Conflict in distributed hypothesis testing with quantized prior probabilities

Joong Bum Rhim, Lav R. Varshney, Vivek K. Goyal

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


The effect of quantization of prior probabilities in a collection of distributed Bayesian binary hypothesis testing problems over which the priors themselves vary is studied, with focus on conflicting agents. Conflict arises from differences in Bayes costs, even when all agents desire correct decisions and agree on the meaning of correct. In a setting with fusion of local binary decisions by majority rule, Nash equilibrium local decision strategies are found. Assuming that agents follow Nash equilibrium decision strategies, designing quantizers for prior probabilities becomes a strategic form game, we discuss its Nash equilibria. We also propose two different constrained quantizer design games, find Nash equilibrium quantizer designs, and compare performance. The system has deadweight loss: equilibrium decisions are not Pareto optimal.

Original languageEnglish (US)
Title of host publicationProceedings - DCC 2011
Subtitle of host publication2011 Data Compression Conference
Number of pages10
StatePublished - 2011
Externally publishedYes
Event2011 Data Compression Conference, DCC 2011 - Snowbird, UT, United States
Duration: Mar 29 2011Mar 31 2011

Publication series

NameData Compression Conference Proceedings
ISSN (Print)1068-0314


Other2011 Data Compression Conference, DCC 2011
Country/TerritoryUnited States
CitySnowbird, UT

ASJC Scopus subject areas

  • Computer Networks and Communications


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