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Searching for scientific articles is a bit like searching for mates (see Chapter 40). Of course, the object of desire is quite different. Nevertheless, in both cases, the search unfolds across alternatives, examined in a temporal sequence. We all know the dilemma. When turning to a new research topic, we do not know what are the relevant publications; the task is to spot them within a cloud of irrelevant articles. Fortunately, this task has been much simplified by electronic literature search systems, which after a few keystrokes spit out a list of publications that match our search terms. The default ordering of the articles, however, does not necessarily reflect their actual relevance, and therefore the user cannot help but peruse the articles (or abstracts) one by one.
How could one speed up this search? In this article, Michael Lee, Natasha Loughlin, and Ingrid Lundberg engineered two methods, one embodying one-reason decision-making, and the other using all available information. According to the complex Bayesian method, publications are ranked according to the probability that that they are relevant. To this end, the authors construct cues that, if present, indicate that an article is relevant. The validity of each cue is estimated on the basis of a reader's acceptance or rejection of previous articles, and is continuously updated. Based on the sum of all validity values associated with each cue, publications are prioritized, with the most relevant articles coming first. The one-reason method employs the same cues and ranks them according to the evidence that they provide in favor of an article being relevant. Validities and order of cues are continually updated with each newly read article. Starting with the most valid cue, a search is then made for unread articles that have a positive value on that cue. If an article is found, search is terminated; otherwise, the same process is repeated with the second-highest cue.
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