Our Theories of Theories of Change: The Social Construction of Transformation, by Peter Jones
“Theories of change are developed using a visual formalism of a logic model, representing a proposed template for action toward outcomes. The endorsement of a given ToC is sustained by persistent reference to it within organizational discourse, in a shared language between an organization and its sponsors or stakeholders, and through the presumption of individual updates to mental models. In this way the theory of change becomes a socially constructed artifact. [...]
The users of ToCs have expanded from funding agencies (many of whom are known for requiring a logic model of change with applications) to impact investing, normative social research, government policymakers. The provision of a working theory and logic model was presumed to represent an empirical and measurable basis, encoded in causal logic, to define how a program’s implementation would develop or result in preferred definite outcomes. However useful these models might be for the organizations involved, the pragmatic effectiveness and the theoretical support for such models is open to question. [...]
In social innovation studies, Paul Brest (2010) discusses both their value to philanthropy, and their issues, and the responses from “skeptics and agnostics.” Agnostics in particular raise the questions to which systemic designers should be attending:
“They believe that it is difficult to create a meaningful theory of change because social problems are complex and ever changing. Rather than spending time and money trying to craft or assess theories of change, agnostics think it is more productive for funders and grantees to focus instead on building great organizations.”
We might consider this outlook representative of any change model however. There are tensions between direct action (that benefits from relationship and learning) and designed interventions (that benefit from analysis of leverage anticipated to effect long-term impacts).
Typically, systems approaches value and build upon systemic reasoning from careful observations, such as leverage analysis of complex systems to determine the most productive investment of efforts or programmatic support. Complexity approaches favor more short learning interventions, coordinated iterations, and experiments to deploy proposals as learning probes.”