Adaptive learning refers to a process where agents learn about the value of their actions from their direct experience with the actions. There is evidence that variants of adaptive learning well describe subjects' behavior in experiments. This project investigates decisions made by agents learning adaptively from their own experience but also from indirect information about actions they did not take. In particular, we investigate the effect that distortions of such indirect information, which we interpret as representing attitudes towards the source of information, have on the quality of the decisions. We find that the pattern of behavior depends on whether the agent inflates or deflates indirect payoff information relative to the objective payoffs. If the agent inflates indirect payoff information, actions that have not been played for a while tend to look better than they truly are, leading the agent to revisit them every so often. As a result, behavior ``cycles'' through more than one action. On the other hand, if the agent tends to deflate indirect information, he will settle to playing a single action. Since the unplayed alternatives are perceived to be worse than they objectively are, the limit action, which the agent perceives to be the best, can be objectively suboptimal. The former pattern resembles variety seeking while the latter resembles ``lock-in'' behavior. Marketing research suggests that both patterns are observed in consumer choice. The second key feature of the learning rule is the degree to which past observations are discounted. In particular, whether the weight put on last period's payoff becomes arbitrarily small or not. We provide preliminary results on the role of discounting, suggesting the effect depends crucially on whether indirect payoff information is utilized or not. While discounting leads to suboptimal behavior in a stationary environment, we look for justifications for it in decision situations which involve limited
