Network evolution with incomplete information and learning

Jie Xu, Simpson Zhang, Mihaela Van Der Schaar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We analyze networks that feature reputational learning: how links are initially formed by agents under incomplete information, how agents learn about their neighbors through these links, and how links may ultimately become broken. We show that the type of information agents have access to, and the speed at which agents learn about each other, can have tremendous repercussions for the network evolution and the overall network social welfare. Specifically, faster learning can often be harmful for networks as a whole if agents are myopic, because agents fail to fully internalize the benefits of experimentation and break off links too quickly. As a result, preventing two agents from linking with each other can be socially beneficial, even if the two agents are initially believed to be of high quality. This is due to the fact that having fewer connections slows the rate of learning about these agents, which can be socially beneficial. Another method of solving the informational problem is to impose costs for breaking links, in order to incentivize agents to experiment more carefully.

Original languageEnglish (US)
Title of host publication2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1163-1168
Number of pages6
ISBN (Print)9781479980093
DOIs
StatePublished - Jan 30 2014
Externally publishedYes
Event2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014 - Monticello, United States
Duration: Sep 30 2014Oct 3 2014

Other

Other2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
CountryUnited States
CityMonticello
Period9/30/1410/3/14

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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