Fact-free learning

• 成果类型: Article
• 署名作者: Aragones, E; Gilboa, I; Postlewaite, A; Schmeidler, D
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/000282805775014308
• 发表日期: 2005
• 页码: 1355-1368
• 关键词: representation; MODEL

摘要

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 R-2 is 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.