models by introducing the extended belief game model. The extended belief game model operates over a subjective belief profile, which consists of belief bases with subjectively annotated formulae. The subjective information attached to each formula en-ables the proposed model to prioritize the formulae

JSTOR, please contact support@jstor.org. . The Econometric Society is collaborating with JSTOR to digitize, preserve and extend access to Econometrica. Recent work in game theory has shown that, in principle, it may be possible for firms in an industry to form a self-policing cartel to maximize

belief propagation; a way of applying Rao-Blackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main

that reflects the user’s intrinsic belief in WWW acceptance. Using it as an intrinsic motivation factor, we extend and empirically validate the Technology Acceptance Model (TAM) for the WWW

introduced a greedy layer-wise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with many layers of hidden causal variables. In the context of the above optimization problem, we study this al-gorithm empirically and explore variants to better understand its success

We describe a general model, `experience-weighted attraction' (EWA) learning, which includes reinforcement learning and a class of weighted fictitious play belief models as special cases. In EWA, strategies have attractions which reflect prior predispositions, are updated based on payoff

of economic models of coordination failure. For symmetric binary action global games, equilibrium strategies in the limit (as noise becomes negligible) are simple to characterize in terms of 'diffuse' beliefs over the actions of others. We describe a number of economic applications that fall

This paper describes and reviews the theory of planned behavior (TPB). The focus is on evidence supporting the further extension of the TPB in various ways. Empirical and theoretical evidence to support the addition of 6 variables to the TPB is reviewed: belief salience measures, past behaviodhabit

approximations to the Bayesian score. In this paper, I extend Structural EM to deal directly with Bayesian model selection. I prove the convergence of the resulting algorithm and show how to apply it for learning a large class of probabilistic models, including Bayesian networks and some variants thereof.

, by constructing a formal game theoretic model of the system and analyzing equilibria of user strategies under several novel payment mechanisms. We support and extend this workwith results from experiments with a multi-agent reinforcement learning model. 1