Making Sense of Sensemaking 2: A Macrocognitive Model

For pt.1 see ibid., vol.21, no.4, p. 70-73 (2006). In this paper, we have laid out a theory of sensemaking that might be useful for intelligent systems applications. It's a general, empirically grounded account of sensemaking that goes significantly beyond the myths and puts forward some nonobvious, testable hypotheses about the process. When people try to make sense of events, they begin with some perspective, viewpoint, or framework - however minimal. For now, let's use a metaphor and call this a frame. We can express frames in various meaningful forms, including stories, maps, organizational diagrams, or scripts, and can use them in subsequent and parallel processes. Even though frames define what count as data, they themselves actually shape the data Furthermore, frames change as we acquire data. In other words, this is a two-way street: Frames shape and define the relevant data, and data mandate that frames change in nontrivial ways. We examine five areas of empirical findings: causal reasoning, commitment to hypotheses, feedback and learning, sense-making as a skill, and confirmation bias. In each area the Data/Frame model, and the research it's based on, doesn't align with common beliefs. For that reason, the Data/Frame model cannot be considered a depiction of commonsense views