David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Episteme 6 (2):145-163 (2009)
In recent years, various computational models have been developed for studying the dynamics of belief formation in a population of epistemically interacting agents that try to determine the numerical value of a given parameter. Whereas in those models, agents’ belief states consist of single numerical beliefs, the present paper describes a model that equips agents with richer belief states containing many beliefs that, moreover, are logically interconnected. Correspondingly, the truth the agents are after is a theory (a set of sentences of a given language) rather than a numerical value. The agents epistemically interact with each other and also receive evidence in varying degrees of informativeness about the truth. We use computer simulations to study how fast and accurately such populations as wholes are able to approach the truth under differing combinations of settings of the key parameters of the model, such as the degree of informativeness of the evidence and the weight the agents give to the evidence
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References found in this work BETA
Igor Douven (2010). Simulating Peer Disagreements. Studies in History and Philosophy of Science Part A 41 (2):148-157.
Gérard Weisbuch, Guillaume Deffuant, Frédéric Amblard & Jean‐Pierre Nadal (2002). Meet, Discuss, and Segregate! Complexity 7 (3):55-63.
Citations of this work BETA
Igor Douven & Christoph Kelp (2011). Truth Approximation, Social Epistemology, and Opinion Dynamics. Erkenntnis 75 (2):271-283.
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