Systems and Complexity

Scalable Analysis

Published in Open Source Futures
Feb 2, 2018

In the preceding posts about trends, I have focused on how the different trends interact with each other. I do this because of an understanding that trends are the outcomes of systems of processes; the trend that we see is often the resulting phenomenon. We still do trends analysis, however, because they often can get at the important aspects of the systems that we discuss.

The contemporary thrust of systems thinking began with Donella Meadows and her associates in creating modeling software that could help people visualize the systems they were thinking about. Part of this effort led to the book Thinking in Systems, where she together with her coauthors laid out the principles for affecting change in systems. Donella Meadows was also part of the team led by Jay Forrester in creating a systems model for the world. Those forecasts, made for the Club of Rome, spelled out how the world was heading to an “overshoot and collapse” situation — a common feature of population explosion in an environment of finite resources.

Systems thinking looks at how different components interact and the importance of the indicators and the incentives that drive the behavior. Change the indicators and incentives, and behaviors change, and systems change. There is a much larger philosophy to this. Donella Meadows described the things that, if changed in a system, could lead to impacts, with the greatest (and hardest) impacts to be gained at the bottom.

12. Constants, parameters, and numbers (such as subsidies, taxes, standards).
11. The sizes of buffers and other stabilizing stocks relative to their flows.
10. The structure of material stocks and flows (such as transport networks population age structures).
9. The lengths of delays relative to the rate of system change.
8. The strength of negative feedback loops relative to the impacts they are trying to correct against.
7. The gain around driving positive feedback loops.
6. The structure of information flows (who does and does not have access to information).
5. The rules of the system (such as incentives, punishments, constraints).
4. The power to add, change, evolve, or self-organize system structure.
3. The goals of the system.
2. The mindset or paradigm out of which the system — its goals, structure, rules, delays, parameters — arises.
1. The power to transcend paradigms.

Complexity sciences also look at various non-linear relationships in networks in different domains of sciences. The same techniques for examining ecologies and food webs are also useful for looking at social networks. Complexity sciences also share an affinity with information theories in looking at how simple rules can generate order out of disorder. This is not to say that these order generation systems break the second law of thermodynamics but that the order that arises from these simple rules can be quite startling.

There are various features of complexity that people should be aware of:

• networks
• thresholds and phase transitions
• normal accidents

Networks can create path dependence for innovation. An initial decision to pick California as the site of WW2 research can have lasting consequences half a century on, as Silicon Valley became established. We see it in the geography of innovation, as when centres are established, it can be difficult for new centres to compete.

Social networks show themselves to be powerful and necessary. Robert Putnam showed that there are at least two kinds of social capital — bonding capital that helps a group cohere together, and bridging capital — for links and brings different groups together. The strength of both social networks becomes important during periods of disasters as people and companies form linkages to assist each other.

Systems and complexity focus on how things interact with each other, causing unintended and non-linear outcomes, and generating surprises. These surprises are usually threshold events, when processes occur at a rate that changes the existing environment, leading to a different paradigm with different rules. Phase transitions is a useful term in this respect, as how water changes into ice, or when it changes into steam. In these different states, H2O takes on different volumes and behaves differently.

World Wars, and the Depression are usually signals of phase transitions, where the old rules are irrelevant and new rules apply. Or like the turkey, which gets fed until Thanksgiving Day, and promptly gets slaughtered.

The climate is a system where phase transitions are unclear. Although we can model the climate and the possible changes in precipitation patterns, much of it remains guesswork. We don’t know if the North Atlantic Oscillation is going to remain, or what would happen if the ice sheets in Greenland collapsed. But we do know that the new climate will be different enough from the present day, and this is one basis for the nervousness regarding climate.

Social systems are also prone to phase transitions. A youth bulge without productive channels for their energies could mean changes in the socio-political order. The classic case for such phase transitions is the various social revolutions throughout history, where different social classes fight to change or to preserve the status quo. The American Revolution removed the colonial rule and replaced it with an American-led government; the Bolshevik revolutions replaced the Russian aristocracy with a new group of educated elite imposing authoritarian rule. The French Revolution temporarily replaced the monarchy with a group of intellectuals, in turn, replaced by a military ruler, and later became a republic. Physical depravations might be one contribution to violent dissent, but even these social movements tend to require ideas, leadership, and some military power to be successful in changing the social order in a region. It is difficult to tell whether one attempt at revolution will be more successful than the other.

And so we come to the idea of “normal accidents” — how catastrophes come about. It is often not because of a single decisive factor, but rather an accumulation of small events that lead up to a catastrophic outcome, in what is termed a failure cascade. In the Three Mile Island nuclear accident, various

valves that were supposed to be functioning were not, although indicators showed that they were. Operators faced a variety of alarms, that disoriented them, leading to more flawed decisions, which worsened things.

There is certainly more I could go into in a more systematic way, and what I’ve done here is to give a sense of an introduction into systems/complexity sciences. There is certainly more to this area than what I have alluded to here, and these are the concepts in my mind as I think about constructing scenarios and thinking about how trends interact.

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Systems and Complexity