Advertisement

Socionics pp 1-14 | Cite as

Contribution of Socionics to the Scalability of Complex Social Systems: Introduction

  • Klaus Fischer
  • Michael Florian
Chapter
  • 511 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3413)

Abstract

The aim of the introduction is to provide insight into the interdisciplinary research program of Socionics and to clarify fundamental concepts like micro-macro linkage and scalability from the two different perspectives of Sociology and DAI&MAS research. Far away from the intention to offer final answers, the article rather tries to provide a framework to understand the contributions of the book as well as to relate their content to each other. The introduction also informs the reader about the scientific context of the interdisciplinary field of Socionics and deals with basic concepts and comments from the point of view of both Sociology and DAI&MAS research.

Keywords

Multiagent System Macro Level Sociological Theory Meso Level Sociological Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Müller, J., Malsch, T., Schulz-Schäffer, I.: Socionics. Introduction and potential. Journal of Artificial Societies and Social Simulation 1 (1998), http://www.soc.surrey.ac.uk/JASSS/1/3/5.html
  2. 2.
    Hewitt, C.E.: Offices are open systems. ACM Transactions on Office Information Systems 4, 271–287 (1986)CrossRefGoogle Scholar
  3. 3.
    Hewitt, C.E.: Open information systems semantics for distributed artificial intelligence. Artificial Intelligence 47, 79–106 (1991)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Gasser, L.: Social conceptions of knowledge and action. DAI foundations and open systems semantics. Artificial Intelligence 47, 107–138 (1991)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Malsch, T.: Naming the unnamable. Socionics or the sociological turn of/to Distributed Artificial Intelligence. Autonomous Agents and Multi-Agent Systems 4, 155–186 (2001)CrossRefGoogle Scholar
  6. 6.
    Hayes-Roth, F.: Towards a framework for distributed AI. Sigart Newsletter 73, 51 (1980)Google Scholar
  7. 7.
    Bond, A., Gasser, L. (eds.): Readings in Distributed Artificial Intelligence. Morgan Kaufmann, San Francisco (1988)Google Scholar
  8. 8.
    Huhns, M.N. (ed.): Distributed Artificial Intelligence. Pitman/Morgan Kaufmann, San Francisco (1987)zbMATHGoogle Scholar
  9. 9.
    Gasser, L., Huhns, M.N. (eds.): Distributed Artificial Intelligence. Research Notes in Artificial Intelligence, vol. II. Morgan Kaufmann, San Mateo (1989)Google Scholar
  10. 10.
    Strübing, J.: Bridging the gap. On the collaboration between symbolic interactionism and distributed artificial intelligence in the field of multi-agent systems research. Symbolic Interaction 21, 441–464 (1998)CrossRefGoogle Scholar
  11. 11.
    Castelfranchi, C., Conte, R.: Distributed artificial intelligence and social science: Critical issues. In: O’Hare, G.M.P., Jennings, N.R. (eds.) Foundations of Distributed Artificial Intelligence, pp. 527–542. John Wiley & Sons, Inc, Chichester (1996)Google Scholar
  12. 12.
    Hechter, M.: The Microfoundations of Macrosociology. Temple University Press, Philadelphia (1983)Google Scholar
  13. 13.
    Collins, R.: On the microfoundations of macrosociology. American Journal of Sociology 86, 984–1010 (1981)CrossRefGoogle Scholar
  14. 14.
    Coleman, J.: Microfoundations and macrosocial behavior. [22] 153–173Google Scholar
  15. 15.
    Coleman, J.: Foundations of Social Theory. The Belknap Press, Cambridge (1990)Google Scholar
  16. 16.
    Russel, S.J., Norvig, P.: Artificial Intelligence — A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2003)Google Scholar
  17. 17.
    Wooldridge, M.J., Jennings, N.R. (eds.): ECAI 1994 and ATAL 1994. LNAI, vol. 890. Springer, Heidelberg (1995)zbMATHGoogle Scholar
  18. 18.
    Johnson, W., Hayes-Roth, B.: Proceedings of the First International Conference on Autonomous Agents. ACM Press, Marina del Rey (1997)CrossRefGoogle Scholar
  19. 19.
    Gerstein, D.: To unpack micro and macro: Link small with large and part with whole. [22] 86–11Google Scholar
  20. 20.
    Münch, R., Smelser, N.J.: Relating the micro and macro. [22] 356–387Google Scholar
  21. 21.
    Ritzer, G.: Sociological Theory, 4th edn. McGraw-Hill, New York (1996)Google Scholar
  22. 22.
    Alexander, J., Giesen, B., Münch, R., Smelser, N. (eds.): The Micro-Macro Link. University of California Press, Berkeley (1987)Google Scholar
  23. 23.
    Huber, J. (ed.): Macro-Micro Linkages in Sociology. Sage Publications, Newbury Park (1991)Google Scholar
  24. 24.
    Knorr-Cetina, K., Cicourel,K.: (eds.): Advances in Social Theory and Methodology. Methuen, New York (1981)Google Scholar
  25. 25.
    Wiley, N.: The micro-macro problem in social theory. Sociological Theory 6, 254–261 (1988)CrossRefGoogle Scholar
  26. 26.
