Open Systems Exploration: An Example with Ecosystems Management

  • Masatoshi FunabashiEmail author
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


Open complex systems in operation can only be managed with internal observation, where the reproducibility and predictability of conventional science with external observation is not necessary valid nor fully achievable. However, globally important problems are mostly open systems that beget one-time-only events. Still, in open complex systems, internal observers may better manage the system by interactive database and interfaces to explore effective variables. We develop plural modalities of such data-driven interface for open systems exploration, taking an example in ecosystems management with citizen observation. The examples are developed from data-supported widening of choice, suggestion with statistical inference, and to a basic setup that can interactively select a best prediction model with inputs on-the-fly. These interfaces were applied to 1-year field observation and yielded a tentative scoring system of index species. We define basic conceptual framework with a realization of the initial steps of open systems exploration, that will subsequently follow interactive reconfiguration as the systems evolve.


Voronoi Diagram Ecosystem Management Hide State Information Communication Technology Symbolic Dynamic 
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.



The author acknowledges Hidemori Yazaki, Kousaku Ohta, Tatsuya Kawaoka, Kazuhiro Takimoto, and Shuntaro Aotake who worked as research assistant.


  1. 1.
    Funabashi M (2010) Dynamical system and information geometry - a complementary approach to complex systems. Ph.D. thesis, General Physics, Ecole Polytechnique XGoogle Scholar
  2. 2.
    Olivier N et al (2010) Cell lineage reconstruction of early zebrafish embryos using label-free nonlinear microscopy. Science 329(5994):967–971ADSCrossRefGoogle Scholar
  3. 3.
    Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20(2):130–141ADSCrossRefGoogle Scholar
  4. 4.
    Xu L et al (2013) Temperature and vegetation seasonality diminishment over northern lands. Nat Clim Chang 3:581–586ADSGoogle Scholar
  5. 5.
    Kaneko K, Tsuda I (2000) Complex systems: chaos and beyond, a constructive approach with applications in life sciences. Springer, BerlinzbMATHGoogle Scholar
  6. 6.
    Funabashi M (2015) Synthetic modeling of autonomous learning with a chaotic neural network. Int J Bifurcation Chaos 25(4):1550054MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Kaneko K, Tsuda I (1994) Constructive complexity and artificial reality: an introduction. Physica D 75:l–10Google Scholar
  8. 8.
    Tokoro M (2010) What is open systems science? In: Tokoro M (ed) Open systems science: from understanding principles to solving problems (the future of learning). IOS Press, AmsterdamGoogle Scholar
  9. 9.
    Tokoro M (2015) A new method for new challenges. Sci Am, vol 477, 64–66; Innovation Special IssueGoogle Scholar
  10. 10.
    Taleb NN (2010) The black swan: the impact of the highly improbable. Random House Trade Paperbacks, New YorkGoogle Scholar
  11. 11.
    Diamond JM (1999) Guns, germs, and steel: the fates of human societies. W. W. Norton & Company, New YorkGoogle Scholar
  12. 12.
    Kurzweil R (2006) The singularity is near: when humans transcend biology. Penguin Books, New YorkGoogle Scholar
  13. 13.
    Barnosky AD et al (2012) Approaching a state shift in Earth’s biosphere. Nature 486:52–58ADSCrossRefGoogle Scholar
  14. 14.
    Porta M (ed) (2008) Dictionary of epidemiology. Oxford University Press, OxfordGoogle Scholar
  15. 15.
    Ben-Shlomo Y, Kuh D (2002) A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Int J Epidemiol 31(2):285–293CrossRefGoogle Scholar
  16. 16.
    Tokoro M (2015) Open systems dependability: dependability engineering for ever-changing systems. CRC Press, Boca RatonCrossRefGoogle Scholar
  17. 17.
    Petherick A (2012) The ten countries most vulnerable to climate change and their corruption rating. Nat Clim Chang 2:144–145ADSCrossRefGoogle Scholar
  18. 18.
    Strogatz SH (2014) Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. Westview Press, CambridgezbMATHGoogle Scholar
  19. 19.
    Murphy KP (2012) Machine learning: a probabilistic perspective. The MIT Press, CambridgezbMATHGoogle Scholar
  20. 20.
