A Recurrent Neural Network for Game Theoretic Decision Making
We formalize the biased activation theory of anchoring using a bidirectional associative memory network. Anchors determine the starting state of this network. As the network settles, we show that the nodes representing numerical responses activate and deactivate consecutively, generating sequential adjustment. By demonstrating that anchoring as adjustment emerges naturally from the dynamics of the biased activation process, we are able to unify the two main theories of the anchoring effect, and subsequently provide a parsimonious explanation for a large range of findings regarding anchoring, and its determinants. Although we focus largely on phenomena related to anchoring, the results of this paper apply equivalently to all judgments under the influence of bidirectional processing, including those involving constraint satisfaction.