    Kemeny, J.: Perspectives on the micro-macro distinction. Sociological Review 24, 731–752 (1976)Google Scholar
  27. 27.
    Alexander, J.: Action and its environments. [22] 289–318Google Scholar
  28. 28.
    Blau, P.: Contrasting theoretical perspectives. [22] 71–85Google Scholar
  29. 29.
    Coleman, J.: Social theory, social research, and a theory of action. American Journal of Sociology 91, 1309–1335 (1986)CrossRefGoogle Scholar
  30. 30.
    Weiß, G. (ed.): Multi-Agent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge (1999)Google Scholar
  31. 31.
    Nwana, H.S.: Software agents: An overview. Knowledge Engineering Review, 205–244 (1996)Google Scholar
  32. 32.
    Schillo, M., Fischer, K., Klein, C.: The micro-macro link in DAI and sociology. [50]Google Scholar
  33. 33.
    Sandholm, T.: Distributed rational decision making. [30]Google Scholar
  34. 34.
    Yamagishi, T.: The provision of a sanctioning system as a public good. Journal of Personality and Social Psychology 51, 110–116 (1986)CrossRefGoogle Scholar
  35. 35.
    Langton, C.G., ed.: Artificial Life I (ALIFE-87): 1st Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems, Los Alamos, NM, USA. Addison-Wesley, Reading (1989) Google Scholar
  36. 36.
    Sichman, J.S., Conte, R., Castelfranchi, C., Demazeau, Y.: A social reasoning mechanism based on dependence networks. In: Cohn, A.G. (ed.) Proc. of the 11th European Conf. on Artificial Intelligence, ECAI 1994 (1994)Google Scholar
  37. 37.
    Castelfranchi, C., Falcone, R.: Principles of trust for MAS: Cognitive anatomy, social importance, and quantification. In: Demazeau, Y. (ed.) Proc. of the 3rd Int. Conf. on Multi-Agent Systems, ICMAS 1998 (1998)Google Scholar
  38. 38.
    Durfee, E.: Scaling up agent coordination strategies. IEEE Computer 34, 39–46 (2001)Google Scholar
  39. 39.
    Fischer, K., Schillo, M., Siekmann, J.: Holonic multiagent systems: The foundation for the organization of multiagent systems. In: Mařík, V., McFarlane, D.C., Valckenaers, P. (eds.) HoloMAS 2003. LNCS (LNAI), vol. 2744, pp. 71–80. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  40. 40.
    Schillo, M., Fischer, K., Siekmann, J.: The link between autonomy and organisation in multiagent systems. In: Mařík, V., McFarlane, D.C., Valckenaers, P. (eds.) HoloMAS 2003. LNCS (LNAI), vol. 2744, pp. 81–90. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  41. 41.
    Rosenschein, J.S., Zlotkin, G.: Rules of Encounter: Designing Conventions for Automated Negotiation among Computers. MIT Press, Cambridge (1994)Google Scholar
  42. 42.
    Fischer, K., Müller, J.P., Pischel, M.: A model for cooperative transportation scheduling. In: Proceedings of the 1st International Conference on Multiagent Systems (ICMAS 1995), San Francisco, pp. 109–116 (1995)Google Scholar
  43. 43.
    Schillo, M., Kray, C., Fischer, K.: The eager bidder problem: A fundamental problem of DAI and selected solutions. In: Proceedings of the First International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2002), pp. 599–608 (2002)Google Scholar
  44. 44.
    Lesser, V., Decker, K., Wagner, T., Carver, N., Garvey, A., Horling, B., Neiman, D., Podorozhny, R., Nagendra Prasad, M., Raja, A., Vincent, R., Xuan, P., Zhang, X.Q.: Evolution of the GPGP/TAEMS domain-independent coordination framework. Autonomous Agents and Multi-Agent Systems 9, 87–143 (2004)CrossRefGoogle Scholar
  45. 45.
    Klusch, M.: Agent-mediated trading: Intelligent agents and e-business. In: Hayzelden, A., Bourne, R. (eds.) Agent Technology applied to Networked Systems.John Wiley & Sons, Chichester (2000)Google Scholar
  46. 46.
    Klusch, M.: Information agent technology for the internet: A survey. In: Fensel, D. (ed.) Journal on Data and Knowledge Engineering, Special Issue on Intelligent Information Integration, vol. 36. Elsevier Science, Amsterdam (2001)Google Scholar
  47. 47.
    Turner, P.J., Jennings, N.R.: Improving the scalability of multi-agent systems. In: Proc. 1st Int. Workshop on Infrastructure for Scalable Multi-Agent Systems, Barcelona, Spain (2000)Google Scholar
  48. 48.
    Wellman, B., Berkowitz, S. (eds.): Social Structures: A Network Approach. Cambridge University Press, Cambridge (1988)Google Scholar
  49. 49.
    House, R., Rousseau, D., Thomas-Hunt, M.: The meso paradigm: A framework for the integration ofmicro and macro organizational behavior. Research in Organizational Behavior 17, 71–114 (1995)Google Scholar
  50. 50.
    Moss, S., Davidsson, P. (eds.): MABS 2000. LNCS (LNAI), vol. 1979. Springer, Heidelberg (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Klaus Fischer
    • 1
  • Michael Florian
    • 2
  1. 1.German Research Center for Artificial Intelligence (DFKI) GmbHSaarbrückenGermany
  2. 2.Department of Technology AssessmentHamburg University of TechnologyHamburgGermany

Personalised recommendations