Convergence analysis of iterated belief revision in complex fusion environments

Thanuka L. Wickramarathne, Kamal Premaratne, Manohar N. Murthi, Nitesh V. Chawla

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


We study convergence of iterated belief revision in complex fusion environments, which may consist of a network of soft (i.e., human or human-based) and hard (i.e., conventional physics-based) sensors and where agent communications may be asynchronous and the link structure may be dynamic. In particular, we study the problem in which network agents exchange and revise belief functions (which generalize probability mass functions) and are more geared towards handling the uncertainty pervasive in soft/hard fusion environments. We focus on belief revision in which agents utilize a generalized fusion rule that is capable of generating a rational consensus. It includes the widely used weighted average consensus as a special case. By establishing this fusion scheme as a pool of paracontracting operators, we derive general convergence criteria that are relevant for a wide range of applications. Furthermore, we analyze the conditions for consensus for various social networks by simulating several network topologies and communication patterns that are characteristic of such networks.

Original languageEnglish (US)
Article number6803072
Pages (from-to)598-612
Number of pages15
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number4
StatePublished - Aug 2014


  • Belief revision
  • conditional update equation
  • consensus
  • convergence of opinions
  • soft/hard fusion

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing


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