, Volume 189, Supplement 1, pp 51–65 | Cite as

Recognition-primed group decisions via judgement aggregation



We introduce a conceptual model for reaching group decisions. Our model extends a well-known, single-agent cognitive model, the recognition-primed decision (RPD) model. The RPD model includes a recognition phase and an evaluation phase. Group extensions of the RPD model, applicable to a group of RPD agents, have been considered in the literature, however the proposed models do not formalize how distributed and possibly inconsistent information can be combined in either phase. We show how such information can be utilized by aggregating it using a specific social choice method, namely judgment aggregation. Our model is applicable to hierarchical groups of agents containing at least one RPD agent.


Recognition-primed decisions Judgment aggregation Computational social choice Multiagent systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Chevaleyre, Y., Endriss, U., Lang, J., & Maudet, N. (2007). A short introduction to computational social choice. In Proceedings of the 33rd Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM ’07 (pp. 51–69). Berlin: Springer.Google Scholar
  2. Fan, X., Sun, S., McNeese, M., & Yen, J. (2005a). Extending the recognition-primed decision model to support human-agent collaboration. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS ’05 (pp. 945–952). New York: ACM.Google Scholar
  3. Fan, X., Sun, S., & Yen, J. (2005b). On shared situation awareness for supporting human decision-making teams. In Proceedings of 2005 AAAI Spring Symposium on AI Technologies for Homeland Security (pp. 17–24).Google Scholar
  4. Fan, X., & Yen, J. (2008). Team-rpd: A collaborative model of the recognition-primed decision process. In International Conference on Web Intelligence and Intelligent Agent Technology (Vol. 2, pp. 136–139).Google Scholar
  5. Ganesan, V., Slavkovik, M., Sousa, S., & van der Torre, L. (2012). Selecting judgment aggregation rules for Nao robots: An experimental approach. In 11th International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’12. IFAAMAS.Google Scholar
  6. Ji Y., Massanari R. M., Ager J., Yen J., Miller R. E., Ying H. (2007) A fuzzy logic-based computational recognition-primed decision model. Journal of Information Science 177: 4338–4353CrossRefGoogle Scholar
  7. Jones, R. E., Connors, E., Mossey, M., Hyatt, J., Hansen, N., & Endsley, M. (2010). Modeling situation awareness for army infantry platoon leaders using fuzzy cognitive mapping techniques. In Proceedings of the 19th Conference on Behavior Representation in Modeling and Simulation, Charleston, SC.Google Scholar
  8. Klein G. A. (1989) Recognition-primed decisions. In: Rouse W. (ed) Advances in man–machine systems research. JAI Press, Greenwich, CT, pp 47–92Google Scholar
  9. Klein G. (1999) Sources of power: How people make decisions. MIT Press, Cambridge, MAGoogle Scholar
  10. Klein G. (2008) Naturalistic decision making. Human Factors: The Journal of the Human Factors and Ergonomics Society 50(3): 456–460CrossRefGoogle Scholar
  11. Klein G. A., Calderwood R., Clinton-Cirocco A. (2010) Rapid decision making on the fire ground: The original study plus a postscript. Journal of Cognitive Engineering and Decision Making 4: 186–209CrossRefGoogle Scholar
  12. List C., Puppe C. (2009) Judgment aggregation: A survey. In: Anand P., Puppe C., Pattanaik P. (eds) Oxford handbook of rational and social choice. Oxford University Press, OxfordGoogle Scholar
  13. Norling, E. (2004). Folk psychology for human modelling: Extending the bdi paradigm. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems—Volume 1, AAMAS ’04 (pp. 202–209). Washington, DC: IEEE Computer Society.Google Scholar
  14. Sen, A. (1970). The impossibility of a paretian liberal. Journal of Political Economy, 78(1).Google Scholar
  15. Shattuck L., Lewis Miller N. (2006) Extending naturalistic decision making to complex organizations: A dynamic model of situated cognition. Organization Studies 27: 989–1009CrossRefGoogle Scholar
  16. Slavkovik, M. (2012). Judgment aggregation for multiagent systems. PhD Thesis, University of Luxembourg.
  17. Slavkovik, M., & Jamroga, W. (2011). Distance-based judgment aggregation of three-valued judgments with weights. In E. Elkind, U. Endriss & J. Lang (Eds.), Proceedings of the IJCAI Workshop on Social Choice and Artificial Intelligence (pp. 81–87).Google Scholar
  18. Urquhart A. (2001) Basic many-valued logic. In: Gabbay D., Guenthener F. (eds) Handbook of philosophical logic (2nd ed., Vol. 2). Kluwer Academic Publishers, Dordrecht, pp 249–295Google Scholar
  19. Warwick, W., McIlwaine, S., Hutton, R., & McDermott, P. (2001). Developing computational models of recognition-primed decision making. In Proceedings of the 10th Conference on Computer Generated Forces.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.University of Luxembourg, University of LiverpoolLuxembourgLuxembourg
  2. 2.University of TurinTurinItaly

Personalised recommendations