Nowadays,
there is a convergent interest from different
disciplines (economics, certainly, but
also sociology as well as physics or biology)
to understand the unfolding of interaction
processes as the result of a complex (and
possibly evolving) network of connections
among the different "units"
involved. The essential idea that underlies
this approach is the belief that the topology
(or architecture) of the interaction is
an essential part of many processes and
it cannot be ignored without missing a
crucial ingredient of the phenomena at
hand.
Somewhat more specifically,
this is the point stressed by sociologists
and (a growing number of) economists when
they speak of the key role played by social
networks in "embedding" many
socio-economic phenomena. Such a network
embedding is important, for example,
to understand how information flows and
employement is distributed in modern labor
markets. They have also been found to
play a key role in reconciling the twin
and conflicting forces of cooperation
and competition (say in R&D or marketing
activities) arising naturally in industrial
setups. The range of socio-economic problems
whose theoretical and empirical analysis
is well served by a detailed understanding
of the social networks that underlie them
is very wide. Besides the former two,
one may list the following phenomena:
- the diffusion of technologies
and fads;
- patterns of scientific collaboration;
- the operating structure -- formal
and informal -- of firms and other organizations;
- processes of inter-agent bargaining
in large populations;
- patterns of inter-industrial
trade in both domestic and globalized
(international) markets;
- the persistence of institutions
in either modern or historical times.
This rich collection of examples underscores
the importance of social networks in understanding
a wide variety of socio-economic phenomena.
But, of course, it also begs the question
of how those social networks (in particular,
their architecture and general topological
features) come about in the first place.
It raises the question, that is, of how
social networks are endogenously shaped
out of the purposeful decisions of the
agents that form part of them.
Understanding network
formation and its interplay with other socio-economic
decisions is the central concern of the
field of social network analysis. To address
it, much of the research has been pursued
along two largely independent lines.
One of them is game-theoretic, in the
classic sense of the term, and focuses
on agents' payoffs and incentives. It
involves positing a network-formation
game, where the possible options (strategies)
available to the agents as well as their
payoffs are specified in detail. Then,
some "solution concept" is applied
to single out the predicted, or otherwise
prescribed, outcome of the network-formation
game. Implicitly, this approach presumes
a high degree of rationality and knowledge
on the part of agents or, alternatively,
a relatively simple and stable environment.
By contrast, the second approach is of
a phenomenological nature and stresses
the statistical regularities to be expected
in large and complex environments when
the networking process is governed by
simple ("algorithmic") rules.
In this case, the richness of detail is
not associated to the decision framework
of agents but pertains instead to the
systemic regularities resulting from the
interaction of a large number of independent
agents. Many interesting insights are
obtained in this way, although they are
not fully satisfactory since they lack
a well-formulated "micro foundation"
in agent behavior.
The above considerations
suggest that large benefits could accrue
from an integration of the former two methodologies,
i.e. from an approach that not only reflects
agents' incentives but also accounts for
the complex setup they face. Much of my
recent research efforts are devoted to advancing
in this integration, with a special focus
on issues of search and congestion in large
complex environments that are subject to
significant volatility. In these contexts,
an important finding is that the so-called
Red Queen Principle ("... it takes
all the running you can do, to keep in the
same place.") applies starkly. That
is, only if the social network evolves to
a state where wide and effective search
is possible, a dense and fruitful level
of interaction can be maintained. |
