Network Parameters: Autonomy

1.2.5 Network Parameters 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, in turn, gives concrete handles on finding the best intervention points to improve system dynamics. The network parameters also create a verbal construction set, allowing teams to specify and explore in detail where certain dynamic patters come from, or can be engaged. Network parameters are on a higher level of abstraction than object level items. Because of this abstraction, it’s easier to extract a universal of parameters for networks. This standard set is printed on the left page. While standardized, for each challenge, we still check if these fit or are relevant on a case-by-case basis, but they are a great start. You can also choose to use a simplified set. 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 paramters. While their names are awkward, this may help you remember which paramters are in each set. The Resilience set is called CRAFTDCCV, the Autonomy set SSCNE, and those dealing with Harmony are called PEAIE. They’ve been derived from existing network science literature, and then changed, adapted, and refined in practice over about a 10 year period. On the next pages in this section, we discuss all parameters in detail, but first, we shortly look at the nature of these sets of network parameters. The network-level is all about collection of connections. This means each parameter reflects on all of the relationships in the system related to that parameter. These relationships can then be split into the object level to be investigated in greater detail. For example, the resilience parameter of diversity reflects on the diversity of all the relationships in a system. You can split this ‘diversity’ network parameter further up with ELSI, to specifically look at the diversity of energy systems, or transport systems, financial relations, and so on. 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’s being said, or what device they use to communicate. The network parameters can be observed from both the top-down as well as the bottom-up. From the top-down, they’re evaluated on how they contribute to the RAH system indicators. From the bottom-up, they are constructed by looking at the various connections between areas of interest on the object level. Because they can be approached from both sides, they are extremely flexible. In addition, they derive much of their meaning and definition to the context of the challenge at hand. Combined with their flexible nature, the network level is often a playground for creativity. Quantifying network parameters N etwork 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, a top-down evaluation of resilience, by scoring each CRAFTDCCV network parameter in a qualitative way, can also be investigated in a quantitative way from the bottom-up. 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 for this to neighboring systems. The SSCNE set lists as follows: 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. These are 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. The PEAIE set consists of the following: Power Balance Expression Access Inclusion Equity