Applying One Reason Decision-Making: The Prioritisation of Literature Searches
Michael Lee, Natasha Loughlin, Ingrid B. Lundberg · Oxford University Press eBooks · 2011
The prioritization of literature searches aims to order the large numbers of articles returned by a simple search so that the ones most likely to be relevant are at the top of the list. Prioritization relies on having a good model of human decision-making that can learn from the articles users select as being relevant to make predictions about which of the remaining articles will be relevant. This chapter develops and evaluates two psychological decision-making models for prioritization: A “rational” model that considers all of the available information, and a “one reason” model that uses limited information to make decisions. The models are evaluated in an experiment where users rate the relevance of every article returned by PsycINFO for a number of different research topics, with the results showing that both models achieve a level of prioritization that significantly improves upon the default ordering of PsycINFO. The one-reason model is shown to be superior to the rational model, especially when there are only a few relevant articles. The implications of the results for developing prioritization systems in applied settings are discussed, together with implications for the general modeling of human decision-making.