說明 
92 p 
附註 
Source: Dissertation Abstracts International, Volume: 7105, Section: A, page: 1746 

Advisers: Alessandro Pavan; Marciano Siniscalchi 

Thesis (Ph.D.)Northwestern University, 2010 

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 

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 

Chapter 3 establishes a fixed point convergence result in Euclidean spaces. The result is used to extend existing results in the literature on NonBayesian learning in networks. In a NonBayesian 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 

School code: 0163 
Host Item 
Dissertation Abstracts International 7105A

主題 
Economics, Theory


0511

Alt Author 
Northwestern University. Economics

