Coherentism, reliability and Bayesian networks

Download Free PDF View PDF

There is an escalating perception in some quarters that the conclusions drawn from digital evidence are the subjective views of individuals and have limited scientific justification. This paper attempts to address this problem by presenting a formal model for reasoning about digital evidence. A Bayesian network is used to quantify the evidential strengths of hypotheses and, thus, enhance the reliability and traceability of the results produced by digital forensic investigations. The validity of the model is tested using a real court case. The test uses objective probability assignments obtained by aggregating the responses of experienced law enforcement agents and analysts. The results confirmed the guilty verdict in the court case with a probability value of 92.7%.

Download Free PDF View PDF

Abstract Coherentism maintains that coherent beliefs are more likely to be true than incoherent beliefs, and that coherent evidence provides more confirmation of a hypothesis when the evidence is made coherent by the explanation provided by that hypothesis. Although probabilistic models of credence ought to be well-suited to justifying such claims, negative results from Bayesian epistemology have suggested otherwise.

Download Free PDF View PDF

Proc. BDA, Namur, Belgium …

Download Free PDF View PDF

This paper critically examines the "reliability challenge" associated with the work of Benacerraf (1973) and Field (1989). It argues -against a widely held view- that the force of Field's challenge derives from the "causal criterion": when that challenge is stripped of the causal considerations on which Benacerraf focused, it is not clear that any interesting epistemological challenge remains. The paper also critically examines an explanatory version of the causal criterion on which, it suggests, Field's challenge depends. It adapts an argument of Noam Chomsky's to argue that this explanatory version of the criterion is plausibly false. It goes on to argue that the Chomskyan argument casts doubt on the tenability of Field's distinction between justification and explaining reliability itself -at least in interesting cases that lie at the limits of current knowledge and understanding.

Download Free PDF View PDF

Abstract When information sources are unreliable, information networks have been used in data mining literature to uncover facts from large numbers of complex relations between noisy variables. The approach relies on topology analysis of graphs, where nodes represent pieces of (unreliable) information and links represent abstract relations. Such topology analysis was often empirically shown to be quite powerful in extracting useful conclusions from large amounts of poor-quality information.

Download Free PDF View PDF

See Full PDF

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Download Free PDF View PDF

Arxiv preprint cs/0003041

Download Free PDF View PDF

Philosophy of Science

Download Free PDF View PDF

The Fifth International Conference on …

Download Free PDF View PDF

Topics in Cognitive Science

Download Free PDF View PDF

European Journal for Philosophy of Science

Download Free PDF View PDF

Cybernetics and Systems

Download Free PDF View PDF

Organizational Behavior and Human Performance

Download Free PDF View PDF

Journal of Applied Logic

Download Free PDF View PDF