Download PDF Winter Issue of The Bridge on Complex Unifiable Systems December 15, 2020 Volume 50 Issue 4 The articles in this issue are a first step toward exploring the notion of unifiability, not merely as an engineering ethos but also as a broader cultural responsibility. Addressing Social Displacement Friday, December 18, 2020 Author: Christopher G. Glazner For decades this country has seen the decay of former industrial centers, the rise of opioid addiction, an increase in chronic homelessness, widening economic inequality, and the overrepresentation of minority populations in the criminal justice system. No one desires these outcomes, and there usually is no shortage of ideas for how to fix them, especially after an event brings them to the public’s attention. Laws have been passed, policies enacted, and funds appropriated to address these symptoms of social displacement, yet they continue to fester, resistant to all efforts. Why? Focusing on Symptoms Instead of Systems These pernicious symptoms are the proverbial “tip of the iceberg” that result from the dynamic behavior of complex systems. Social systems, like all complex systems, are highly interdependent with significant feedback and nonlinear relationships. For every action, there are second- and third-order effects, which are often not anticipated. While it may be possible to understand a small part of a system, it is not usually possible to predict how changes to that part will change the behavior of the larger system over time. For example, in a community with a large number of homeless persons, emergency housing shelters may be funded to give them safe shelter and the opportunity to rebuild their life. Over time, however, it becomes clear that the presence of these shelters can act in multiple ways to limit society’s long-term ability and willingness to help the homeless population make a lasting recovery. For many thorny social issues, the focus is often on “quick fixes” that are intuitive, easy to communicate, and closely related to the symptoms to be addressed. However, quick fixes, like the funding of emergency shelters for the homeless, often fail over the long run. “Fixes that fail” appear so frequently in public policy that they are deemed a systems thinking archetype: a common pattern in systems that leads to expected system behaviors. In the classic application of fixes that fail, short-term actions are taken to address a symptom of a system. The fix may work temporarily, but it does not address important underlying drivers of the symptom, which eventually overwhelm the fix. The Iceberg Model In all complex systems, but especially social systems, people respond first to events that they can directly observe. They see the unjust actions of police, homelessness encampments, a town’s closed factory. The first impulse is to take immediate action to address these events, and that becomes the goal in implementing social policy. A complex system requires looking deeper than events to understand where to take action. The practice of systems thinking provides the iceberg model to help guard against premature solutions in systems. In the iceberg model, events in a system are the visible tip of the iceberg; system understanding comes from looking at successively lower levels “underwater” for opportunities to effect change. The first level “below the waterline” involves understanding events as part of patterns of behavior over time. Patterns of behavior in systems are driven by underlying structures of causal effects and influences, which is the next deeper level in the iceberg. At the deepest level are the underlying mental models and beliefs of the system. We can have more impact in a system with less resistance by influencing the underlying structure that gives rise to behaviors, or, even more effectively, by shifting the mental model of the system through changes in the policy objectives of a social program. Social policies based on a deeper system understanding can have more lasting effect compared to the common fixes that fail. Effective VA Efforts for Homeless Vets In the early years of the Obama administration, the US Veteran’s Administration (VA) took on the daunting challenge of ending homelessness among veterans. The VA leadership understood that this was a complex system; for many years the number of homeless veterans, who were disproportionately represented in the homeless population, had proven very difficult to reduce despite a number of funded, supporting programs. In an effort that began in 2010, the VA adopted a systems engineering approach that brought -together a variety of stakeholders, from social scientists to statisticians, who developed a holistic plan to reduce homelessness among veterans. As a member of this group, I worked with colleagues from government, academia, and the nonprofit sector to develop dynamic systems simulation models that helped us understand why programs had performed as they had. We developed models to support budget requests to Congress for new programs designed to break the “fixes that fail” archetype and lead to a long-term reduction in veteran homelessness. This model-driven analysis provided confidence in the VA’s program design and contributed toward the full funding of programs such as VA Supportive Housing for thousands of veterans, which helped break cycles of their flow between shelters and the streets. This was possible in part because diverse stakeholders came together, communicated their mental models of the system from different perspectives, and used data and simulation models to explore . Stakeholder Mental Models In efforts to address any social problem, stake-holders and policymakers must commit to sharing mental -models explicitly in order to build deeper system understanding. Once a shared understanding is achieved, the tools and approaches of complexity can be brought to bear. For example, if stakeholders team up with modeling experts, they can use graphical techniques such as -causal loop diagramming to visually develop their -mental models. This process requires stakeholders to make their reasoning and understanding explicit and visual, enabling them to more effectively communicate their perspective to others. If this process is repeated for multiple stakeholders with different perspectives, the models can be used in conjunction with data to build simulations, exposing competing mental models to analytical scrutiny and providing a rigorous platform to evaluate ideas to solve social challenges before testing them on the general population. Conclusion While this approach does not pretend to solve all social problems, it is more likely to identify leverage points deeper in the iceberg and thus yield lasting impact. It also provides a powerful tool to test potential solutions and better understand their impacts from all angles. A systems approach can avoid quick fixes that address only the symptoms of a problem and inevitably fail to effect the broader systems changes that are so desperately needed. The author’s affiliation with The MITRE Corporation is provided for identification purposes only and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author. About the Author:Christopher Glazner is a senior principal systems engineer and chief engineer of the Systems Engineering Innovation Center at The MITRE Corporation.