The power of computers has increased in recent decades, and one might expect improved management to result because decisions can be made with understanding available only via models. However, there is potential for quite the opposite: poor decisions due to unrealistic model output generated by users without access to appropriate training in the use of models. We discuss and, by reference to water demand models (IWR-MAIN, MWD-MAIN), illustrate three areas in which unintended errors of judgment by untrained personnel may cause difficulty:• Attributes of management models; if output from any type of model has no measure of confidence, then results may be over-or undervalued • Input data; with complex models, problems here typically will be difficult to detect.• Calibration and history-matching (verification); if these steps or data are combined, then users should be less trustful of model output than otherwise.Because all models have weaknesses and because there always is uncertainty about output from any model, we end with suggestions for coping with complex models. Monitoring programs play a central role in such efforts because they can identify discrepancies between model predictions and actual events and because they can ensure time is available to develop solutions for problems unanticipated in the modeling effort. (KEY TERMS: models; modeling; IWR-MAIN; MWD-MAIN; water forecasting systems; water use; water demand.) IPaper No. 94016 of the Waler Resources Bulletin. Discussions are open until December 1, 1995. 2Respectively, Associate Professor,