Predictability for Autonomous Decision Support

  • Francisco Coelho
  • Helder Coelho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3891)


The experimental scientist need tools to quantify and classify collected data. This paper proposes to give meaning and measure to the intuitive concept of predictability. It is a global and time dependent real valued quantity that, we argue, indicates how hard it is to make a forecast for the next value on a time series. We start with a a definition of predictability for binary words and show properties about its growth and computational cost. Our measure evaluates in time On 3, what is an acceptable performance specially for supporting bounded time decisions. Then, we investigate application procedures illustrated with data achieved from iterations of the logistic map, economic simulations and the Portuguese GDP (Gross Domestic Product).


MultiAgent System Social Rank Predictability Function Chaotic Regime Individual Power 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Luck, M., McBurney, P., Shehory, O., Willmott, S.: the AgentLink Community: Agent technology: Computing as interaction –a roadmap for agent based computing–. Technical report, AgentLink (2005)Google Scholar
  2. 2.
    Wooldridge, M.: An Introduction to MultiAgent Systems. John Wiley and Sons Ltd, Chichester (2002)Google Scholar
  3. 3.
    Walczak, A.: Planning and the belief-desire-intention model of agency. Technical report, University of Hamburg (2005)Google Scholar
  4. 4.
    Pörn, I.: The Logic of Power. Basil Blackwell, Oxford (1970)Google Scholar
  5. 5.
    Fasli, M.: On the interplay of roles and power. In: Skowron, A., Barthes, J.P., Ron Sun, L.J. (eds.) IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 499–503 (2005)Google Scholar
  6. 6.
    López y López, F., Luck, M., d’Inverno, M.: Normative agent reasoning in dynamic societies. In: Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2003)Google Scholar
  7. 7.
    Castelfranchi, C.: Social Power: A point missed in Multi-Agent, DAI and HCI. In: Demazeau, Y., Müller, J.P. (eds.) Decentralized A.I., pp. 49–62. North-Holland, Amsterdam (1989)Google Scholar
  8. 8.
    Castelfranchi, C., Falcone, R.: Principles of trust for MAS. In: Cognitive anatomy, social importance and quantification, pp. 72–79. IEEE, Los Alamitos (1998)Google Scholar
  9. 9.
    Castelfranchi, C.: The micro-macro constitution of power. personal communication (2003)Google Scholar
  10. 10.
    Coelho, F., Coelho, H.: Towards individual power design. In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS (LNAI), vol. 2902, pp. 366–378. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Caldas, J.C., Coelho, H.: The interplay of power on the horizon. In: Proceedings of the II International Conference on Computer Simulation and the Social Sciences, Paris (2000)Google Scholar
  12. 12.
    Grimm, V., Railsback, S.F.: Individual-based Modelling and Ecology. Princeton University Press, Princeton (2005)MATHGoogle Scholar
  13. 13.
    Shalizi, C.R.: Methods and techniques of complex systems science: An overview (2003)Google Scholar
  14. 14.
    Fraser, A.M., Dimitriadis, A.: Forecasting probability densities by using hidden markov models with mixed states. In: Time Series Prediction: Forecasting the Future and Understanding the Past, Addison-Wesley, Reading (1993)Google Scholar
  15. 15.
    Viterbi., A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13, 260–267 (1967)CrossRefMATHGoogle Scholar
  16. 16.
    Vitanyi, P.: Algorithmic chaos (2003)Google Scholar
  17. 17.
    Hopcroft, J.E., Ullman, J.D.: Introduction to Automata and Formal Languages. Addison-Wesley, Reading (1979)MATHGoogle Scholar
  18. 18.
    Takens, F.: Detecting strange attractors in fluid turbulence. In: Rand, D.A., Young, L.S. (eds.) Symposium on Dynamical Systems and Turbulence, pp. 366–381. Springer, Heidelberg (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Francisco Coelho
    • 1
  • Helder Coelho
    • 2
  1. 1.Departamento de MatemáticaUniversidade de ÉvoraPortugal
  2. 2.Departamento de Informática, Faculdade de CiênciasUniversidade de LisboaPortugal

Personalised recommendations