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University of New Mexico

Collective belief models for representing consensus and divergence in communities of Bayesian decision-makers

Abstract

dc:description.abstract

Bayesian belief aggregation is the process of forming a consensus model from the probabilistic beliefs of multiple individuals. Preference aggregation attempts to find an optimal solution for a population considering each individual's beliefs, desires and objectives. Belief and preference aggregation approaches that form a single consensus average away any diversity in a population. In the process they may fail to uphold a set of mathematical properties for rational aggregation defined by social choice theorists. This dissertation introduces a new aggregation approach that maintains the diversity of a population and allows the competitive aspects of a situation to emerge, enabling game theoretic analysis in large populations of decision-makers. Each individual's beliefs and preferences are represented by a Bayesian network. Based on the result of inference on the networks, a population is separated into collectives whose members agree on the relatively likelihood or desirability of the possible outcomes of a situation. An aggregate for each collective can then be computed such that the aggregate upholds the rationality properties. Game theoretic analysis is then applied using 'super-agents' that represent each collective as the game players. In this manner, the set of Pareto optimal and Nash equilibrium solutions can be found, even in situations that cause single consensus models to return non-Pareto or otherwise 'irrational' solutions.

Degree

thesis:*
Name thesis:degree_name
Computer Science
Level thesis:degree_level
Dissertation
Discipline thesis:degree_discipline
Department of Computer Science
Year
2010

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Greene, Kshanti
Contributors dc:contributor
  • Luger, George
  • Kniss, Joe
  • Moses, Melanie
  • Ross, Tim
  • Stern, Carl

Subjects

dc:subject × 9

Rights

Language dc:language
English

Identifiers

dc:identifier.*
OAI identifier oai:identifier
oai:digitalrepository.unm.edu:cs_etds-1007

Chain of custody

source
Harvested from
University of New Mexico
Base URL
digitalrepository.unm.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Greene, Kshanti. Collective belief models for representing consensus and divergence in communities of Bayesian decision-makers. Dissertation thesis, 2010. http://hdl.handle.net/1928/10878