Where Forward-Looking and Backward-Looking Models Meet

Abstract

The present paper begins by deriving an instantaneous formulation for the backward-looking (reinforcement based learning) satisfaction balance model of Gray and Tallman (1984). This model is then used to generate interactional data from four simulated agents in a network interaction experiment. Because this initial model does not generate stable interaction structures in the network experiment, it is altered step by step in the direction of a forward-looking (agent with goals) model that has been shown to generate such stable interaction structures. The purpose of the modifications are to learn what aspects of the forward-looking model are needed to evolve a stable interaction structure, and to learn how these aspects may be incorporated into a model that remains essentially reinforcement based.