2022
Journal article  Open Access

Normative change: an AGM approach

Maranhão J. S. A., Casini G., Van Der Torre L., Pigozzi G.

Belief change  Belief revision  Normative reasoning 

Studying normative change has practical and theoretical interests. Changing legal rules poses interpretation problems to determine the content of legal rules. The question of interpretation is tightly linked to those of determining the validity and the ability to produce effects of legal rules. Different formal models of normative change seem better suited to capture these dimensions: the dimension of validity appears to be better captured by the AGM approach, whereas syntactic methods are better suited to model how rules' effects are blocked or enabled. Historically, the AGM approach of belief revision (on which we focus in this chapter) was the first formal model of normative change. We provide a survey on the AGM approach along with the main criticisms made to it. We then turn to a formal analysis of normative change that combines AGM theory and input/output logic, allowing for a clear distinction between norms and obligations. Our approach addresses some of the difficulties of normative change, like the combination of constitutive and regulative rules (and the normative conflicts that may arise from such a combination), the revision and contraction of normative systems, as well as the contraction of normative systems that combine sets of constitutive and regulative rules. We end our chapter by highlighting and discussing some challenges and open problems of normative change in the AGM approach.

Source: IfCoLog Journal of Logics and their Applications 9 (2022): 825–889.



Back to previous page
BibTeX entry
@article{oai:it.cnr:prodotti:488498,
	title = {Normative change: an AGM approach},
	author = {Maranhão J. S. A. and Casini G. and Van Der Torre L. and Pigozzi G.},
	journal = {IfCoLog Journal of Logics and their Applications},
	volume = {9},
	pages = {825–889},
	year = {2022}
}

TAILOR
Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization


OpenAIRE