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This paper introduces a certainty-weighted detection system (CWDS) based on distributed decision makers that can classify a binary phenomenon as true, false, or uncertain. The CWDS is composed of two main blocks: the definite decision block (DDB), which provides a decision regarding the presence or absence of the phenomenon, and the uncertainty measure block (UMB) that provides a measure of uncertainty. The final decision, which may be definite (true or false) or uncertain, depends on characteristic parameters that define the region of uncertainty (RUi and α) used by piecewise linear certainty functions in the DDB and in the UMB. The Bayes cost analysis is extended to include the cost of uncertain classifications and cost of errors. A cost function is used to compare the CWDS to decision structures based on the Dempster-Shafer theory and fuzzy logic that also provide uncertain decisions. The CWDS performs similarly to a classical Bayes detection system when no uncertain classifications are provided. By changing the parameters RUi and α, the CWDS can also be adjusted to perform similarly to the Dempster-Shafer and fuzzy structures. The differences between these approaches are mainly in their characterization of uncertainty, and they can reduce the total costs below that of the Bayesian model if the cost of uncertain classifications is sufficiently smaller than the cost of errors. The performance of the CWDS was less sensitive to changes in the ratio of the costs of uncertain decisions to the cost of incorrect certain decisions, showing the CWDS to be more robust to system parameters than the fuzzy and Dempster-Shafer systems.