Coping with the complexities of multiple-solution problems: a case study

Abstract

A model is proposed to account for the expertise of a skilled immunohematologist in solving multiple-solution problems. These problems, which he must deal with daily, are concerned with ensuring the safe transfusion of blood into patients. This model suggests that he copes with this difficult class of problems by: (1) Using patterns in the data to simplify the problem, hypothesizing the number of primitive solutions necessary to account for the test results and, when possibles, decomposing the problem into a set of less complex, single-solution problems. Such decompositions then enable more powerful reasoning processes; (2) making use of a mixture of data-driven and hypothesis-driven processes in order to reduce the chances that heuristic (and therefore error-prone) methods and cognitive biases will lead away from critical data; (3) Relying on a mixture of confirmatory and rule-out processes to provide converging evidence, thus reducing the changes of error; (4) uncovering his own errors through the use of “error models” that identify the conditions where one of his processes is likely to make an error (similar to the use of student models by expert tutors to diagnose mistakes made by students).