Artificial Intelligence and Law

, Volume 21, Issue 4, pp 399–423 | Cite as

Agile: a problem-based model of regulatory policy making

  • Alexander BoerEmail author
  • Tom van Engers


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.


Public administration Policy making Knowledge management Model-based diagnosis 


  1. Atkinson KM, Bench-Capon TJM, Cartwright D, Wyner AZ (2011) Semantic models for policy deliberation. In: Proceedings of the 13th international conference on artificial intelligence and law. ACM, New York, NY, USA, ICAIL ’11, pp 81–90. doi: 10.1145/2018358.2018369
  2. Beer S (1985) Diagnosing the system for organizations. The Managerial cybernetics of organization, Wiley.
  3. Boer A, van Engers T (2011a) An agent-based legal knowledge acquisition methodology for agile public administration. In: Proceedings of the 13th international conference on artificial intelligence and law. ACM, New York, NY, USA, ICAIL ’11, pp 171–180. doi: 10.1145/2018358.2018383
  4. Boer A, van Engers T (2011b) Diagnosis of multi-agent systems and its application to public administration. In: Abramowicz W, Maciaszek L, Wȩcel K, Aalst W, Mylopoulos J, Rosemann M, Shaw MJ, Szyperski C (eds) Business information systems workshops. Lecture Notes in Business Information Processing, vol 97. Springer, Heidelberg, pp 258–269. doi: 10.1007/978-3-642-25370-6-26
  5. Boer A, van Engers TM (2011c) Implementing compliance controls in public administration. In: Atkinson K (ed) Legal knowledge and information systems—JURIX 2011: the twenty-fourth annual conference, University of Vienna, Austria, 14th–16th December 2011, IOS Press, Frontiers in Artificial Intelligence and Applications, vol 235, pp 33–42Google Scholar
  6. Borgo S, Guarino N, Masolo C (1996) Stratified ontologies: the case of physical objects. In: Proceedings of the ECAI-96 workshop on ontological engineeringGoogle Scholar
  7. Breuker J (1994) Components of problem solving and types of problems. In: Steels L, Schreiber G, Van de Velde W (eds) A future for knowledge acquisition. Lecture Notes in Computer Science, vol 867. Springer, Heidelberg, pp 118–136CrossRefGoogle Scholar
  8. Breuker J, de Velde WV (1994) Introduction and overview. In: Breuker J, de Velde WV (eds) CommonKADS library for expertise modelling. IOS-Press/Ohmsha, Amsterdam/Tokyo, pp 1–8Google Scholar
  9. Bussmann W (2010) Evaluation of legislation: skating on thin ice. Evaluation 16(3):279–293. doi: 10.1177/1356389010370252, URL CrossRefGoogle Scholar
  10. Chandrasekaran B, Johnson TR (1993) Generic tasks and task structures: History, critique and new directions. In: David JM, Krivine JP, Simmons R (eds) Second generation expert systems. Springer, BerlinGoogle Scholar
  11. Clancey WJ (1985) Heuristic classification. Artif Intell 27:289–350CrossRefGoogle Scholar
  12. Cohen AM, Stavri PZ, Hersh WR (2004) A categorization and analysis of the criticisms of evidence-based medicine. Int J Med Inform 73(1):35–43. doi: 10.1016/j.ijmedinf.2003.11.002, URL CrossRefGoogle Scholar
  13. Fischer F, Miller G, Sidney MS (2007) Handbook of public policy analysis: theory, politics, and methods/edited by Frank Fischer, Gerald J. Miller, Mara S. Sidney. CRC Press, Boca Raton, FL.
  14. Gong Y, Janssen M (2012) From policy implementation to business process management: principles for creating flexibility and agility. Gov Inf Q 29, Supplement 1(0):S61–S71. doi: 10.1016/j.giq.2011.08.004, URL
  15. Hobbs JR (1995) Sketch of an ontology underlying the way we talk about the world. Int J Hum-Comput Stud 43(5–6):819–830, doi: 10.1006/ijhc.1995.1076 CrossRefGoogle Scholar
  16. Schreiber G (2000) Knowledge engineering and management: the CommonKADS methodology. The MIT Press, CambridgeGoogle Scholar
  17. Sileno G, Boer A, van Engers TM (2012) Analysis of legal narratives: a conceptual framework. In: Schäfer B (ed) JURIX, IOS Press, Frontiers in Artificial Intelligence and Applications, vol 250, pp 143–146Google Scholar
  18. Steels L (1990) Components of expertise. AI Mag 11(2):30–49Google Scholar
  19. Valente A (1995) Legal knowledge engineering: a modelling approach. PhD thesis, University of AmsterdamGoogle Scholar
  20. Vatiero M (2010) From WN hohfeld to JR commons, and beyond? A “law and economics” enquiry on jural relations. Am J Econ Sociol 69(2):840–866. doi: 10.1111/j.1536-7150.2010.00724.x Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.University of AmsterdamAmsterdamThe Netherlands

Personalised recommendations