Synereo Whitepaper With Cover

distributed architecture. Subsequently, we address both gateway and third party
services. Then, we discuss deployment options. Finally, we make a few closing
remarks about the nature of Synereo in the wider ecosystem of social networks.
Notation
Most of the notation adopted here is relatively standard for computer science.
The presentation is primarily functional in the sense that the universe of discourse
used is built from (structured) sets and functions. Additionally, we do
avail ourselves of higher-order functions, making use of lambda terms λx.term
to define anonymous functions.
Out of courtesy, whenever we are defining a function we first give its type.
We assume a rich ambient type theory that includes basics like tupling (with
fst and snd denoting the first and second projections of a pair, respectively) as
well as fancier more modern machinery like parametric polymorphism and the
usual suspects of collection types like List and Tree, and assume all the standard
functions for collections (map, filter, etc) are well understood computationally
and as mathematical objects. Occasionally we will use expressions like node(G)
as a type; this should not be taken to mean that the model presented here fundamentally
requires a dependent type theory. However, dependent type theory
is becoming more and more mainstream, and so we don’t feel that a more aggressive
use of dependent types would actually present a serious obstacle. To
the contrary, it would likely simplify the model in many places.
Essentially, all of these assumptions amount to making it quite straightforward
to render the model as a simulation in a functional language. Likewise, a
reader with some facility in functional programming should have no difficulty
parsing the presentation of our model.
2.2 A dynamics for social agency
In this section, we describe the network and its dynamics. We show how engagement,
quality of attention, and the flow of content are related to Reo, a
measure of reputation in the network, and AMPs, Synereo’s information flow
currency. We’ll present the model in stages of refinement. The first step regards
the network as a whole. The next step shows how the network as a whole is
mapped onto concurrently running distributed compute nodes relying solely on
local interaction.
2.2.1 Network Model
The description presented here will provide a global or “god’s eye” view of the
network to illustrate the dynamics of the whole network. In subsequent sections
we will show how to realize these global dynamics in terms of entirely local
interactions in a distributed network of interacting processes.
We’ll begin by modeling a Synereo network as a graph, G, with nodes,
written node(G), representing users of Synereo (plus their system footprint)
and edges, written edge(G), representing communication links through Synereo.
When it’s convenient we’ll write in G (N) to denote the set of edges in G with N
as the target, and similarly out G (N) to denote the set of edges in G with N as
the source. More formally,
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