In this study, Fuzzy Cognitive Maps (FCMs), which are powerful tools for graphical representation of knowledge, are analyzed from an ambiguity and fuzziness perspective. In conventional FCMs the causal strengths are represented with singleton (crisp) fuzzy numbers, but recently, other researchers proposed different FCM structures where uniform (interval) or triangular fuzzy numbers are used in causal strength representation. Here, FCMs are analyzed by means of fuzziness and ambiguity measures that are proposed in literature to investigate the capability of models to represent uncertainties. In addition, two new measures, called the average ambiguity measure (AAM) and the average fuzziness measure (AFM), are proposed to indicate uncertainty representation of an FCM. A well-known FCM model of a public health system is used as a case study to show how the fuzzy weights determine the uncertainty representation of FCMs, and then the outcomes are discussed.