Most recent edit on 2004-12-21 16:43:24 by DarTar []
Additions:
The project consists in analyzing the dynamic of the web as providing a case of reputation system that is essentially similar to the meritocratic system implemented in contemporary science. We show why and how the Web constitutes a computational model of the scientific practices of deference. On the basis of this model, we analyze the processes leading to deferential behavior, i.e. the mechanics of deference.
The project consists in analyzing the dynamic of the web as providing a case of reputation system that is essentially similar to the meritocratic system implemented in contemporary science. We show why and how the Web constitutes a computerized model of the scientific practices of deference. On the basis of this model, we analyze the processes leading to deferential behavior, i.e. the mechanics of deference.
Oldest known version of this page was edited on 2004-07-27 18:42:04 by DarTar []
Page view:
Mechanics of Deference
The project consists in analyzing the dynamic of the web as providing a case of reputation system that is essentially similar to the meritocratic system implemented in contemporary science. We show why and how the Web constitutes a computerized model of the scientific practices of deference. On the basis of this model, we analyze the processes leading to deferential behavior, i.e. the mechanics of deference.
Individualistic epistemology has been under strong attack these days. The fact is that knowledge, especially scientific knowledge, essentially involves social phenomena. The relevance of social interactions, institutions, consensus, trust, and other social facts for epistemological concerns has been recently pointed out, thus leading to the growth of social epistemology (Schmitt, 1994). This turn in how epistemology is conceived lays a challenge for computational philosophy of science: computational models of the social aspects of scientific knowledge production are needed. This is especially true because computational models of knowledge production and acquisition, drawing on AI, have traditionally taken the individual cognitive agent as their object study. On the other hand, computational modeling of social phenomena has proved to be successful in diverse domains such as linguistics, epidemiology and economics, and benefit from adapted mathematical frameworks such as complex system theory. We argue that these advances are promising for social epistemology, and could be fruitfully used to enrich its theories with mathematical and computational models.
The computational work that is most relevant for social epistemology remains, however, distributed Artificial Intelligence (DAI), with which – it has been suggested – one could model the social organization of science (Thagard, 1993). The idea is to exploit the cognitivist analogy between human cognitive agents and computational devices by modeling community of scientists with network of computational devices. The links between the nodes of the network allow and constrain communication between the devices, thus allowing to model communication between scientists. The reason, therefore, for developing DAI models of scientific practice is that they could help us understand the processes underlying the production of knowledge that is the fate of a community of cognitive agents.
To be continued ...