusters.