  Recent work [Hazy 2012] has demonstrated computationally that collectives
that are organized into networks which govern the flow of resources can learn
to recognize newly emerging opportunities distributed in the environment. This
paper argues that the system does this through a process analogous to neural
network learning with relative status playing the role of synaptic weights.
Hazy showed computationally that learning of this type can occur even when
resource allocation decision makers have no direct visibility into the
environment, have no direct understanding of the opportunity, and are not
involved in their exploitation except to the extent that they evaluate the
success or failure of funded projects. Effectively, the system of interactions
learns which individuals have the best access to information and other
resources within the ecosystem. Hazy [2012] calls this previously unidentified
emergence phenomenon: Influence Process Structural Learning (IPSL). In the
prior model of IPSL, a three-tiered organizational structure was predetermined
in the model design [Hazy 2012]. These initial conditions delimit the extent to
which the emergence of collective intelligence can be posited because the model
itself assumes a defined structure. This work contributes to the field by
extending the IPSL argument for collective intelligence to a holistic emergence
argument. It begins by briefly reviewing previously published work. It
continues the conversation by adding two additional steps: Firstly, it shows
how a three-tier organizing structure might emerge through known complexity
mechanisms. In this case the mechanism identified is preferential attachment
[Barabasi 2002]. Secondly, the paper shows how collective intelligence can
emerge within a system of agents when the influence structure among these
agents is treated as a the genetic algorithm.
