Wow! Way too much good stuff to respond to in one e-mail. I'll try to respond to more in a later e-mail but . . . . (and I also want to get your reaction to a few things first :-)
>> However, I still don't think that a below-average-IQ human can pragmatically >> (i.e., within the scope of the normal human lifetime) be taught to >> effectively carry out statistical evaluation of theories based on data, >> given the realities of how theories are formulated and how data is obtained >> and presented, at the present time... Hmmm. After some thought, I have to start by saying that it looks like you're equating science with statistics and I've got all sorts of negative reactions to that. First -- Sure. I certainly have to agree for a below-average-IQ human and could even be easily convinced for an average IQ human if they had to do it all themselves. And then, statistical packages quickly turn into a two-edged sword where people blindly use heuristics without understanding them (p < .05 anyone?). A more important point, though, is that humans natively do *NOT* use statistics but innately use very biased, non-statistical methods that *arguably* function better than statistics in real world data environments. That alone would convince me that I certainly don't want to say that science = statistics. >> I am not entirely happy with Lakatos's approach either. I find it >> descriptively accurate yet normatively inadequate. Hmmm. (again) To me that seems to be an interesting way of rephrasing our previous disagreement except that you're now agreeing with me. (Gotta love it :-) You find Lakatos's approach descriptively accurate? Fine, that's the scientific method. You find it normatively inadequate? Well, duh (but meaning no offense :-) . . . . you can't codify the application of the scientific method to all cases. I easily agreed to that before. What were we disagreeing on again? >> My own take is that science normatively **should** be based on a Bayesian >> approach to evaluating theories based on data That always leads me personally to the question "Why do humans operate on the biases that they do rather than Bayesian statistics?" MY *guess* is that evolution COULD have implemented Bayesian methods but that the current methods are more efficient/effective under real world conditions (i.e. because of the real-world realities of feature extraction under dirty and incomplete or contradictory data and the fact that the Bayesian approach really does need to operate in an incredibly data-rich world where the features have already been extracted and ambiguities, other than occurrence percentages, are basically resolved). **And adding different research programmes and/or priors always seems like such a kludge . . . . . ----- Original Message ----- From: Ben Goertzel To: [email protected] Sent: Tuesday, October 21, 2008 4:15 PM Subject: Re: AW: AW: [agi] Re: Defining AGI Mark, >> As you asked for references I will give you two: Thank you for setting a good example by including references but the contrast between the two is far better drawn in For and Against Method (ISBN 0-226-46774-0). I read that book but didn't like it as much ... but you're right, it may be an easier place for folks to start... Also, I would add in Polya, Popper, Russell, and Kuhn for completeness for those who wish to educate themselves in the fundamentals of Philosophy of Science All good stuff indeed. My view is basically that of Lakatos to the extent that I would challenge you to find anything in Lakatos that promotes your view over the one that I've espoused here. Feyerabend's rants alternate between criticisms ultimately based upon the fact that what society frequently calls science is far more politics (see sociology of scientific knowledge); a Tintnerian/Anarchist rant against structure and formalism; and incorrect portrayals/extensions of Lakatos (just like this list ;-). Where he is correct is in the first case where society is not doing science correctly (i.e. where he provided examples regarded as indisputable instances of progress and showed how the political structures of the time fought against or suppressed them). But his rants against structure and formalism (or, purportedly, for freedom and humanitarianism <snort>) are simply garbage in my opinion (though I'd guess that they appeal to you ;-). Feyerabend appeals to my sense of humor ... I liked the guy. I had some correspondence with him when I was 18. I wrote him a letter outlining some of my ideas on philosophy of mind and asking his advice on where I should go to grad school to study philosophy. He replied telling me that if I wanted to be a real philosopher I should **not** study philosophy academically nor become a philosophy professor, but should study science or arts and then pursue philosophy independently. We chatted back and forth a little after that. I think Feyerabend did a good job of poking holes in some simplistic accounts of scientific process, but ultimately I found Lakatos's arguments mostly more compelling... Lakatos did not argue for any one scientific method, as I recall. Rather he argued that different research programmes come with different methods, and that the evaluation of a given piece of data is meaningful only within a research programme, not generically. He argued that comparative evaluation of scientific theories is well-defined only for theories within the same programme, and otherwise one has to talk about comparative evaluation of whole scientific research programmes. I am not entirely happy with Lakatos's approach either. I find it descriptively accurate yet normatively inadequate. My own take is that science normatively **should** be based on a Bayesian approach to evaluating theories based on data, and that different research programmes then may be viewed as corresponding to different **priors** to be used in doing Bayesian statistical evaluations. I think this captures a lot of Lakatos's insights but within a sound statistical framework. This is my "social computational probabilistic" philosophy of science. The "social" part is that each social group, corresponding to a different research programme, has its own prior distribution. I have also, more recently, posited a sort of "universal prior", defined as **simplicity of communication in natural language within a certain community**. This, I suggest, provides a baseline prior apart from any particular research programme. However, I still don't think that a below-average-IQ human can pragmatically (i.e., within the scope of the normal human lifetime) be taught to effectively carry out statistical evaluation of theories based on data, given the realities of how theories are formulated and how data is obtained and presented, at the present time... -- Ben ------------------------------------------------------------------------------ agi | Archives | Modify Your Subscription ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=117534816-b15a34 Powered by Listbox: http://www.listbox.com
