1. A computer implemented method of analysis of competing hypotheses in estimative intelligence, said method comprising the steps of: a. deciding on a plurality of possible hypotheses to be considered; b. identifying significant items of evidence for and against each of said plurality of hypotheses; c. configuring a processor to construct and store onto a memory a model for analyzing the hypotheses by: i. producing a set of exhaustive and exclusive hypotheses, wherein only one hypothesis may be true; ii. assessing and assigning base rates for each hypothesis; iii. determining from said significant items of evidence identified in relation to respective hypotheses a set of items of evidence that are relevant to, have a causal influence on or would disconfirm more than one hypothesis; iv. assessing and assigning base rates for each item of evidence; v. deciding for each item of evidence whether the item should be treated as being a causal influence or diagnostic indicator with respect to the set of the hypotheses; vi. if the item of evidence is to be treated as a causal influence, making a judgment as to the likelihood of each hypothesis: A. if the evidence were true, and B. if the evidence were false; vii. if the item of evidence is to be treated as a diagnostic indicator, making a judgment as to the likelihood of the evidence being true: A. if the hypothesis were true; d. assessing the belief for each item of evidence being true; e. deciding a set of interim beliefs in each hypothesis for each individual item of evidence by: i. employing a conditional inference operator for evidence that is to be treated as a causal influence; and ii. employing a reverse conditional inference operator for evidence that is to be treated as a diagnostic indicator; f. deciding the overall belief in each hypothesis by employing a consensus operator on the respective set of interim beliefs; and g. outputting a set of beliefs representing the certainty and likelihood of each hypothesis.