http://cultural-science.org/blog/?p=11 Sat, 27 Mar 2010 14:46:06 +0000 http://wordpress.org/?v=2.9.1 hourly 1 http://cultural-science.org/blog/?p=11&cpage=1#comment-9 Carsten Herrmann-Pillath Sat, 10 May 2008 12:27:01 +0000 http://cultural-science.org/blog/?p=11#comment-9 There is a book that treats all these issues in much detail: Scott Page's The Difference, which I highly recommend. Regarding the physics approaches, Page especially mentions simulated annealing, which I think is a better analogy than Brownian motion. The principle of simuating annealing is that you get more structure by first destroying structure. This is what is applied in metallurgy and related areas. You first heat up, then let cool down and see what has happened. If you approach the desired structure, you heat up again, but less, and then you let cool down again. This is repeated with declining maximum temperatures until the desired structural properties have materialized. This engineering approach can be generalized into a universal algorithm for knowledge production. So you always need upheavals and chaos to start the creation of new knowledge, but in the end the dust must settle, and you should not stir up again and again. Of course that requires a firm standard of selecting the optimum ex post. Carsten, writing from Witten, Germany There is a book that treats all these issues in much detail: Scott Page’s The Difference, which I highly recommend. Regarding the physics approaches, Page especially mentions simulated annealing, which I think is a better analogy than Brownian motion. The principle of simuating annealing is that you get more structure by first destroying structure. This is what is applied in metallurgy and related areas. You first heat up, then let cool down and see what has happened. If you approach the desired structure, you heat up again, but less, and then you let cool down again. This is repeated with declining maximum temperatures until the desired structural properties have materialized. This engineering approach can be generalized into a universal algorithm for knowledge production. So you always need upheavals and chaos to start the creation of new knowledge, but in the end the dust must settle, and you should not stir up again and again. Of course that requires a firm standard of selecting the optimum ex post.
Carsten, writing from Witten, Germany

]]>