Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658151
Title: Essays in social learning
Author: Zhang, Min
ISNI:       0000 0004 5352 2638
Awarding Body: London School of Economics and Political Science (University of London)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2015
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Abstract:
This thesis contains two theoretical essays built upon the canonical models of social learning, and one that applies social learning theory to durable goods markets. The first chapter, "Non-Monotone Observational Learning", revisits the canonical social-learning model that rationalizes herding in the long run, to investigate the possibility of non-imitative behavior in the short run generated by non-monotone learning: ceteris paribus, when some predecessor(s) switch to actions revealing greater confidence in one state of the world, agents become less confident in that state. I characterize conditions on the underlying information structures that lead to non-monotone learning. In particular, in a general setting with continuous private signals, I provide a necessary condition for non-monotone learning with an argument for its plausibility, as well as two non-restrictive suffient conditions that do not rely on parametrization. The second chapter, "Does Public Information Disclosure Help Social Learning?", studies the effect of releasing exogenous public information in the canonical social-learning model that predicts incomplete learning. To improve social learning, I show that it is weakly better to postpone the disclosure of a public signal irrespective of its precision. However, such weak monotonicity no longer holds if the objective is to maximize the discounted sum of people's expected payoffs or if the model goes beyond the canonical binary setting. On the other hand, it is suboptimal to ever release a public signal less precise than people's private signals even if sophisticated releasing strategies are allowed. The last chapter, "Learning and Price Dynamics in Durable Goods Markets", is joint work with Francesco Palazzo. We study how markets for durable goods with unobservable and time-varying aggregate market conditions determine price dynamics with market participants constantly learning from public observations. We set up a dynamic auction model with two key features: first, agents enjoy heterogeneous private use values and later resell the asset; second, prices do not incorporate all available information dispersed in the economy. Informational frictions slow down learning and affect price movements asymmetrically across high and low aggregate demand states. Learning and the resale motive are the predominant force for durable goods with short resale horizons, slow time varying aggregate demand, and similar use values across agents.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.658151  DOI: Not available
Keywords: HB Economic Theory
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