Traditionally, information fusion approaches to meaning making have been integrative or aggregative in nature, creating
meaning “containers” in which to put content (e.g., attributes) about object classes. In a large part, this was due to the
limits in technology/tools for supporting information fusion (e.g., computers). A different synthesis based approach for
meaning making is described which takes advantage of computing advances. The approach is not focused on the
events/behaviors being observed/sensed; instead, it is human work centric. The former director of the Defense
Intelligence Agency once wrote, “Context is king. Achieving an understanding of what is happening – or will happen –
comes from a truly integrated picture of an area, the situation and the various personalities in it…a layered approach over
time that builds depth of understanding.”1 The synthesis based meaning making framework enables this understanding.
It is holistic (both the sum and the parts, the proverbial forest and the trees), multi-perspective and emulative (as opposed
to representational). The two approaches are complementary, with the synthesis based meaning making framework as a
wrapper. The integrative approach would be dominant at level 0,1 fusion: data fusion, track formation and the synthesis
based meaning making becomes dominant at higher fusion levels (levels 2 and 3), although both may be in play. A
synthesis based approach to information fusion is thus well suited for “gray zone” challenges in which there is
aggression and ambiguity and which are inherently perspective dependent (e.g., recent events in Ukraine).