But then I look outside the confines of a system and realize that, at least this human, tends to work extensionally. I look at the pile of data and create some kind of reality around it. Probably making many leaps of faith, many erroneous deductions, probably drawing erroneous conclusions, positint theories and adding to my own knowledgebase.
So a simple fact (you are unable to meet me for a meeting) + the increase in your linkedin activity, + a tripit notification that you have flown to SJC will at least give me pause for thought. Perhaps you are job hunting! I don’t know, but I might posit that thought in my head and then look for things to confirm or deny it (including phoning you to ask). How do I put that into a schema? How do I decide that is relevant?
I don’t. In fact I may never have had the explicit job-hunt “object” or at least never had explicit properties for it, but somehow this coming together of data has led me to think about it.
The point here is, of course, that if we attempt to model everything about our data intensionally we are doomed. We will be modeling for ever. If we don’t model the right things intensionally, we are equally doomed.
This is the fundamental dichotomy pervading the SQL/NoSQL movement today. We want to have the control that intensional approaches give us so that we can be accurate and consistent – especially with our transactional data, but we also want the ability to make new discoveries based on the data that we find.
We can’t just have a common set of semantics and have everyone expect to agree. In Women, Fire and Dangerous Things, George Lakoff describes some categories that are universal across the human race. Those are to some extent intensional. Then there are all the others that we make up and define newly, refine membership rules, etc. and those are largely extensional.