Towards a Context- and Scope-Sensitive Analysis for Specifying Agent Behaviour

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 229)

Abstract

A structure for analysing narrative data is suggested, one that distinguishes three parts: context, scope and narrative elements. This structure is first motivated and then illustrated with some simple examples taken from Sukaina Bhawani’s thesis. It is hypothesised that such a structure might be helpful in preserving more of the natural meaning of such data, as well as being a good match to a context-dependent computational architecture. This structure could clearly be combined and improved by other methods, such as Grounded Theory. Finally some criteria for judging any such method are suggested.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abell, P.: The role of rational choice and narrative action theories in sociological theory the legacy of cole- 6 Figure 5: events and goals. man’s foundations. Revue Franaisede Sociologie 44(2), 255–273 (2003)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bennett, J.W.: The Ecological Transition: Cultural Anthropology and Human Adaptation. Pergamon Press Inc., New York (1976)Google Scholar
  3. 3.
    Bhawani, S.: Adaptive Knowledge Dynamics and Emergent Artificial Societies: Ethnographically Based Multi-Agent Simulations of Behavioural Adaptation in Agro-Climatic Systems. Doctoral Thesis, University of Kent, Canterbury, UK (2004), http://cfpm.org/qual2rule/Sukaina%20Bharwani%20Thesis.pdf
  4. 4.
    Bharwani, S.: Understanding complex behaviour and decision making using ethnographic knowledge elicitation games (Kn ETs). Social Science Computer Review 24(1), 78–105 (2006)CrossRefGoogle Scholar
  5. 5.
    Edmonds, B.: The Pragmatic Roots of Context. In: Bouquet, P., Serafini, L., Brézillon, P., Benercetti, M., Castellani, F. (eds.) CONTEXT 1999. LNCS (LNAI), vol. 1688, pp. 119–132. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Edmonds, B., Norling, E.: Integrating Learning and Inference in Multi-Agent Systems Using Cognitive Context. In: Antunes, L., Takadama, K. (eds.) MABS 2006. LNCS (LNAI), vol. 4442, pp. 142–155. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Edmonds, B.: Towards an Ideal Social Simulation Language. In: Sichman, J.S., Bousquet, F., Davidsson, P. (eds.) MABS 2002. LNCS (LNAI), vol. 2581, pp. 105–124. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Gigerenzer, G., Goldstein, D.G.: Reasoning the fast and frugal way: models of bounded rationality. Psychological Review 103(4), 650 (1996)CrossRefGoogle Scholar
  9. 9.
    Kemp-Benedict, E.J., Bharwani, S., Fischer, M.D.: Methods for linking social and physical analysis for sustainability planning. Ecology and Society 15(3), 4 (2010), http://www.ecologyandsociety.org/vol15/iss3/art4/ Google Scholar
  10. 10.
    Kokinov, B., Grinberg, M.: Simulating Context Effects in Problem Solving with AMBR. In: Akman, V., Bouquet, P., Thomason, R.H., Young, R.A. (eds.) CONTEXT 2001. LNCS (LNAI), vol. 2116, pp. 221–234. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Luhmann, N.: SozialeSysteme. GrundrisseinerallgemeinenTheorie, Frankfurt/M (1984); Engl.: Social Systems. Stanford University Press (1995)Google Scholar
  12. 12.
    McCarthy, J., Hayes, P.J.: Some Philosophical Problems from the Standpoint of Artificial Intelligence. Readings in Planning, 393 (1990)Google Scholar
  13. 13.
    Moss, S., Gaylard, H., Wallis, S., Edmonds, B.: SDML: A Multi-Agent Language for Organizational Modelling. Computational and Mathematical Organization Theory 4, 43–69 (1998)CrossRefGoogle Scholar
  14. 14.
    Norling, E.: Folk psychology for human modelling: Extending the BDI paradigm. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 202–209. IEEE Computer Society (July 2004)Google Scholar
  15. 15.
    ten Have, P.: Doing Conversation Analysis: A Practical Guide (Introducing Qualitative Methods). SAGE Publications (1999)Google Scholar
  16. 16.
    Simon, H.A.: Administrative behaviour, a Study of decision-making processes in Administrative Organization. Macmillan (1947)Google Scholar
  17. 17.
    Taylor, R.I.: Agent-Based Modelling Incorporating Qualitative and Quantitative Methods: A Case Study Investigating the Impact of E-commerce upon the Value Chain. Doctoral Thesis, Manchester Metropolitan University, Manchester, UK (2003), http://cfpm.org/cpmrep137.html
  18. 18.
    Tomasello, M.: The cultural origins of human cognition. Harvard University Press (1999)Google Scholar
  19. 19.
    Toulmin, S.: The uses of argument. Cambridge University Press (2003)Google Scholar
  20. 20.
    Urquhart, C.: Grounded Theory for Qualitative Research: A Practical Guide. SAGE Publications Limited (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Centre for Policy ModellingManchester Metropolitan UniversityManchesterUnited Kingdom

Personalised recommendations