Is Holism A Problem For Inductive Inference? A Computational Analysis

Maxwell A. Bertolero, Thomas L. Griffiths

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

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014
PublisherThe Cognitive Science Society
Pages188-193
Number of pages6
ISBN (Electronic)9780991196708
StatePublished - 2014
Externally publishedYes
Event36th Annual Meeting of the Cognitive Science Society, CogSci 2014 - Quebec City, Canada
Duration: Jul 23 2014Jul 26 2014

Publication series

NameProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014

Conference

Conference36th Annual Meeting of the Cognitive Science Society, CogSci 2014
Country/TerritoryCanada
CityQuebec City
Period7/23/147/26/14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • Bayesian networks
  • ConceptNet
  • Fodor
  • complex networks
  • graph theory
  • holism
  • inductive inference

Fingerprint

Dive into the research topics of 'Is Holism A Problem For Inductive Inference? A Computational Analysis'. Together they form a unique fingerprint.

Cite this