I like your idea of point allocation (especially if we can include STR, DEX, and INT). But how do we set up the taxonomy of attributes? Particularly with hierarchical relationships (ex: is Machine Learning a subset of AI?) you'd want rough consensus if you're going to run graph algorithms over it and conclude anything beyond data-visualization style "Look, pretty data!" data pron.
That's one of the hilarious things about linkedin attributes; people accrue tags that have vastly different importance depending on context. But hey, it's hard to say no to someone else vouching for my Computer Animation trait, whatever that means. It has the same sort of vague benefit with negligible cost situation as friend graphs. This is why I like your idea of careful scarce allocation, vs. limitless accrual. Information is only meaningful inasmuch as it represents choice.
That's one of the hilarious things about linkedin attributes; people accrue tags that have vastly different importance depending on context. But hey, it's hard to say no to someone else vouching for my Computer Animation trait, whatever that means. It has the same sort of vague benefit with negligible cost situation as friend graphs. This is why I like your idea of careful scarce allocation, vs. limitless accrual. Information is only meaningful inasmuch as it represents choice.