In　this　paper,　concepts　of　information　entropy　and　information　granulation-based　uncertainty　measures　are　introduced　in　incomplete　information/decision　systems,　and　based　on　maximal　consistent　block　technique,　some　variants　of　information　entropy　and　information　granulation　are　presented　to　measure　the　discernibility　ability　of　an　incomplete　information　system.　Then,　some　important　properties　of　them　are　discussed.　From　these　properties,　it　can　be　shown　that　these　proposed　measures　provide　important　approaches　to　measure　the　uncertainty　ability　of　different　knowledge　in　incomplete　information/decision　systems.　And　relationships　among　these　measures　are　established　as　well.　These　results　will　be　very　helpful　for　understanding　the　essence　of　knowledge　content　and　uncertainty　measures　in　incomplete　information　systems.