What is it about?

If we recast our data analysis strategies to nurture the multiple roles (looking in/out from skin, family, and culture) that folks play in their communities, this might increase a trend toward balanced narratives on-line, and at the same time help us harness the power of task-layer diversity i.e. the fact that we all have different strengths. For example, what kind of model for task-layer multiplicity/diversity in the community pictured above might we come up with? In the 19th century, center-of-mass (community-wide) multiplicity might have been about 5.6 out of the 6 maximum, with geometric (average-individual) multiplicity closer to 3.2 compared to about 4¼ for the uniformly-distributed case. The specialization index would then be about 5.6/3.2 ≈ 1¾, which is less than the specialization index of 2 expected for a purely yin-yang community (with e.g. male-female role specialization) but more than the index of 1.4 expected when all types of assignment are equally probable.

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Why is it important?

Global electronic communications are excellent at propagating unbalanced narratives, pushing us from one extreme to another. Even scientific analyses generally treat only one layer of organization at a time. By following up on a simple strategy to examine the effects of disasters and policy changes on all layers of activity important to individual humans at once, we might be able to shift the conversation toward balanced narratives that heal rather than divide.


A multilayer approach to characterizing community-level health (with or without deep roots in the way complex systems evolve) could be a great place for folks to pool their own insights about strategies that might work. We all have such insights, I suspect...

P. Fraundorf
University of Missouri at Saint Louis

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This page is a summary of: Task-Layer Multiplicity as a Measure of Community Level Health, Complexity, July 2019, Hindawi Publishing Corporation, DOI: 10.1155/2019/1082412.
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