Social Dynamics in a Systems Perspective by Sergio Barile Marco Pellicano & Francesco Polese

Author:Sergio Barile, Marco Pellicano & Francesco Polese
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

In terms of risk management, we are aware of the uncertainties stemming from the non-linearity of situations dominated by ‘butterfly effects’. Social situations are dynamic non-linear systems in which, as illustrated for the finance system, small changes in some of its components may trigger unexpected and large effects in time. These systems are dominated by uncertainty and not by measurable, centrality driven, risk; they may experience unexpected black swans or outliers or extreme behaviours (Taleb 2008). Since it is not possible to anticipate how or when small changes will produce black swans, fitting power laws to already experienced outliers may help anticipating necessary system’s capabilities to deal with the unexpected as and when they happen. This anticipation requires organisational systems with response capacity to deal with distributed outliers that fit power law distributions, avoiding the chaotic overloading of a poorly structured organisational system experiencing connecting cost. Structural recursion becomes a complexity management strategy closely connected to frozen seeds of extreme events. In this perspective, the increasing connectivity of systemic components and their related proliferating environmental complexity may trigger adaptive organisation structures better prepared to deal with the unexpected. Good cybernetics, supported by empirical research, is a must for policy processes aimed at co-evolutionary processes to improve the response capacity of systems, making them more adaptive and resilient (Espejo 2015).

What is apparent is that for a system the 80/20 distribution expresses interconnectivity and complexity while the cognitive schema of people working with Gaussian distributions, of averages and standard deviations, imply orderly events which can be managed as aggregations of independent events. However helpful this latter approach might be to deal with already structured situations, it offers an unrealistic view of a complex world. Power laws give us the chance to build up response capacity to unexpected, problematic situations, which reflect systemicity at several levels. These are risky and unpredictable problematic situations with increasing need for creativity and innovation, beyond the standard responses of linearity. They produce Pareto tails that require distributed adaptation and learning. The English railways’ attempt to cut off the tail of diminishing returns was a recipe to destroy its complexity and functionality. Pareto distributions of unexpected behaviours recognise connecting costs, self-organised criticality and other forms of systemic behaviour that require high variety responses. These behaviours may indicate high variety events lacking appropriate adaptive responses and possibly systems operating in dysfunctional chaotic regimes or fragmented regimes (the first and third complexity management strategies discussed before in this paper) that fail achieving adequate performance. These would be organisational systems failing to co-evolve and adapt to complex environments. Systems would benefit from policies building up constrains in their co-evolution with environmental agents. For complex adaptive systems, these are the hallmark of scalable structures, such as Beer’s recursive structures (Beer 1979).

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