By Simon Parsons
During this publication Simon Parsons describes qualitative tools for reasoning lower than uncertainty, "uncertainty" being a catch-all time period for numerous sorts of imperfect details. the good thing about qualitative equipment is they don't require exact numerical details. in its place, they paintings with abstractions corresponding to period values and knowledge approximately how values switch. the writer doesn't invent thoroughly new equipment for reasoning lower than uncertainty yet offers the potential to create qualitative types of current equipment. to demonstrate this, he develops qualitative models of chance concept, probability idea, and the Dempster-Shafer thought of evidence.According to Parsons, those theories are most sensible thought of complementary instead of specific. hence the publication helps the rivalry that instead of look for the only most sensible strategy to deal with all imperfect info, one should still use whichever approach most closely fits the matter. This procedure leads evidently to using numerous assorted equipment within the answer of a unmarried challenge and to the complexity of integrating the results--a challenge to which qualitative tools offer an answer.
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Extra resources for Qualitative Methods for Reasoning under Uncertainty
To show the “probabilistic equivalence” of the two alternatives it is necessary to add information about the second, namely that in the absence of knowing how many black and red balls there are, it is reasonable to assume equal numbers. Thus maybe all the experiments show is that people are not comfortable with this assumption. MIT Press Math7X9/2001/05/17:11:56 Page 25 All about uncertainty 25 the degree to which A resembles B. Availability is used when the frequency of an event is assessed by the ease with which an occurrence may be brought to mind, and adjustment and anchoring are the related phenomenon of estimating by taking a guess and moving it as required.
0 litres or more) but it will not be certain that it is correct. Thus in terms of the classification discussed above it is possible to have incomplete, vague, uncertain and imprecise data. Motro then identifies the sources of these imperfections. They may result from unreliable sources, such as faulty sensors, input errors, or the inappropriate choice of representation (such as forcing an attribute with a disjunctive value to be single valued). He also discusses the problem of subjective errors introduced, for instance, by measuring timevarying quantities.
Thus it arises because, for instance, it is not possible to decide if a given statement, such as “It will rain at 5pm,” is true or false. As a result it is only possible to estimate the objective propensity or the subjective belief or credibility that the statement is true. Imprecision arises from the granularity of the language in which the data is represented. Thus the precise statement “Simon is 33 years and six months old” becomes the imprecise statement “Simon is between 33 and 34” years old when months are ignored.