CogSciK: Clustering for Cognitive Science Motivated Decision Making
This addresses decision-making modeling for cognitive science applications, but appears incremental as it adapts K-means clustering with cognitive theory.
The authors tackled the problem of modeling decision-making by developing an algorithm that uses cognitive science theory to classify an actor's possible decisions based on their orientation towards an object, resulting in an unsupervised classification method for decision points.
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.