A Critical Inquiry into the Value of Systems Thinking in the Time of COVID-19 Crisis

Abstract

The COVID-19 pandemic offers an historic precedent to review and challenge the values of social, economic, environmental, and cultural belief systems. The concept of the “New Normal” and the experience of the global pandemic provide points of transition in thinking about our relationship to our planet and to each other. These include the fragility of contemporary economics, dependency on industrialized urban infrastructures, and reliance on top-down governance, vulnerability to climate disasters, dislocation from the natural world, societal inequalities, and the loss of cultural memory. The paper considers the potential role of systems thinking in attempting to manage societies’ responses to the pandemic. To provide the methodological context in which some systems thinking can be applied to alleviate the pandemic, we conduct a focused literature review of systemic frameworks, and using examples from Brazil and England, the paper questions the validity of existing disaster management systems and proposes an integrated critical systems approach. Reflecting on these experiences, questions of systems criticality are further developed and considered in relation to critical recovery from disasters and as integral critical systems (ICS) to interrogate the intention of systems. Finally, the paper reflects upon the value of systems and the values embedded in systems that may or may not promote equitable well-being in recovery from disasters such as COVID-19.

How can systems thinking help us in the COVID‐19 crisis?

“…Systems thinking may be perceived as a certain kind of epistemological antireductionism and serve as a way of solving complex problems by “seeing both the forest and the trees” (Klement, 2020b ). Even though systems thinking may offer an effective way to investigate upon the causes of the COVID‐19 crisis, to diagnose the present situation and to propose possible and sustainable solutions, it should not be treated as the panacea for the pandemic (Haley, Paucar‐Caceres, & Schlindwein, 2021 ). The quality of the proposed solutions greatly depends on how adequately complexity is defined and conceptualized (Jackson, 2020 ).…”

Causal Loop Diagramming of Socioeconomic Impacts of COVID-19: State-of-the-Art, Gaps and Good Practices

“…Additionally, we find that there is no clear pattern between a motif's prevalence in CLDs and its size (measured by the number of components), as well as between a motif's prevalence and the date of its publication, i.e., more recent CLDs do not necessarily contain more complex causal structures than the early maps. 9 (z-score) of the number of motifs across 1000 realizations of the randomly generated graphs. The red mark depicts the actually observed indicator standardized in the same manner, so the red mark denotes the number of standard deviations by which the actually observed number of motifs differs from the mean of the distribution.…”

“…The red mark depicts the actually observed indicator standardized in the same manner, so the red mark denotes the number of standard deviations by which the actually observed number of motifs differs from the mean of the distribution. 9 (z-score) of the number of motifs across 1000 realizations of the randomly generated graphs. The red mark depicts the actually observed indicator standardized in the same manner, so the red mark denotes the number of standard deviations by which the actually observed number of motifs differs from the mean of the distribution.…”

“…Systems thinking has been suggested to unravel this challenge by accounting for essential links and feedback loops between issues that both scientists and policymakers tend to Systems 2021, 9, 65 2 of 17 consider in isolation, creating a shared understanding of the problem and identifying potential leverage points [7,8]. Some scholars responded to this call advocating the use of systems thinking in a rather general sense [9][10][11], while others came up with some concrete examples of the application of systems thinking, usually through employing causal loop diagrams [2,4,[12][13][14][15][16] or system dynamic models [17][18][19].…”

“…Figure 3. Grey dots represent the standard score9 (z-score) of the number of motifs across 1000 realizations of the randomly generated graphs. The red mark depicts the actually observed indicator standardized in the same manner, so the red mark denotes the number of standard deviations by which the actually observed number of motifs differs from the mean of the distribution.…”