You might be interested in some of the stuff at listed at
ftp://parcftp.xerox.com/pub/dynamics/multiagent.html
While not specifically 'memetics', there are some concrete models.
Here are abstracts from some of their stuff. Most of these papers are
available only as Postscript files.
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Computational Societies and Economies
Large collections of computational agents have many analogies to
market economies and biological
ecosystems in that the individuals must make decisions, based on local
information that is often
incomplete, out of date and uncertain.
Distributed systems, social and computational, are characterized
by individual programs or
agents that must make decisions based on limited, uncertain and
delayed information:
B. A. Huberman and T. Hogg, The Emergence of Computational
Ecologies, 1993
The nature of their instabilities and volatility is
described in
T. Hogg, B. A. Huberman, and M. Youssefmir, The Instability
of Markets, 1995
M. Youssefmir and B. A. Huberman, Clustered Volatility in
Multiagent Dynamics,
1995
Their response to environmental changes is described in
N. Glance, T. Hogg and B. A. Huberman, Computational
Ecosystems in a Changing
Environment, 1991
The chaotic behavior that can appear in computational ecologies
can often be controlled
with a simple local reward mechanism:
T. Hogg and B. A. Huberman, Controlling Chaos in Distributed
Systems, 1991
Cooperative problem solving is a powerful method for approaching
difficult problems. This
paper describes two computational search examples for which
cooperative parallel search is
effective for hard problem instances (cryptarithmetic and graph
coloring).
T. Hogg and B. A. Huberman, Better Than the Best: The Power of
Cooperation, 1993
For discussions of the individual examples see:
cryptarithmetic: S. Clearwater, B. Huberman and T. Hogg,
Cooperative Problem
Solving, 1992
graph coloring: T. Hogg and C. P. Williams, Solving the
Really Hard Problems with
Cooperative Search, 1993
Market-like mechanisms can form the basis for flexible
distributed software systems and for
allocation of computational resources:
allocating time on idle computers
C. A. Waldspurger et al., Spawn: A Distributed Computational
Economy, 1992
managing energy use in office buildings
B. A. Huberman and S. H. Clearwater, A Multi-Agent System
for Controlling
Building Environments, 1995
A novel methodology, based on notions of risk in economics, for
creating computational
portfolios that can be effective in the solution of hard
problems:
B. A. Huberman, R. M. Lukose and T. Hogg, An Economics Approach
to Hard
Computational Problems, 1996
Social dilemmas relating to the provision of public goods can
arise in distributed
computational systems even when all agents are designed with a
single overall goal.
N. S. Glance and T. Hogg, Computational Social Dilemmas, 1995
A shorter overview with a genetic algorithm example is in:
N. S. Glance and T. Hogg, Dilemmas in Computational Societies,
ICMAS 1995
These situations can arise quite easily in computational
ecosystems:
T. Hogg, Social Dilemmas in Computational Ecosystems, IJCAI 1995
An application of computational societies is to the control of
smart matter consisting of
distributed sensors, actuators and computers.
Controls to keep a material in a physically unstable
configuration:
T. Hogg and B. A. Huberman, Controlling Smart Matter, 1996
An application of hypothetical quantum computers for
control:
T. Hogg and J. G. Chase, Quantum Smart Matter, 1996
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You also wrote (on another thread):
<Many religions have been propagated through force of the sword. In
many cases this proved to be an effective technique. If religion is
such a profoundly intertwined aspect of a person's being, how were so
many countries and regions "converted" permanently and effectively
simply by force, even if occupation was relatively transient? This
seems to be inconsistent with the supposed nature of religion.>
The simple answer is that religion has rarely been separated from
politics, economics and culture.
Mark Crosby