    Funabashi M (2014) Network decomposition and complexity measures: an information geometrical approach. Entropy 16(7):4132–4167ADSCrossRefGoogle Scholar
  21. 21.
    Hayes PJ (1971) The Frame problem and related problems in artificial intelligence. Technical Report, Stanford University, Stanford, CA, USAGoogle Scholar
  22. 22.
    Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: biodiversity synthesis. World Resources Institute, Washington, DC. Google Scholar
  23. 23.
    Yeo-Chang Y, Misum P (2014) Value of traditional knowledge in forest policy process. In: Proceedings of international conference, sustainable management including the use of traditional knowledge in Satoyama and other SELPS, pp 73–78Google Scholar
  24. 24.
    Ministry of Agriculture, Forestry and Fisheries of Japan (2012) Index species assessment manual of biodiversity for agriculture (in Japanese), (last viewed on 30 Jun 2015)
  25. 25.
    The International Partnership for the Satoyama Initiative (IPSI) (2014) Toolkit for the indicators of resilience in socio-ecological production landscapes and seascapes (SEPLS), (last viewed on 30 Jun 2015)
  26. 26.
    Jensen ME, Bourgeron PS (2000) A guidebook for integrated ecological assessment Springer, New YorkGoogle Scholar
  27. 27.
    Biggs R et al (2009) Turning back from the brink: detecting an impending regime shift in time to avert it. Proc Natl Acad Sci U S A 106:826–831ADSCrossRefGoogle Scholar
  28. 28.
    ISC-PIF (Institut des Systémes Complexes, Paris Île-de-France) (2009) French roadmap for complex systems 2008–2009. ISC-PIF, Paris.
  29. 29.
    Funabashi M (2013) IT-mediated development of sustainable agriculture systems–toward a data-driven citizen science. J Inf Technol Appl Educ 2(4):179–182Google Scholar
  30. 30.
    Pool R (1989) Ecologists flirt with chaos. Science 243(4889):310–313ADSCrossRefGoogle Scholar
  31. 31.
    Hoffman A et al (2014) Improved access to integrated biodiversity data for science, practice, and policy - the European biodiversity observation network (EU BON). Nat Conserv 6:49–65CrossRefGoogle Scholar
  32. 32.
    Pettorelli N, Kamran Safi K, Turner W (2014) Satellite remote sensing, biodiversity research and conservation of the future. Philos Trans R Soc Lond B Biol Sci 369(1643):20130190CrossRefGoogle Scholar
  33. 33.
    Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327:828–831ADSCrossRefGoogle Scholar
  34. 34.
    Xu H-L (2001) Nature farming history, principles and perspectives. J Crop Prod 3(1):1–10CrossRefGoogle Scholar
  35. 35.
    Fujita M (2001) Nature farming practices for apple production in Japan. J Crop Prod 3(1):119–125MathSciNetCrossRefGoogle Scholar
  36. 36.
    Seifert V, Ramankutty N, Foley JA (2012) Comparing the yields of organic and conventional agriculture. Nature 485:229–232ADSCrossRefGoogle Scholar
  37. 37.
    Tuck SL et al (2014) Land-use intensity and the effects of organic farming on biodiversity: a hierarchical meta-analysis. J Appl Ecol 51:746–755CrossRefGoogle Scholar
  38. 38.
    Bengtsson J, Ahnstrom J, Weibull A-C (2005) The effects of organic agriculture on biodiversity and abundance: a meta-analysis. J Appl Ecol 42:261–269CrossRefGoogle Scholar
  39. 39.
    Douglas L, Brian M (1995) An introduction to symbolic dynamics and coding. Cambridge University Press, CambridgezbMATHGoogle Scholar
  40. 40.
    Atsuyuki Okabe A, Boots B, Sugihara K, Chiu S-N (2000) Spatial tessellations - concepts and applications of voronoi diagrams, 2nd edn. Wiley, Chichester, p 671CrossRefzbMATHGoogle Scholar
  41. 41.
    Automated Meteorological Data Acquisition System (AMeDAS). (last viewed on 30 Jun 2015)
  42. 42.
    Funabashi M, Chavalarias D, Cointet J-P (2009) Order-wise correlation dynamics in text data. In: Complex networks, studies in computational intelligence, vol 207. Springer, New York, pp 161–171Google Scholar
  43. 43.
    Hirata Y, Judd K, Kilminster D (2004) Estimating a generating partition from observed time series: symbolic shadowing. Phys Rev E 70:016215ADSMathSciNetCrossRefGoogle Scholar
  44. 44.
    Baum LE, Ted Petrie T (1966) Statistical inference for probabilistic functions of finite state Markov chains. Ann Math Stat 37(6):1554–1563MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Sony Computer Science Laboratories, Inc.TokyoJapan

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