MARC 主機 00000nam  2200349   4500 
001    AAI3402228 
005    20110110132610.5 
008    110110s2010    ||||||||||||||||| ||eng d 
020    9781109742282 
035    (UMI)AAI3402228 
040    UMI|cUMI 
100 1  Muller-Frank, Manuel 
245 10 Essays on learning in networks 
300    92 p 
500    Source: Dissertation Abstracts International, Volume: 71-
       05, Section: A, page: 1746 
500    Advisers: Alessandro Pavan; Marciano Siniscalchi 
502    Thesis (Ph.D.)--Northwestern University, 2010 
520    This dissertation makes several contributions to the 
       theory on learning in networks. Chapter 1 provides a 
       formal characterization of the process of rational 
       learning in social networks. A finite set of agents select
       an option out of a choice set under uncertainty in 
       infinitely many periods observing the history of choices 
       of their neighbors. Choices are made based on a common 
       behavioral rule. I find that if learning ends in finite 
       time and the choice correspondence is union consistent, 
       then every action selected by any agent once learning ends
       is optimal for all his neighbors. I further provide 
       sufficient conditions such that every action chosen 
       infinitely often by an agent is optimal for all his 
       neighbors in the limit. If only common knowledge of 
       rationality rather than common knowledge of strategies is 
       assumed, the validity of the aforementioned results 
       depends on the network structure. If the network is 
       complete, the result of local indifference across 
       neighbors once learning ends still holds, while it can 
       fail in incomplete networks 
520    Chapter 2 considers aggregation of private information in 
       networks where Bayesian agents announce their posterior 
       belief of an uncertain event to their neighbors in each of
       countable communication rounds. I show by example that 
       complete networks can be inferior to incomplete networks 
       in terms of the quality of information aggregation under 
       certain circumstances. I then characterize sufficient 
       conditions on the informational structure for optimality 
       of complete networks 
520    Chapter 3 establishes a fixed point convergence result in 
       Euclidean spaces. The result is used to extend existing 
       results in the literature on Non-Bayesian learning in 
       networks. In a Non-Bayesian learning framework, agents 
       announce their belief of an uncertain event to their 
       neighbors in each of countable communication rounds using 
       updating rules: the posterior they announce in a given 
       round is a function of the last period announcements of 
       their neighbors and themselves. I show that if the 
       updating rules are continuous and contracting, then the 
       beliefs of all agents in a connected social network 
       converge 
590    School code: 0163 
650  4 Economics, Theory 
690    0511 
710 2  Northwestern University.|bEconomics 
773 0  |tDissertation Abstracts International|g71-05A 
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/
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