Network Parameters: Autonomy

Where This Fits

Resilience covered how systems survive shocks. This unit covers Autonomy: the degree to which a system can meet its own needs and govern itself. Without autonomy, a system depends on external forces it cannot control.

Network Parameters: The Interface Between Levels

Sitting in between the system and object level, the network parameters are a powerful interface between the top-down and the bottom-up. The network level parameters help to unravel the complexities of the system level indicators, reveal complex system interactions, test and explore them, and find the object level drivers of these behaviors. This gives concrete handles on finding the best intervention points to improve system dynamics.

Network parameters also create a verbal construction set, allowing teams to specify and explore in detail where certain dynamic patterns come from, or can be engaged. Network parameters are on a higher level of abstraction than object level items. Because of this abstraction, it is easier to extract a universal set of parameters for networks.

The standardized network parameters in SiD are split into the RAH system indicators. Their names are acronyms, taking the first letter of each of their parameters. The Resilience set is called CRAFTDCCV, the Autonomy set SSCNE, and those dealing with Harmony are called PEAIE. They have been derived from existing network science literature, and then changed, adapted, and refined in practice over about ten years.

The Playground of System Dynamics

On the network level we find more powerful systemic behaviors that remain hidden to us on the object level. On a network level we are no longer interested in a single flow, object, or element. It exclusively concerns itself with groups of connections throughout the system. The network is about "how," rather than "what."

For example, it matters more to the network level that people can communicate instantly over long distances, rather than what is being said, or what device they use to communicate. The network parameters can be observed from both the top-down and the bottom-up. Because they can be approached from both sides, they are extremely flexible. Combined with their flexible nature, the network level is often a playground for creativity.

Quantifying Network Parameters

Network parameters are used most commonly in a qualitative sense. However, if need be, they can be used to construct quantitative simulation models using agent-based simulation or other techniques. For example, each separate ELSI element can be quantified and related with numerical analysis in relation to each CRAFTDCCV parameter.

The Autonomy Set: SSCNE

SSCNE are the five main network parameters that inform the Autonomy system indicator. Taking into account these five parameters gives you a good grasp of the aspects that influence the self-reliance of the system, such as resource self-sufficiency, as well as the support it can provide to neighboring systems.

• Self-Governance
• Self-Sufficiency
• Circularity
• Network Support
• Efficiency

The Harmony Set: PEAIE

PEAIE are the five main network parameters that inform Harmony. These allow you to investigate to what degree the system has internal tension, or equilibrium within itself. They reflect the balance between peace and war, orderly and disorderly society, and pressure towards revolution and rioting. These are the hardest to quantify of the three sets, and the most influenced by social science.

• Power Balance
• Expression
• Access
• Inclusion
• Equity

Takeaway

Autonomy measures self-sufficiency and self-governance. A system that cannot feed, power, or govern itself is vulnerable to external disruption. The network parameters here (self-sufficiency, self-governance) give you a way to assess and improve a system's independence without isolating it from beneficial connections.