Problem solving at S-II works really well as long as we have solved the problem before. Elliott’s problem solving schema related to states of thinking –
S-I – Declarative (trial and error)
S-II – Cumulative (best practices)
S-III – Serial (root cause analysis, single system)
S-IV – Parallel (multi-system analysis)
S-II – Cumulative state is one of connection. Given a problem, a person with S-II capability can see the pattern causing the problem AND only has to match the pattern to an existing (documented) solution. This is the world of best practices. Best practices work well as long as the problem is one we have solved before. Forty percent of the population can effectively use best practices to solve problems (which is why “best practices” are so popular in management literature).
But, best practices are of little use if the problem is new (we have never solved it before).
S-III – Serial state is one of cause and effect. This is where NEW problems are solved. And, only 4-7 percent of the general population can effectively engage in root cause analysis. COVID-19 presents itself as a problem in all four states of thinking. Initially, COVID-19 was characterized as something we have seen before and could be dealt with using best practices (treat it like the flu). When it became apparent that the contagion rate was higher and the (yet to be defined) mortality rate was higher, the medical community responded with trial and error problem solving (S-I), recommending social distancing, initially no masks, then masks. Trial and error solutions became best practices and now the world is mask-wearing (go figure). But, root cause analysis (S-III) will provide the only inroads to a lasting solution (vaccine).
S-IV – Multi-system analysis will confront the longer term problems of vaccine distribution (capacities and priorities, medical systems) along with economic impacts (economic systems) and social behavior (social systems).
How much trouble do we create for ourselves when we mix up an S-I solution to an S-IV problem?