Precedent and Interpersonal Convergence in the Method of Reflective Equilibrium

We present a computational model of reflective equilibrium with precedent. Each agent considers a rule by which to accept or reject cases. Cases are represented as labeled binary strings: intuitive accept, intuitive reject, or no intuition. Rules are represented as a pair: a binary string and a tolerance threshold determining if a case is a close enough match to accept. Rule-updates are driven by intuitions about cases and precedents set by other agents. We compare four networks: empty, ring, 4-regular, and complete. Results suggest that increasing connectivity encourages, but doesn't guarantee, interpersonal convergence on a single reflective equilibrium.