ELSI: Cross-Domain Effects Part 1 (continued) (continued)

Increasing these network parameters through law or policy will lead to increased Harmony in the long run. Harmony is typically tracked in space and context for social justice issues of today. Mapping Harmony in time leads to questions concerning future generations, as well as learning from past mistakes. From the dark to the light Harmony is an immensely powerful aspect when used in the right way.

If you internalize that Harmony is not only about preventing harm and to stop injustice, but about boosting positive developments as well, new avenues open up. We can achieve great systemic improvements when systems are tuned towards aspects such as happiness, balances between freedom and responsibility, and the support structures to guide these in a healthy way.

These Harmony dynamics can make systems move by harnessing the internal tensions for good, by engaging internal willpower, passion, and fascination. In many cases, when smartly implemented, positive impacts on Harmony can be achieved in most projects with little effort, and a great return on investment.

Harmony can also be seen as a measure of how much each individual can strive for personal flourishing in a system. For example, if a company is able to channel the passion and dedication of its employees to create value, it will be a vastly more effective organization.

Harmony’s dynamics live deeply rooted in our society. It’s fast-moving, and can shift focus rapidly. In the last decades we’ve seen issues such as poverty, gender equality, LGBTI rights, labor practices, and many more receive significant global attention, much to the benefit of specific communities and human rights in general.

In this light, Harmony and Resilience go hand in hand, sharing the needs for transparency and awareness first and foremost in the path towards sustainable change. Time Daylight Traveler Food Ecosystem Advertisement put in picture of network evaluation sheet bottom-up network exercise Imagine a social network. Let’s call it Numbernet. Numbernet can be used to connect to friends online.

Now, do the following three things: Only considering the social network aspect (not the technical aspects): go down the list of 9 resilience network parameters one by one, and determine what simple formula would be useful to inform each network parameter. Do the same, but now only considering the technical aspects (devices, servers, etc).

Point to the network parameters that are likely to influence the success of the network most. Examples Connectivity The total amount of users multiplied by the total amount of connections between all users. Awareness The number of users that will see a message of importance that is posted by a single user in a 24 hour period.

• or -

The time it takes for an important message to reach 90%+ of the network’s users. Redundancy The number of devices a single user can access the network on.

• or - The number of backup servers for each critical node in the technical system

Flexibility The time it takes to make or break connections between users. Transparency The time it takes for a message to travel from one user to another.

• or -

A calculation of all messages multiplied by the amount of people allowed to see the messages, divided by the total amount of messages. Diversity The total amount of users divided by the different types of users in a certain chosen category (age group, nationality, etc). Centrality The median number of connections per person divided by the average number of connections per person. Complexity The total amount of users multiplied by the total amount of connections.

Validity The total messages transmitted to users divided by the network’s consensus (or scientific consensus) of the validity of the messages. Top-down network exercise Take an imaginary country, which we will call Billistan.

Conjure up an image of a small country with a few dozen towns about a few hundred years ago and see which resilience network parameters you can influence by adding a modern technology, or policy measure. Do this for all the network parameters. news: connectivity and awareness Imagine that there is no form of news propagation in Billistan: no radio, TV, newspaper, or otherwise.

This means the ‘connectivity’ network parameter is low, and subsequently also the ‘awareness’ indicator of Billistan’s network is low. Its effect could be, for instance, that one town within Billistan will only hear of another town’s failed harvest if someone from one village happens to pass through and deliver the news to the other village.

This may result in famined villages collapsing, while they could have been helped by other towns (and in return help with something else the next year). So, we can conclude that this reduced connectivity and awareness reduces the resiliency of the country as a whole.

Adding a news network to Billistan increases its connectivity and awareness, and if the news stays true (validity), it will withstand something like famine better, and thus increase its resilience.

This comes at a cost of efficiency, because some people need to dedicate themselves to making news, and can no longer produce resources. Effects of censorship In another example, consider an oppressive political system which censors bad news. This lack of transparency produces a great disparity between perceived awareness and reality.

Consequently, this reduced awareness harms resilience. Censorship reduces the transparency of the news to such a degree that bad news is not allowed to be reported, and the actual awareness value of the news is reduced.

In this case, it may be even though of as worse, because perceived awareness and actual awareness may diverge, possibly reducing the effort to increase awareness. Diversity of education Consider a different resilience network parameter - diversity. If Billistan has a low diversity of education, social class, and background, it’s conceivable that there is great consensus in opinion and perspective.

This in turn likely generates a single echo chamber which jeopardizes resilience. Diversity often positively contributes to resilience. For example, a diverse population provides varied know-hows that could make or break its emergence through a crisis.

Diversity in life-styles and population demographics increases resilience through disease-resistance, increased creative power, and flexibility. the resilience network parameters CRAFTDCCV resilience network, CRAFTDCCV in Detail The resilience network parameters concern the whole of the collection of objects and their inter-object relationships, in time and space.

These nine holistic network parameters look at the composition of the network, and together they help to determine a system’s resilience. In this next section we explain the meaning of these parameters and some of our experience with what they mean for creating a resilient state of a complex dynamic system.

The 9 resilience network parameters are: Connectivity Redundancy Awareness Flexibility Transparency Diversity Centrality Complexity Validity In some cases, it helps to subdivide these 9 parameters according to types, as follows: Structure These three parameters mostly deal with how the network is structured: the amount of connections, and the network’s (physical) structure.

Connectivity Redundancy Centrality Character These three parameters most often deal with the character of the system’s network; how fast it can react, and the diversity of its composition. Flexibility Diversity Complexity Content These three parameters reflect on the content of information traveling around the network. To what degree nodes are aware of this information, the speed of transfer, and truthfulness of information. Awareness Transparency Validity “For every complex problem there is an answer that is clear, simple, and wrong.”

• H. L, Mencken

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Takeaway

Autonomy and Harmony dynamics interact in complex ways. A system's self-sufficiency affects its internal tensions, and vice versa. Media transparency, political inclusion, and resource sovereignty all demonstrate how RAH indicators cascade across ELSI domains in real-world systems.