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Coherent lower previsions are general probabilistic models allowingincompletely specified probability distributions. However, for completedescription of a coherent lower prevision -- even on finite underlying samplespaces -- an infinite number of assessments is needed in general. Therefore,they are often only described approximately by some less general models, suchas coherent lower probabilities or in terms of some other finite set ofconstraints. The magnitude of error induced by the approximations has oftenbeen neglected in the literature, despite the fact that it can be significant,with substantial impact on consequent decisions. An apparent reason is that nowidely used general method for estimating the error seems to be available atthe moment.  This paper provides a practically applicable method that allows calculatingan upper bound for the maximal error induced by approximating a coherent lowerprobability with its values on a finite set of gambles. An algorithm is alsoprovided with an estimation of its computational complexity.

Errors bounds for finite approximations of coherent lower previsions on finite probability spaces