Connectionists: Paper: Ideas by Statistical Mechanics (ISM)

[Lester Ingber]

The following working paper is available from http://www.ingber.com or
http://alumnus.caltech.edu/~ingber

%A L. Ingber
%T Ideas by Statistical Mechanics (ISM)
%R Report 2006:ISM
%I Lester Ingber Research
%D 2006
%O URL http://www.ingber.com/smni06_ism.pdf

Ideas by Statistical Mechanics (ISM) is a generic program to model
evolution and propagation of ideas/patterns throughout populations
subjected to endogenous and exogenous interactions.  The program is based
on the author's work in Statistical Mechanics of Neocortical Interactions
(SMNI), and uses the author's Adaptive Simulated Annealing (ASA) code
for optimizations of training sets, as well as for importance-sampling
to apply the author's copula financial risk-management codes, Trading
in Risk Dimensions (TRD), for assessments of risk and uncertainty.
This product can be used for decision support for projects ranging
from diplomatic, information, military, and economic (DIME) factors of
propagation/evolution of ideas, to commercial sales, trading indicators
across sectors of financial markets, advertising and political campaigns,
etc.

It seems appropriate to base an approach for propagation of ideas on
the only system so far demonstrated to develop and nurture ideas, i.e.,
the neocortical brain.  A statistical mechanical model of neocortical
interactions, developed by the author and tested successfully in
describing short-term memory and EEG indicators, is the proposed model.
ISM develops subsets of macrocolumnar activity of multivariate stochastic
descriptions of defined populations, with macrocolumns defined by
their local parameters within specific regions and with parameterized
endogenous inter-regional and exogenous external connectivities.
Parameters with a given subset of macrocolumns will be fit using ASA to
patterns representing ideas.  Parameters of external and inter-regional
interactions will be determined that promote or inhibit the spread of
these ideas.  Tools of financial risk management, developed by the author
to process correlated multivariate systems with differing non-Gaussian
distributions using modern copula analysis, importance-sampled using
ASA, will enable bona fide correlations and uncertainties of success
and failure to be calculated.  Marginal distributions will be evolved
to determine their expected duration and stability using algorithms
developed by the author, i.e., PATHTREE and PATHINT codes