Work

Fact-Free Learning

Year: 2005

Type: article

Abstract: People may be surprised to notice certain regularities that hold in existing knowledge they have had for some time. That is, they may learn without getting new factual information. We argue that this can be partly explained by computational complexity. We show that, given a knowledge base, finding a small set of variables that obtain a certain value of R2is computationally hard, in the sense that this term is used in computer science. We discuss some of the implications of this result and of fact-free learning in general.

Source: American Economic Review

DOI: 10.1257/000282805775014308

Authors: Enriqueta Aragones, Itzhak Gilboa, Andrew Postlewaite, David Schmeidler

Institutions: Institut d'Anàlisi Econòmica, Tel Aviv University, Yale University, University of Pennsylvania, The Ohio State University

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3122734808

FWCI: 7.685

Cites: 18

Cited by: 119

Related to: 10