Using Complexity in Nature to Understand the Safe Network

Complexity is vital to the organizational patterns shown in decentralized systems. As a recurring theme when discussing the Safe network, the relationship to natural phenomenon can help less technical individuals better understand how the system works. From the most basic of structures to the systems which encompass planets and galaxies, there are nearly infinite examples of nature organizing itself. The Safe network is a global system built with these phenomenon in mind in order to achieve self-regulation, self-rehabilitation and self-organization without a central party.

David Irvine commonly references ant colonies which show extremely complex behavior rooted in the cooperation and communication between individual ants — similar patterns also develop in other social insects. It is worth noting that the very basic sets of rules which individual ants follow helps with focus and conserving resources while the division of labor they exhibit allows those basic rule sets to be diverse when accounting for the entire colony working together. While both types play important roles in the colony, there is no need for soldier ants to know the tasks of forager ants (and vice versa) thus a division of knowledge minimizes the amount of brain processing which in turn reduces energy usage.

Similarly in the Safe network, vaults created by individuals are assigned by the network to a specific persona which will enable it to follow only a specific set of rules. These personas and the correlating rule sets are for managing parts of other vaults in the network. Though there are several aspects to manage for each vault, those managing personas will always be distributed across many vaults and will never be concentrated in one. Like with ants, basic rules within a division of knowledge/labor mechanism can conserve resources for the individual vaults while enabling a wider variety of functionality to the network as a whole. Additionally, ranking mechanisms based on vault health and resourcefulness to the network determine which vaults are assigned which managing personas.

At a similar scale to ants, the creation of swarming patterns in birds and fish depict another great emergent behavior that only arises through interactions with many components. Natural, emergent, decentralized systems are evidence that cooperation is evolutionarily beneficial to achieve a higher level of organization and general progression. Systems of interacting organisms create incentive to cooperate and to fend off both external offenders and internal threats.

Decentralized order can also be found in the interactions of non-living objects. An example at a much smaller scale is in snowflakes and coral reefs which create fractal patterns from the shapes and movement patterns of independent water crystals and ocean minerals respectively. At a much larger scale, weather patterns form due to relative air pressure between points on the globe and because of these patterns we are able to predict future behavior. The complexity shown in weather is a great illustration of the balancing effect between order and chaos at a global level and is a great comparison for how the Safe network can manage storing chunks of distributed, encrypted data. Like all forms of complexity, the Safe network will be constantly morphing and optimizing in its never ending quest for equilibrium.