Semantic De-biased Associations (SDA) model to improve ill-structured decision support

Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7664 LNCS (PART 2), pp. 483 - 490
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Decision makers are subject to rely upon their biased mental models to solve ill-structured decision problems. While mental models prove to be very helpful in understanding and solving ill-structured problems, the inherent biases often lead to poor decision making. This study deals with the issue of biases by proposing Semantic De-biased Associations (SDA) model. SDA model assists user to make more informed decisions by providing de-biased, and validated domain knowledge. It employs techniques to mitigate biases from mental models; and incorporates semantics to automate the integration of mental models. The effectiveness of SDA model in solving ill-structured decision problems is illustrated in this paper through a case study. © 2012 Springer-Verlag.
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