Philip K. Dick (2) vs. Jean Baudrillard

February 22, 2015
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(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

For yesterday, I was gonna go with Vincent, based on X’s comment:

In addition to his unique painting style and very special life, van Gogh was highly literate, as shown through the 844 letters from him that are available today.

X also made a missing-body-part joke, which I generally don’t think is so cool but, if anyone’s allowed to get away with that sort of humor, it’s X.

Anyway, now I was curious so I googled *Vincent Van Gogh letters* and found this site. I clicked through and looked at a few letters and they seemed like nothing special.

So, given that this was the best argument in favor and it wasn’t so great, I’ll have to call it for Grandma Moses, boring as she sounds.

pkd

Today we have Horselover Fat vs. the self-parodying intellectual. Dick would seem to be the easy winner here. But Baudrillard did write this:

Decidedly, joggers are the true Latter Day Saints and the protagonists of an easy-does-it Apocalypse. Nothing evokes the end of the world more than a man running straight ahead on a beach, swathed in the sounds of his walkman, cocooned in the solitary sacrifice of his energy, indifferent even to catastrophes since he expects destruction to come only as the fruit of his own efforts, from exhausting the energy of a body that has in his own eyesbecome useless. Primitives, when in despair, would commit suicide by swimming out to sea until they could swim no longer. The jogger commits suicide by running up and down the beach. His eyes are wild, saliva drips from his mouth. Do not stop him. He will either hit you or simply carry on dancing around in front of you like a man possessed.

I think we can safely say this is a contest between two guys who did not spend much time at the gym.

P.S. As always, here’s the background, and here are the rules.

The post Philip K. Dick (2) vs. Jean Baudrillard appeared first on Statistical Modeling, Causal Inference, and Social Science.



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