  Fuzzy cognitive maps (FCMs) model feedback causal relations in interwoven
webs of causality and policy variables. FCMs are fuzzy signed directed graphs
that allow degrees of causal influence and event occurrence. Such causal models
can simulate a wide range of policy scenarios and decision processes. Their
directed loops or cycles directly model causal feedback. Their nonlinear
dynamics permit forward-chaining inference from input causes and policy options
to output effects. Users can add detailed dynamics and feedback links directly
to the causal model or infer them with statistical learning laws. Users can
fuse or combine FCMs from multiple experts by weighting and adding the
underlying fuzzy edge matrices and do so recursively if needed. The combined
FCM tends to better represent domain knowledge as the expert sample size
increases if the expert sample approximates a random sample. Many causal models
use more restrictive directed acyclic graphs (DAGs) and Bayesian probabilities.
DAGs do not model causal feedback because they do not contain closed loops.
Combining DAGs also tends to produce cycles and thus tends not to produce a new
DAG. Combining DAGs tends to produce a FCM. FCM causal influence is also
transitive whereas probabilistic causal influence is not transitive in general.
Overall: FCMs trade the numerical precision of probabilistic DAGs for pattern
prediction, faster and scalable computation, ease of combination, and richer
feedback representation. We show how FCMs can apply to problems of public
support for insurgency and terrorism and to US-China conflict relations in
Graham Allison's Thucydides-trap framework. The appendix gives the textual
justification of the Thucydides-trap FCM. It also extends our earlier theorem
[Osoba-Kosko2017] to a more general result that shows the transitive and total
causal influence that upstream concept nodes exert on downstream nodes.
