@inproceedings{7b7fb21c8ef44e20b9632070abc30836,
title = "Is Holism A Problem For Inductive Inference? A Computational Analysis",
abstract = "We investigate whether holism presents a problem for inductive inference by examining the relationship between the size of a Bayesian network that represents human conceptual knowledge and the computational complexity of probabilistic inference in that network. We find that, despite prior claims, holism may not be a problem for inductive inference, as computational cost does not increase exponentially as the network grows. While the network we analyze is holistic, it has a modular organization and grows in a way that potentially makes efficient inductive inference possible.",
keywords = "Bayesian networks, ConceptNet, Fodor, complex networks, graph theory, holism, inductive inference",
author = "Bertolero, {Maxwell A.} and Griffiths, {Thomas L.}",
note = "Publisher Copyright: {\textcopyright} 2014 Proceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014. All rights reserved.; 36th Annual Meeting of the Cognitive Science Society, CogSci 2014 ; Conference date: 23-07-2014 Through 26-07-2014",
year = "2014",
language = "English (US)",
series = "Proceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014",
publisher = "The Cognitive Science Society",
pages = "188--193",
booktitle = "Proceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014",
}