A Graph-based Similarity Function for CBDT: Acquiring and Using New Information

Federico E. Contiggiani, Fernando Delbianco () and Fernando Tohm\'e
Additional contact information
Federico E. Contiggiani: Universidad Nacional de R\'io Negro
Fernando Tohm\'e: Instituto de Matem\'atica de Bah\'ia Blanca, CONICET-UNS

Papers from arXiv.org

Abstract: One of the consequences of persistent technological change is that it force individuals to make decisions under extreme uncertainty. This means that traditional decision-making frameworks cannot be applied. To address this issue we introduce a variant of Case-Based Decision Theory, in which the solution to a problem obtains in terms of the distance to previous problems. We formalize this by defining a space based on an orthogonal basis of features of problems. We show how this framework evolves upon the acquisition of new information, namely features or values of them arising in new problems. We discuss how this can be useful to evaluate decisions based on not yet existing data.

Date: 2021-04
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2104.14268 Latest version (application/pdf)

Related works:
Working Paper: A Graph-based Similarity Function for CBDT: Acquiring and Using New Information (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2104.14268

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().