  Computational models of decisionmaking must contend with the variance of
context and any number of possible decisions that a defined strategic actor can
make at a given time. Relying on cognitive science theory, the authors have
created an algorithm that captures the orientation of the actor towards an
object and arrays the possible decisions available to that actor based on their
given intersubjective orientation. This algorithm, like a traditional K-means
clustering algorithm, relies on a core-periphery structure that gives the
likelihood of moves as those closest to the cluster's centroid. The result is
an algorithm that enables unsupervised classification of an array of decision
points belonging to an actor's present state and deeply rooted in cognitive
science theory.
