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

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/10878

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

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Title: Collective belief models for representing consensus and divergence in communities of Bayesian decision-makers
Author: Greene, Kshanti
Advisor(s): Luger, George
Committee Member(s): Kniss, Joe
Moses, Melanie
Ross, Tim
Stern, Carl
Department: University of New Mexico. Dept. of Computer Science
Subject: Decision-making
Bayesian reasoning
Social choice theory
Probabilistic reasoning
Bayesian belief aggregation
game theory
diversity
artificial intelligence
social decision-making
LC Subject(s): Social choice--Mathematical models.
Bayesian statistical decision theory.
Game theory.
Degree Level: Doctoral
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.
Graduation Date: May 2010
URI: http://hdl.handle.net/1928/10878


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