Path: utzoo!attcan!uunet!salt.acc.com!ucsd!orion.oac.uci.edu!jhess From: jhess@orion.oac.uci.edu (James Hess) Newsgroups: comp.ai.philosophy Subject: Re: Re: Emergent properties (was: What AI is exactly) Message-ID: <2711556F.7484@orion.oac.uci.edu> Date: 9 Oct 90 04:43:27 GMT References: <15132@venera.isi.edu> <18070001@hp-ses.SDE.HP.COM> Reply-To: jhess@orion.oac.uci.edu (James Hess) Organization: University of California, Irvine Lines: 31 In article kyriazis@iear.arts.rpi.edu (George Kyriazis) writes: > >Humans are extremely inconsistent and unpredictable, as opposed >to neurons or whatever else forms other organized systems. This >increases the randomness of the system and the resulting global >behavious is not so stable to be characterized organized. Now, here >is the flip side: Neurons definetely cannot comprehend human >behaviour, so a human (being part of a society) cannot comprehend >the behaviour of the society. So, even if the organized behaviour of >the human society exists, I think we won't be able to realize its >existance! Monitoring humanity for long periods of time will >be valuable for the understanding of the path of the human society, >and maybe predicting its future, but I don't think it's going to >get anywhere. > You can stretch analogy too far. But indeed, understanding the behavior of human society (sociology, social psychology, anthropology, economics (Ugh!)) is the true hard science. Physics and chemistry are for wimps. Competing suggestions for further thought: Cybernetics includes the study of ways of building reliable systems from unreliable components. (Redundancy, feedforward, feedback, error correcting) Predicting the future of human society--organized behavior does exist, and we use our recognition of it to guide our choices. But there are too many variables to model--we must simplify and reduce. Here we lose much of the variance of the system. This variance is often the source of novelty and social change. Think of mutation and evolution. And chaos theory shows that we can't always predict the behavior of some very simple recursive systems with one variable.