Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
As economics modeling moves from super rational decision makers to considering boundedly rational agents, some economic problems deserve a second look. This paper studies the effects of learning on the efficiency of search. Once learning is taken into account, the structure of information flow becomes important. In particular, I highlight the truncated information structure in the search problem. Agents stop searching once a sufficiently attractive price is found. Therefore, they observe the performance of shorter searches, but do not directly observe the performance of longer searches. I design and conduct an experiment to test the hypothesis that this asymmetric flow of information leads agents to search too little. I find strong evidence in its favor. This suggests that in the presence of learning, the provision of a more symmetric information structure will make search more efficient. Copyright Springer Science + Business Media, Inc. 2005