It is an extremely rare situation, and thus ignoring it will can be expected not to have a large effect on the predicted outcome. Next we construct the analogical set, which consists of all of the pointers and outcomes from the homogeneous supracontexts. The figure below shows the pointer network with the homogeneous contexts highlighted. The pointers are summarized in the following table: 4 of the pointers in the analogical set are associated with the outcome e, and the other 9 are associated with r. In AM, a pointer is randomly selected and the outcome it points to is predicted. With a total of 13 pointers, the probability of the outcome e being predicted is 4/13 or 30.8%, and for outcome r it is 9/13 or 69.2%. We can create a more detailed account by listing the pointers for each of the occurrences in the homogeneous supracontexts: We can then see the analogical effect of each of the instances in the data set. Historical context Analogy has been considered useful in describing language at least since the time of Saussure. Noam Chomsky and others have more recently criticized analogy as too vague to really be useful (Bańko 1991), an appeal to a deus ex machina. Skousen's proposal appears to address that criticism by proposing an explicit mechanism for analogy, which can be tested for psychological validity. Applications Analogical modeling has been employed in experiments ranging from phonology and morphology (linguistics) to orthography and syntax. Problems Though analogical modeling aims to create a model free from rules seen as contrived by linguists, in its current form it still requires researchers to select which variables to take into consideration. This is necessary because of the so-called "exponential explosion" of processing power requirements of the computer software used to implement analogical modeling. Recent research suggests that quantum computing could provide the solution to such performance bottlenecks (Skousen et al. 2002, see pp 45–47). See also Computational Linguistics Connectionism Instance-based learning k-nearest neighbor algorithm References Skousen, Royal. (2003). Analogical Modeling: Exemplars, Rules, and Quantum Computing. Presented at the Berkeley Linguistics Society conference. External links Analogical Modeling Research Group Homepage LINGUIST List Announcement of Analogical Modeling, Skousen et al. (2002) Classification algorithms Computational linguistics Analogy