When you try to use "logical" methods on an inductive (open and non-monotonic data space) the logical or rational methods will act more like heuristics at best.
Yes, and they are entirely appropriate there as long as you realize the shortcomings as well as the advantages and you document your assumptions so that you can revisit them later. My beef with "intuition" is that it normally is logic without the documentation or the recognition of it's shortcomings -- which can be turned to use if you only acknowledge them. And a great deal of knowledge is based on partial knowledge or on conjectures about the data environment (or subject matter). However, I still feel that advantageous to use logic and other rational methods to examine concepts within the fluid boundaries that we can construct around them. Absolutely. We use anything we can even when it is not certain -- otherwise we would never make progress. But, too often, logic is also improperly used to block paths and make assertions that are provably not true -- and then the fact that they are "logical" is frequently even used as a trump over real live experimental data. That's where it's a problem. I think that our thinking is comprised of mixtures of rational and non-rational reasoning. This means that they can be examined using rational analysis even though they are not purely rational. Again, absolutely. Just always keep track of your error bars and failure modes. ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=103754539-40ed26 Powered by Listbox: http://www.listbox.com
