reinforcement learning wherein individuals become unduly optimistic after receiving good returns. In sharp contrast, there is little …

thesis makes two distinct contributions in the field of learning game theory and one in the field of evolutionary game theory … tractability, and also to illustrate, complement and extend various analytical derivations. The two extensions to learning game … one of two alternative decision-making processes –case-based reasoning or reinforcement learning– that have received …

We describe a candid model for learning, why and how learning transpires. We investigate the original as well as the … leading conditions of the learning process. We provide an insight into the realm of beliefs and their formation, their … the influence that learning in general has on human behavior. …

to be employing a simple heuristic alternative to more complex learning models. Simulations of these various belief …

This paper studies the analytical properties of the reinforcement learning model proposed in Erev and Roth (1998), also … termed cumulative reinforcement learning in Laslier et al (2001). This stochastic model of learning in games accounts for two … close to one, all the realizations of the reinforcement learning process will, from some time on, lie within an " band of …

In this paper, a reinforcement learning version of the connections game first analysed by Jackson and Wolinsky is …, the main nding is that the pattern of reinforcement learning process is similar, but does not fully converge to the … benchmark results. Before these optimal results can be discovered in a learning process, agents often get locked in a state of …

This paper deals with cognitive theories behind agent-based modeling of learning and information processing … function under reinforcement learning model. In doing so, I have considered the spatio-temporal environment under bounded … linking contexts with optimal decisions that agents undertake under hypercompetitive market pressure through learning which …