Aligning our Mental Models with Our Problem Domains
Paul Bundick, PhD, Director of Economic Development and Livelihoods at FHI 360, reflects on the changing problem-solving approaches that have dominated the international development landscape over time. He argues that practitioners are now using a third iteration of a mental model which recognizes that systems are evolving and end states can’t be known ahead of time. This current mental model, he explains, requires a new type of expertise where experiments take place at the margins and successes are incorporated iteratively. FHI 360 is hosting a Challenge Conference on September 18, 2015 in Washington, DC that will focus on “Deepening Systemic Engagement.”
One of the common issues we face as funders and practitioners of development is the frequent mismatch between our mental models of a problem and the nature of the problem itself. To think about anything requires an image or mental model. Problem solving is naturally more effective if our models are congruent with the issues we are trying to address. In this era of rapid change, models, like computer operating systems, can quickly go out of date. Change is altering the very nature of reality and therefore the very problems we are trying to solve. As a development community, we are in desperate need to both understand our problem modeling and introduce new model upgrades to bring our thinking and actions more into alignment with the accelerating complexity of our problem domains.
Mental Model 1.0: The Mechanism
The dominant mental model we use in development is the mechanism. I call this conceptual system version 1.0. All mechanisms are deterministic systems. The behavior of the parts are determined by their internal structure and the behavior of the external controller or operator. To operate machines, we pull levers and push buttons. We drive change. If the mechanism doesn’t work, we break it down to understand the parts and replace the piece that is malfunctioning. Our management guides assured us that the right actions, such as following a proven manual of best practices, would inevitably produce right results if the internal mechanism is functioning properly.
Version 1.0 is applied (often misapplied) across our many problem domains. This version is, however, most appropriate for service delivery programs, as we often see in health and education projects, where the capacity building of implementers or system-wide change is not the primary purpose. In service delivery, the measures of success are usually outcomes tightly linked to outputs in linear causal models framed in deterministic language. Version 1.0 also appears in our recent obsession with evidence and randomized controlled studies to prove strong relationships between simple variables. In doing so, best practice is about reducing the complexity of the environment (making it more mechanistic) by disconnecting the problem to be solved from its context and by applying more constraints to reduce variety production. By applying controls and reducing variety, we can manage for results (deliver services more efficiently); or in other words, we can more easily study the artificially constrained and controlled relationship between simple variables.
Mental Model 2.0: Shift Toward Capacity Building
As our world became more connected through advances in technology, our reality got more complicated. Our problem domains changed. The mechanistic thinking of version 1.0 became less applicable across the board and more appropriate to a smaller set of problems. Mental model 2.0 was rolled out. We begin to understand systems in terms of open systems transacting with their environments. The metaphors shifted from mechanisms to organisms. The role of balancing feedback loops in maintaining function, integration, and viability were incorporated into our understanding of system change.
Version 2.0 was applied widely in development under the rubric of capacity building. We focused attention, not on delivering the services ourselves, but working with organizations and governments to build their capacity to do the work for themselves. While we gave up the idea of determinism in version 1.0, we retained a strong belief in our power to influence organizations from the outside—help them to build capacity to transact effectively with their environment. Single loop learning was introduced so organizations could monitor and improve their effectiveness, efficiency, and sustainability against established performance goals provided from the outside. Specialized expertise was required to employ more complicated analytical methods to deal with unanticipated feedback and unintended consequences in problem resolution rather than the far simpler task of training workers to follow recipe-like instructions evident in operating model 1.0.
Mental Model 3.0: Upgrade to Ecologies
As our environment became more densely connected and central control was weakened through accelerating diversity and intensification of interaction across the globe, version 2.0 was upgraded to 3.0. This version introduced a new metaphor of the evolving system or ecology. No longer was it enough to build the capacity of key organizations to do development well but we begin to focus on the evolution of a system such as the employment system, health care or just better functioning private markets.
Ecologies are complex webs of interaction and co-evolutionary change. Reality is relational, contingent, and subject to the disruptive influence of the unexpected. Adding to the prevalent use of dampening or balancing feedback loops in version 2.0, problems framed as ecologies incorporate amplifying feedback as well. A deeper understanding of amplifying feedback and its inherent unpredictable nature made intervening effectively in ecosystems, even for experts, harder to figure out. Amplifying feedback and the related term autocatalytic loops, proved the point that change does in fact change itself.
Using the 3.0 lens, development problems are seen as evolving systems. For a system to evolve, it must have three intertwined processes: variation, selection, and retention. Variety provides the diversity or possibility of change, selection chooses what works, and retention incorporates what has been learned into the ongoing system. Practitioners tackling system-level change have to ensure all three subsystems are working together as one. Versions 2.0 or even 1.0 mental models are often applied to 3.0 problems. Variety production is critical element in the evolution of any system, but an anathema to project managers trying to produce regular and predictable outputs by managing inputs in the delivery of services or conducting controlled experiments. Selection and retention strategies can easily overwhelm variation and bring evolution to a halt. On the other hand, an over proliferation of variation can lead to chaos without appropriate selection and retention subsystems properly in place.
3.0 Problems Require Learning and Adapting
Working with evolving systems requires a different kind of expertise, an ability to read the system and undertake fail-safe experiments on the margins to see what works and then incorporate (retain) into the larger system. The right approach cannot be pre-determined by deduction or analysis but must be built up from experience. The focus is on balancing forces and facilitating movement or directionality of a system toward an ideal that is not yet fully known and subject to modification as we go along. Double-loop learning becomes relevant in 3.0 as mental models are questioned and the parameters of what is selected for and what is retained can be altered. This model can be seen most clearly in our new interest in resilience at the system level, where the ideal can never be fully achieved as an end state. It is more like direction than a state which is contingent upon changing relationships, networks, and conditions inherent in complex environments.
Version 3.0 requires a different approach to development. The “right solutions” must be co-evolved through engagement with stakeholders and not designed by experts beforehand. Experts will still have a role as evolutionary facilitators. But we can no longer act on complex evolutionary problems from outside the system as in versions 1.0 and 2.0. Rather, we must deepen our systemic engagement and act with others to figure out what works and the way forward as we go along.
An Emergent 4.0 Model?
The evolutionary focus of version 3.0 may soon be complemented with a new version 4.0, which is now in formation. Whereas version 3.0 focuses on the evolution of a system within a context, version 4.0 may address the system as a whole—the system IS the context. The new but inadequate metaphor for this emerging holistic lens is a “spatial field,” or a pattern that connects. Last year, at our FHI360 Advancing the Field conference, world renowned systems thinker Peter Senge introduced a prelude to 4.0 with the idea of a hologram, where the whole is in the part and the part in the whole. While this is still a metaphor and beyond our current knowledge, it may hold real promise for understanding threshold-sensitive transformational shifts, deeper levels of learning, as well as why holistic systems are often resistant to incremental change. Version 4.0 will likely incorporate and integrate previous aspects of all three prior versions of our mental models.
Suffice it to say for now, as development workers we must better understand and align our mental models with our problem domains, which are shifting more and more to operating system 3.0. As Otto Scharmer, an economist at MIT and a keynote speaker at an upcoming Challenge Conference in Washington, DC, wrote in Leading from the Emerging Future, “The success of our actions as changemakers does not depend on what we do or how we do it, but on the inner place from which we operate.”