Essays in Search and Learning.

Behavioral Economics;Microeconomics;Economics;Business;Economics

Raymond, Collin BuckleySilverman, Daniel Susman ;

University of Michigan

关键词: Behavioral Economics;    Microeconomics;    Economics;    Business;    Economics;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/93871/cbraymon_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This dissertation consists of three chapters on individual behavior in economic environments that feature search and learning. Chapter 1, ;;Search and Temptation,’ explores optimal search behavior when an agent experiences temptation regarding items previously observed during search. Although the agent’s optimal policy is still a reservation policy, it has several novel features absent from other models of search. First, temptation has a threshold effect: the reservation value is an increasing function of the amount of temptation experienced. Second, temptation has a compromise effect: observing more tempting items means that the agent is no longer willing to choose items that are relatively low in temptation value, but is now willing to choose items relatively low in untempted utility. In Chapter 2, ;;Revealed Search Theory,’ a revealed preference approach is used to analyze models of search. Classical sequential search models characterize the value of the searcher’s optimal policy as the solution to a Bellman equation. I define and provide behavioral foundations for a set of search models, which I refer to as Generalized Search Representations, that nests the classical model of sequential search, but also accommodates non-standard preferences. Chapter 3, ;;A Model of Non-Belief in the Law of Large Numbers,’ is joint with Daniel Benjamin and Matthew Rabin. Psychological research suggests that people believe that even in very large samples, proportions might depart significantly from the population mean. We model this ;;non-belief in the Law of Large Numbers” by assuming that a person believes that proportions in any given sample of binary signals might be determined by a rate different than the true rate. In inference, a non-believer attends too little to sample size, and remains uncertain even after observing an arbitrarily large sample. We explore the both the direct implications of non-belief, as well as how non-belief is often a necessary enabler of other biases, such as the over-influence of vivid signals, that would otherwise be overwhelmed by the logic of the Law of Large Numbers.