Coherentism, reliability and Bayesian networks

The coherentist theory of justification provides a response to the sceptica l challenge: even though the independent processes by which we gather infor mation about the world may be of dubious quality, the internal coherence of the information provides the justification for our empirical beliefs. This central canon of the coherence theory of justification is tested within th e framework of Bayesian networks, which is a theory of probabilistic reason ing in artificial intelligence. We interpret the independence of the inform ation gathering processes (IGPs) in terms of conditional independences, con struct a minimal sufficient condition for a coherence ranking of informatio n sets and assess whether the confidence boost that results from receiving information through independent IGPs is indeed a positive function of the c oherence of the information set. There are multiple interpretations of what constitute IGPs of dubious quality. Do we know our IGPs to be no better th an randomization processes? Or, do we know them to be better than randomiza tion processes but not quite fully reliable, and if so, what is the nature of this lack of full reliability? Or, do we not know whether they are fully reliable or not? Within the latter interpretation, does learning something about the quality of some IGPs teach us anything about the quality of the other IGPs? The Bayesian-network models demonstrate that the success of the coherentist canon is contingent on what interpretation one endorses of the claim that our IGPs are of dubious quality.