- May 24, 2016
Yes, I suppose that's right.
This is the enduring problems thread though, Pinny. Therefore we must identify a problem to discuss in Baudrillard's work. How about this: you say you 'agree' with him on things becoming less real. This is another way of saying that you think some of the propositions he makes are true. But what makes them true?
On the most common interpretation of truth, we would say Baudrillard says things that correspond to reality -- with how things really are, 'the facts', etc. But he himself says that the real is no longer real but hyperreal. Therefore he seems to lack a basis (i.e. reality) for saying true things. Therefore we seem to be in a position to neither agree or disagree with him.
How would you address this paradox?
Me personally, I'd just reject the idea that everything is hyperreality and assert that the word hyperreality only has meaning if there's an underlying reality to compare it to. That reality can be influenced by hyperreality (i.e. when two people who don't really feel anything for each other anymore fake it because their image of a relationship involves it lasting forever and reflecting a genuine interest in the person they're with, etc.), but it's still the actual experience, which can differ from the fantastic image. And within the actual experience, you could have another layer of hyperreality in the feelings they display to the other, and the reality of their actual feelings which they keep to themselves except under stress.
Nutritional science and psych may have other examples of hyperreality, hah. Stuff is said with authority that turns out to be wrong a fair amount, as shown here: https://www.bmj.com/content/361/bmj.k2392 and here https://www.medicalnewstoday.com/articles/why-is-nutrition-so-hard-to-study . The solution to the sort of problems this creates is (for yourself) to start by using the data to make decisions, but then adjusting based on what seems to be working for yourself based on how it's supposed to work for others, and on the data science end to always be aware of how data collection can be improved, and to continuously improve it.