Evaluation in a complexity frame

In this chapter we seek to understand evaluation in terms of approaches to application of complexity-consistent or -congruent approaches. This shift to complexity framing is the most significant development because of its fundamental implications for the construction of the research and the interpretation of findings. We review a set of approaches that are consistent with complexity that have been much more widely applied than in the explicit complexity evaluation literature. This shift in thinking, or as we characterize it, this process of recognition, is necessary so that commissioners in particular will be convinced of the need to engage with evaluation in this vein. We draw on insights from qualitative research that acknowledges the iterative rather than linear nature of the process. Time becomes an important constituting element and the observation of system regularities over time () is discussed in just the way we have noted E.P. Thompson does for social class. We take issue with the simple/complicated/complex categorization in evaluation because it ignores ontological complexity and focuses on intervention complexity. The chapter focuses on the role of social theory in evaluation to remind us that several levels of theory are relevant. Identifying the significance of a relationship to grand theory, we examine the lesser degrees of abstraction offered in theories of the middle range as contributing to complexity account. We note the dangers of using complexity and systems methods without fully embracing their theoretical underpinning, which results in a reintroduction of positivist ideas of objectivity. The discussion of complexity and a conscious theoretical dialogue around it is an ongoing project in evaluation and, in our view, one that must continue in order to strengthen our understanding of complex adaptive systems and to maintain the essential reflexivity in their exploration.

Frame (networking) · Telecommunications · Competitive and Knowledge Intelligence · Complex Systems and Decision Making · Computer Science