The way of the scenario

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Lessons learned

In any exercise of this nature, two aspects are especially important: framing the questions and identifying input data and sources. With regard to framing the questions (i.e., the key elements or conditions to be compared against all three scenarios), a sufficient degree of both orthogonality and completeness is needed. For the COVID-19 project, some key factors included the availability of reliable testing and treatment, the ability to produce and distribute vaccines in sufficient quantities, the status of government stimulus payments to citizens, and estimated time frames for full economic recovery.

Regarding data, most of the inputs came from a growing corpus of curated articles. The availability of such a large assortment of inputs promoted frequent idea generation and discussion. However, as the size of the corpus grew, “citation rings” emerged in which authors repeatedly cited each other’s work. Whether intentional or unintentional, this often results in overweighting particular points of view, theories, and conclusions. This is definitely something to watch out for, especially when applying text analytics to large document collections.

Another problem was the difficulty in determining the impact of trends due to “messy” data. For example, it was hard to distinguish between purely COVID related deaths and deaths involving comorbidities. Also, the presence of little-or-no previous history to use as a baseline limited the ability to perform meaningful extrapolation and trend impact analysis. This was compounded by the challenge of many intertwined problems spanning multiple, diverse disciplines, including politics, socioeconomics, medicine, public health, etc.

As you might expect, notable surprises came up, which quickly made their way into the Real-Time Delphi flow. These included the speed of vaccine development from multiple sources; the extent and strength of anti-vax and anti-mask sentiments; the strength of political divisions; the lack of cohesive planning resulting in competition among cities, states, and countries for personal protective equipment, ventilators, and vaccines; the consequences of committing trillions in economic stimulus; the adverse effects caused by the lack of standard criteria for reopening schools, stores, restaurants, transportation, and sports and entertainment venues; etc. The key lesson here was not to overlook possible “wild cards,” especially when dealing with complex, adaptive sociopolitical-techno-economic systems, not to mention forces of nature.

The report offers two cautions. The first was that “portions of this report are intentionally misleading in order to provoke thought.” In other words, a good scenario “stretches strategic thought” while containing “robust internal logic.”  The second was that one should “never confuse a scenario with a prediction. Effective scenarios reveal possibilities, not predictions.” As KMers, we have the opportunity, even the obligation, to take these lessons and apply them to any and all plausible futures, whether threats or opportunities, across a range of environments, markets, and disciplines.

The role for KM

Scenario planning in today’s environment can be both daunting and unforgiving. A lot of dots need to be connected. But garbage in equals garbage out, and the higher the stakes, the more complete and accurate the data and analysis that are required. Fortunately, for a modest fee, The Millennium Project offers more than three dozen peer-reviewed tools and methods. In addition to Real-Time Delphi, its toolkit includes causal layered analysis, text mining, morphological analysis, relevance trees, simulation and gaming, prediction markets, and heuristics modeling, to name a few. If these are not part of your KM strategic planning toolkit, they should be. The challenges we and our organizations will be facing in the coming months and years will be many, filled with ever-increasing complexity and novelty.

Paul Saffo, who led the development of the third (optimistic) scenario, has been quoted as saying, “In the short term, the pessimists are right, and in the long term, the optimists are right.” During a crisis such as COVID-19, the pain can be pretty intense. But given that we learn from it, we can come out stronger and more resilient than ever, which gives us good reason to be optimistic about the future. And who better than the KM community to take the lead in setting up and facilitating the learning process? Especially if the learning has to be done in real time.

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