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the agora as a social knowledge graph.md
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- On a system level, there exists a [[reference Agora]] that provides a minimum viable implementation for the [[underlay]], [[interlay]], [[overlay]] components of the [[distributed knowledge graph]].
- We are using said reference Agora to refine the proposed system and run experiments.
- Some hypotheses that we are testing:
- Individual contributions can be useful on average when served best-effort in a social context provided only an cheap additional stance (at little extra effort over baseline), and this mechanism benefits from network effects.
- Individual contributions can be useful on average when served best-effort in a social context at the cost of adopting a default social stance (at little extra effort over baseline), and this mechanism benefits from network effects.
- A [[knowledge commons]] model can provide utility to participating communities efficiently, as the cost of systemic integrations with a hub design such as the Agora can scale with `O(N)` instead of the `O(N^2)` provided by a naive full mesh.
- Social composition and integration might be sufficient to yield meaning and sufficient order, or at least significantly complement both taxonomic approaches (hierarchical) and individual-scoped eventual convergence (non hierarchical).
- Social [[composition]] and [[integration]] of notes might be sufficient to yield higher level meaning and order, or at least significantly complement both taxonomic approaches (hierarchical) and individual-scoped eventual convergence (non hierarchical).
- A composition of personal [[hierarchies]] yields a social [[heterarchy]].
- The social context afforded by an Agora provides a path towards faster eventual convergence (that is, `K` people contributing to `N` nodes as categorized using loose or implicit personal ontologies can converge on useful emergent categories as `K` increases).
- Going from a set of voice-preserving [[individual contributions]] to a shared [[group resource]] might be an efficient way to foster opportunistic collaboration at scale.