Environment Systems and Decisions

, Volume 38, Issue 1, pp 88–91 | Cite as

Learning and open data in sustainability transitions: evolutionary implications of the theory of probabilistic functionalism

  • Masaru YarimeEmail author


The Theory of Probabilistic Functionalism, as a general theory of how organisms interact with complex environmental systems, provides a useful framework for describing essential processes of sustainability planning groups. Particularly, the mechanism of producing evolutionary stable representations of and judgment about the environment has important implications for sustainability transitions. In comparison with biological evolution, the environment changes much faster in social phenomena, and the selection dynamics working on heterogeneous groups for successful survival would be incomplete. That makes continuous learning by the members of planning groups as well as collective decision-making among them critically important. The policy of open data for assembling, distributing, and utilizing various kinds of data will facilitate vision creation, strategy development, and consensus building with stakeholders for sustainability transitions.


Theory of probabilistic functionalism Sustainability transition Evolutionary process Learning Open data 


  1. Avelino F, Grin J, Pel B, Jhagroe S (2016) The politics of sustainability transitions. J Environ Plan Policy Manage 18(5):557–567CrossRefGoogle Scholar
  2. Beinhocker ED (2006) The origin of wealth: evolution, complexity and the radical remaking of economics. Harvard Business School Press, BostonGoogle Scholar
  3. Dosi G, Nelson RR (2010) Chapter 3—technical change and industrial dynamics as evolutionary processes. In: Bronwyn HH, Nathan R (eds) Handbook of the economics of innovation. Elsevier, AmsterdamGoogle Scholar
  4. Elster J (1989) Nuts and bolts for the social sciences. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  5. Marshall A (1890) Principles of economics. Macmillan, LondonGoogle Scholar
  6. Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Belknap Press of Harvard University Press, CambridgeGoogle Scholar
  7. Scholz RW (2011) Environmental literacy in science and society: from knowledge to decisions. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  8. Scholz RW (2017) Managing complexity: from visual perception to sustainable transitions—contributions of Brunswik’s theory of probabilistic functionalism. Environ Syst Decis 37(4):381–409Google Scholar
  9. Veblen TB (1898) Why is economics not an evolutionary science? Q J Econ 12(3):373–397CrossRefGoogle Scholar
  10. Yarime M (2017) Facilitating data-intensive approaches to innovation for sustainability: opportunities and challenges in building smart cities. Sustain Sci 12(6):881–885CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Energy and EnvironmentCity University of Hong KongKowloonHong Kong
  2. 2.Department of Science, Technology, Engineering and Public PolicyUniversity College LondonLondonUK
  3. 3.Graduate School of Public PolicyUniversity of TokyoTokyoJapan

Personalised recommendations