Bad numbers: coping with flawed decision support
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Bad numbers: coping with flawed decision support TR Willemain, WA Wallace*, KR Fleischmann, LB Waisela and SN Ganawayb Rensselaer Polytechnic Institute, Troy, NY, USA Evidence is accumulating that many spreadsheet-based decision support systems contain errors. These errors can result in bad numbers, which in turn could lead to bad decisions. We review the literature on the origins and consequences of bad (erroneous) numbers produced by models and/or decision support systems built around them. Then, we present a case study in which an experiment in visual support for a sequential decision-making task was tainted by bad numbers. Both the literature review and the experiment indicate a robust human ability to overcome flawed decision support. We conclude with questions that need to be addressed in order to better understand the capabilities of humans to deal with erroneous results from decision support systems. Journal of the Operational Research Society (2003) 54, 949–957. doi:10.1057/palgrave.jors.2601605 Keywords: decision support systems; practice of OR; human–machine systems
Introduction On two occasions I have been asked [by members of Parliament], ‘Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?’ I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question. — Charles Babbage
Erroneous models and decision support systems (DSSs) can create major problems for decision makers who rely on them. However, having to confront such mistakes can bring out the best in decision makers and demonstrate the flexibility and robustness of the human mind. Such is the case in the experiments reported here. Two experiments were intended to demonstrate how various levels of visual decision support help with a problem in sequential decision making. Instead, they demonstrated how decision makers deal with a dysfunctional DSS. The good performance of the human subjects illuminated some of the problems with the particular DSS in question, but it also illustrated that humans have a unique ability to override flawed decision support. DSSs are aids that help decision makers do their jobs.1,2 In general, a DSS is designed for a certain type of problem faced by a specific group of decision makers. A DSS is composed of input data, mathematical models (or other forms of codified relationships among variables, such as production rules), and a user interface that allows the decision maker to interact with the DSS. The two main computational components of a DSS are the database, *Correspondence: WA Wallace, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180-3590, USA. E-mail: [email protected] a Now with Concurrent Technologies Corporation, Johnstown, PA 15904, USA. b Now with Verizon Corporation, Baltimore, MD 21202, USA.
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