ELSI: Cross-Domain Effects Part 2 (Part 2)

Transparency

Transparency is a measure of the connection speed between nodes in a network. Speed may be affected by layers of transmission and/or opacity. For example, hierarchies require clearance from each subsequent level for information to filter down the chain, hence hampering speed.

In the case of opacity, a bureaucrat may deliberately prevent civilians from gaining insight into governance. High or Low? Low Transparency within a system implies difficult information transmission between nodes. Systems with low Transparency therefore have a slower reaction time than a high Transparency system. High Transparency seems beneficial across the board.

In technological systems, the network throughput speed is often a limiting factor for the performance of the entire system. In governance, social, and cultural domains, Transparency also directly impacts the network parameter harmony. Take for example a corrupt government that takes advantage of lack of transparency to cause division between groups of people.

Using propaganda to create fear for immigrants, for example, has an adverse impact on Harmony. Validity Validity is the truthfulness of information transmitted in a network as reflected on the objective observations of all of the nodes in the system.

Measuring Validity may provide a pivotal insight into a system or society’s health. Validity plays an important role in societal systems where Transparency and Awareness also interact to interchange information. In technical systems, validity will be analogous to the reliability of information transfer, or subsequent corruption of information.

Another interesting use of Validity is in the form of ‘true costing’. This is the practice of determining a monetary value for all externalized factors, in order to make a more ‘truthfully’ weighted economic decision. Natural Capital and True Pricing are forms of this. An economic or financial decision is less valid if some factors have not been considered.

Feedback Between Domains

High or Low? In most cases one would want the Validity of a system to be high. This means that the information that’s passed on from one agent to another is ‘true’ according to the evaluation of their peers, as well as uncorrupted from its originating source. A small amount of corruption of information may be healthy in any system, however, to keep the error-checking systems healthy, active, and alive.

Resilience Parameter Variations There are other useful indicators to analyze Resilience. Most of these have something to do with a spatial or temporal mapping of the network, and could be captured in an accurate mapping of the above indicators in space and time. For instance, the indicator ‘Reach’ is used often in existing research on system dynamics.

Reach can be effectively covered in SiD’s system by mapping a combination of Awareness and Connectivity parameters in space. An indicator such as ‘Transmission speed’ can be captured by mapping the Transparency indicator in time. Otherwise, specific indicators can be used for specialized network analysis situations.

Examples of other parameters we have used are: Coherence Accessibility Sensitivity/Responsiveness Entropy Rigidity/Sturdiness Usually, these aspects can be found in a combination of the CRAFTDCCV parameters, but it may be useful to add or replace parameters where the need arises. the autonomy network parameters sscne autonomy network: SSCNE in Detail The system indicator of Autonomy is naturally more associated with the physical, such as the availability of power and the recycling of resources.

As with all the system and network parameters, these influence each other across the board. E.g. a more decentralized system affects Resilience greatly, but may also affect Autonomy. The following parameters are useful to evaluate Autonomy, in addition to those for Resilience.

Self-sufficiency The measure to which the system can fulfill its own basic needs and beyond Self-governance The measure to which the constituents of a system can govern themselves Circularity The measure to which resources in the system are, and can be re-used, in a closed loop Network support The system’s ability to support neighboring systems in case of calamity Efficiency The amount of agents and assets contributing positively to the system in relation to their cost. Self-Sufficiency The most important indicator of autonomy is self-sufficiency.

Self-sufficiency relates to the self-production of elements that are vital to a system’s operation. For example, when talking about a town, we refer to elements such as drinking water, the required power for essential operations, food, and so on. A system is self-sufficient if it produces these in large enough quantities that when supply from the outside world is cut off, it can continue to operate. There may be a term limit to this.

It’s possible to measure self-sufficiency in time. For example, if a town can survive with its resources for one year before its grain or water storage is depleted, its self sufficiency is that one year. While that may be a great achievement in our current society, one year can be considered reasonably low in light of things we might be facing, in terms of drought, crop failure, storm flooding, etc. As noted before, Autonomy and Resilience can bite each other.

Too high of a level of Autonomy may impact connectivity, flexibility, or other network parameters and thus undermine Resilience. For human communities, in order for that not to be the case, self-sufficiency should focus on the essential required resources.

These are not just water, food, and power. These also include, for example, the capacity for managing waste, public order, basic health treatments, basic economic operations including work and value exchanges, communications, and essential public transport, as well as cultural expression and social connectivity.

And, of course, the capacity to maintain and service these elements, and provide training for their continued operation. The other extreme, of things that are not part of it, are those that are not vital to operations or are of such complexity that their decentralized distribution would lead to large resource losses.

For example, while having transport vehicles is of high value to a town, and a repair station for them may be considered an essential item for self-sufficiency, every town having its own vehicle factory is excessive in terms of resource utilization. It helps to conceptualize self-sufficiency in light of unexpected calamities. The autonomy of a town should be high enough to support its own basic operations indefinitely in case of most unexpected calamities.

The network of towns could then support the non-essential elements. A country, conceptualized as a network of towns and settlements, increases what are ‘basic requirements’ to all essential operations of a country.

For example, while a single town may not need a university as an essential basic service to continue to operate (people can go to another town for it), a country as a whole certainly does (there needs to be one for all the town to be able to get to).

Therefore, the self-sufficiency of a resource or a system is intricately bound to an understanding of its scale and its relation to the network. Scope of self-sufficiency It is often helpful to define a scope and the degree with which self sufficiency should be reached. This scope determines which services are included in the set of basic resources. This then determines the living standard in times of need.

Again here, the scope should not be in excess, to avoid ostracizing the community. A scope that is too low though may threaten self-sufficiency, or the capacity for network support. The degree of self-sufficiency has to do with how long, or to what extent items in the scope are self-sufficient. For example, when making a plan for a self-sufficient housing neighborhood in the Netherlands, we decided that food, electricity, heating, water, and waste were in the scope.

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Takeaway

Network parameters like connectivity, redundancy, and transparency are not abstract metrics. They directly determine how well a system detects threats, distributes resources, and recovers from disruption. Measuring these parameters across ELSI domains reveals the structural health of a system.