Intelligent Data Analysis of Intelligent Systems

  • David C. Krakauer
  • Jessica C. Flack
  • Simon Dedeo
  • Doyne Farmer
  • Daniel Rockmore
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6065)

Abstract

We consider the value of structured priors in the analysis of data sampled from complex adaptive systems. We propose that adaptive dynamics entails basic constraints (memory, information processing) and features (optimization and evolutionary history) that serve to significantly narrow search spaces and candidate parameter values. We suggest that the property of “adaptive self-awareness”, when applicable, further constrains model selection, such that predictive statistical models converge on a systems own internal representation of regularities. Principled model building should therefore begin by identifying a hierarchy of increasingly constrained models based on the adaptive properties of the study system.

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References

  1. 1.
    Hughes, J.M., Graham, D.J., Rockmore, D.N.: Quantification of artistic style through sparse coding analysis in the drawings of Pieter Bruegel the Elder. Proceedings of the National Academy of Sciences USA 107, 1279–1283Google Scholar
  2. 2.
    Hughes, J.M., Graham, D.J., Rockmore, D.N.: Stylometrics of artwork: Uses and limitations. In: Proc. SPIE: Computer Vision and Image Analysis of Art 7531 (2010) (in press)Google Scholar
  3. 3.
    Johnson Jr., C.R., Hendriks, E., Berezhnoy, I., Brevdo, E., Hughes, S., Daubechies, I., Li, J., Postma, E., Wang, J.: Image processing for artist identification – computerized analysis of Vincent van Gogh’s painting brushstrokes. IEEE Signal Processing Magazine, Special Issue on Visual Cultural Heritage 25(4), 37–48 (2008)Google Scholar
  4. 4.
    Olshausen, B., Field, D.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)CrossRefGoogle Scholar
  5. 5.
    Olshausen, B., Field, D.: Sparse coding with an overcomplete basis set: A strategy employed by v1. Vision Research 37(23), 3311–3325 (1997)CrossRefGoogle Scholar
  6. 6.
    Clutton-Brock, T., Parker, G.: Punishment in animal societies. Nature 373, 209–216 (1995)CrossRefGoogle Scholar
  7. 7.
    Frank, S.: Repression of competition and the evolution of cooperation. Evolution 57, 693–705 (2003)Google Scholar
  8. 8.
    Clutton-Brock, T., Albon, S.D., Gibson, R.M., Guinness, F.E.: The logical stag: Adaptive aspects of fighting in red deer (cervus elaphus l.). Anim. Behav. 27, 211–225 (1979)CrossRefGoogle Scholar
  9. 9.
    Maynard Smith, J.: The theory of games and the evolution of animal conflicts. J. Theor. Biol. 47, 209 (1974)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Parker, G., Rubenstein, D.I.: Role of assessment, reserve strategy, and acquisition of information in asymmetric animal conflicts. Anim. Behav. 29, 221–240 (1981)CrossRefGoogle Scholar
  11. 11.
    Taylor, P.W., Elwood, R.W.: The mismeasure of animal contests. Anim. Behav. 65, 1195–1202 (2003)CrossRefGoogle Scholar
  12. 12.
    Mesterton-Gibbons, M., Sherratt, T.N.: Coalition formation: a game-theoretic analysis. Behav. Ecol. 18, 277–286 (2006)CrossRefGoogle Scholar
  13. 13.
    Noe, R., Hammerstein, P.: Biological markets. Trends Ecol. Evol. 10, 336–339 (1995)CrossRefGoogle Scholar
  14. 14.
    Johnstone, R.A.: Eavesdropping and animal conflict. P. Natl. Acad. Sci. USA 98, 9177–9180 (2001)CrossRefGoogle Scholar
  15. 15.
    Covas, R., McGregor, P.K., Doutrelant, C.: Cooperation and communication networks. Behav. Process. 76, 149–151 (2007)CrossRefGoogle Scholar
  16. 16.
    Nowak, M., Sigmund, K.: Evolution of indirect reciprocity by image scoring. Nature 393, 573–577 (1998)CrossRefGoogle Scholar
  17. 17.
    Kamitani, Y., Tong, F.: Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8(5), 679–685 (2005)CrossRefGoogle Scholar
  18. 18.
    Hanson, S.J., Halchenko, Y.O.: Brain Reading Using Full Brain Support VectorMachines for Object Recognition: There Is No ÒFaceÓ Identification Area. Neural Computation 20, 486–503 (2008)MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Dreber, A., Rand, D.G., Fudenberg, D., Nowak, M.A.: Winners don’t punish. Nature 452, 348–351 (2008)CrossRefGoogle Scholar
  20. 20.
    Taylor, P.W., Elwood, R.W.: The mismeasure of animal contests. Anim. Behav. 65, 1195–1202 (2003)CrossRefGoogle Scholar
  21. 21.
    Kazem, A.J.N., Aureli, F.: Redirection of aggression: Multiparty signaling within a network. In: McGregor, P. (ed.) Animal Communication Networks. Cambridge University Press, Cambridge (2005)Google Scholar
  22. 22.
    Flack, J.C., Girvan, M., de Waal, F.B.M., Krakauer, D.C.: Policing stabilizes construction of social niches in primates. Nature 439, 426–429 (2006)CrossRefGoogle Scholar
  23. 23.
    Dedeo, S., Krakauer, D.C., Flack, J.C.: Inductive game theory and the dynamics of animal conflict. Plos. Computational Biol. (2010)Google Scholar
  24. 24.
    Farmer, J.D., Geanakoplos, J.: ÒThe Virtues and Vices of Equilibrium and the Future of Financial Economics. Complexity 14, 11–38 (2009)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Gode, D.K., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. The Journal of Political Economy 101(1), 119–137 (1993)CrossRefGoogle Scholar
  26. 26.
    Daniels, M.G., Farmer, J.D., Gillemot, L., Iori, G., Smith, E.: Quantitative model of price diffusion and market friction based on trading as a mechanistic random process. Physical Review Letters Article no. 108102, 90(10) (2003)Google Scholar
  27. 27.
    Farmer, J.D., Patelli, P., Zovko, I.I.: The Predicitive Power of Zero Intelligence in Financial Markets. PNAS USA 102(11), 2254–2259 (2005)CrossRefGoogle Scholar
  28. 28.
    Bouchaud, J.-P., Doyne Farmer, J., Lillo, F.: ÒHow Markets Slowly Digest Changes in Supply and Demand.Ó. In: Hens, T., Schenk-Hoppe, K. (eds.) Handbook of Financial Markets: Dynamics and Evolution. Elsevier/Academic Press (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • David C. Krakauer
    • 1
  • Jessica C. Flack
    • 1
  • Simon Dedeo
    • 1
  • Doyne Farmer
    • 1
  • Daniel Rockmore
    • 1
  1. 1.Santa Fe InstituteSanta Fe, New MexicoUSA

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