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Title: Essays in search theory
Author: Mauring, E.
ISNI:       0000 0004 8497 6798
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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This thesis consists of three papers on search theory. Chapter 2 studies stationary cutoff-strategy equilibria of a dynamic market model where buyers sample sellers sequentially from an unknown distribution. Buyers learn about the distribution from the sampled sellers and a private "trade signal". The trade signal reveals whether a randomly chosen seller traded yesterday. The signal's precision and the market distribution of options are determined in equilibrium. Observing a trade (as opposed to no trade) is good news about the distribution. Buyers who observe a trade use a higher cutoff than buyers who observe no trade, despite buyers' learning from sampled sellers that puts a countervailing pressure on the cutoffs. The trade signal may reduce market effciency, while an appropriate exogenous signal increases effciency. Chapter 3 extends the standard sequential search model by allowing the agent who inspects items sequentially (the "searcher") to differ from the agent who chooses from the set of inspected items (the "chooser"). I show for a general joint distribution of the agents' preferences that the searcher's optimal policy is a cutoff rule. The cutoff is weakly decreasing in time, i.e., exhibits the "discouragement effect". I characterise the cutoff and discuss some testable implications of the discouragement effect. Chapter 4 relaxes the standard sequential search model's assumption that the searching agent makes no choice mistakes. In my model, once the agent stops the search process, she chooses the best inspected item with probability 1-ε and uniformly among the remaining inspected items with probability ε. I show that her optimal policy is a stochastic cutoff rule and that she may both experience regret and search longer than an agent who makes no mistakes.
Supervisor: Spiegler, R. ; Cripps, M. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available