- Agile: a problem-based model of regulatory policy making
- Artificial Intelligence and Law
- Volume | Issue number
- 21 | 4
- Pages (from-to)
- Document type
- Faculty of Law (FdR)
- Leibniz Center for Law (FdR)
We understand regulatory policy problems against the backdrop of existing implementations of a regulatory framework. There are argument schemes for proposing a policy and for criticising a proposal, rooted in a shared understanding that there is an existing regulatory framework which is implemented in social structures in society, yet has problems. The problems with the existing implementations may be attributed either to those implementations or to the constraints imposed by the regulatory framework. In this paper we propose that calls for change of regulatory policy, and case-based and statistical evidence produced in support of policy proposals, are based in model-based problem solving activities. This perspective suggests schemes for a good argument pro or con a policy proposal, while avoiding the problem of backing up claims and evidence on the policy level with a conjectural deep model of the policy domain.
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