Skip to main content
SpringerLink
Log in

Virtual and Real World Experimentation

  • Chapter
  • First Online:
Water Governance in the Face of Global Change

Part of the book series: Water Governance - Concepts, Methods, and Practice ((WGCMP))

  • 2136 Accesses

Abstract

Understanding human behaviour and complex societal dynamics is essential for understanding and supporting transformative change. Such change may often require investigations beyond the realm of observed patterns of behaviour. This chapter elaborates on the potential of virtual and real world experimentation to broaden the scope of analyses, in order to foster creativity and innovation and to explore new terrains that are beyond current experience. Simulation models are a tool whose potential has only recently started to be exploited in the social sciences. The chapter discusses the role of models for exploratory analyses in this field, but also for supporting communication and social learning that contribute to or stimulate transformative change. It elaborates on the role of virtual and real world laboratories to build knowledge and capacity for transformative change.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    All scientific disciplines, as referred to in this book, embrace natural and social sciences (including economics), engineering and humanities.

  2. 2.

    The concept of bounded rationality was originally introduced by Simon (1982) as an alternative and more realistic model for human decision making in comparison with the rational actor model of neo-classical economics. Bounded rationality takes into account the fact that actors only have access to a limited amount of information and only have a limited amount of time available to evaluate alternative options. Bounded rational actors are satisficers who search for satisfactory rather than optimal solutions.

References

  • Argyris, C. (1995). Action science and organizational learning. Journal of Managerial Psychology, 10(6), 20–26.

    Article  Google Scholar 

  • Argyris, C., Putnam, R., & McLain Smith, D. (1985). Action science—concepts, methods and skills for research and intervention. San Francisco: Joessey-Bass Publishers.

    Google Scholar 

  • Balke, T., & Gilbert, N. (2014). How do agents make decisions? A survey. Journal of Artificial Societies and Social Simulation, 17(4), 13.

    Google Scholar 

  • Brandt, P., Ernst, A., Gralla, F., Luederitz, C., Lang, D., Newig, J., et al. (2013). A review of transdisciplinary research in sustainability science. Ecological Economics, 92, 1–15.

    Article  Google Scholar 

  • Brugnach, M., Pahl-Wostl, C., Lindenschmidt, K. E., Janssen, J. A. E. B., Filatova, T., Mouton, A., et al. (2008). Complexity and uncertainty: Rethinking the modelling activity. In A. J. Jakeman, A. A. Voinov, A. E. Rizzoli, & S. H. Chen (Eds.), Environmental modelling, software and decision support: State of the art and new perspectives (pp. 49–68). Amsterdam: Elsevier.

    Chapter  Google Scholar 

  • Brugnach, M., Dewulf, A., Henriksen, H. J., & van der Keur, P. (2011). More is not always better: Coping with ambiguity in natural resources management. Journal of Environmental Management, 92(1), 78–84. doi:10.1016/j.jenvman.2010.08.029.

    Article  CAS  Google Scholar 

  • Chaudhuri, A. (2011). Sustaining cooperation in laboratory public goods experiments: A selective survey of the literature. Experimental Economics, 14(1), 47–83. doi:10.1007/s10683-010-9257-1.

    Article  Google Scholar 

  • Dewulf, A., Craps, M., Bouwen, R., Taillieu, T., & Pahl-Wostl, C. (2005). Integrated management of natural resources: Dealing with ambiguous issues, multiple actors and diverging frames. Water Science and Technology, 52(6), 115–124.

    CAS  Google Scholar 

  • Ebenhöh, E. (2006). Modelling human behaviour in social dilemmas using attributes and heuristics. Osnabrück: Osnabrück University.

    Google Scholar 

  • Ebenhöh, E., & Pahl-Wostl, C. (2008). Agent behavior between maximization and cooperation. Rationality and Society, 20(2), 227–252. doi:10.1177/1043463108089546.

    Article  Google Scholar 

  • Engel, C. (2011). Dictator games: A meta study. Experimental Economics, 14(4), 583–610. doi:10.1007/s10683-011-9283-7.

    Article  Google Scholar 

  • Epstein, J. M. (2008). Why Model?. Journal of Artificial Societies and Social Simulation, 11(4), 12 http://jasss.soc.surrey.ac.uk/11/14/12.html.

  • Etienne, M. (Ed.). (2013). Companion modelling: A participatory approach to support sustainable development. Netherlands: Springer.

    Google Scholar 

  • European Commission (2009). Living Labs for user-driven open innovation. Luxembourg: European Commission, DG Information, Society and Media.

    Google Scholar 

  • Fehr, E., & Gächter, S. (2000). Cooperation and punishment in public good experiments. The American Economic Review, 90(4), 980–994.

    Article  Google Scholar 

  • Fehr, E., & Gächter, S. (2002). Altruistic punishment in humans. Nature, 415, 137–140.

    Article  CAS  Google Scholar 

  • Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Peter, S., & Trow, M. (1994). The new production of knowledge. The dynamics of science and research in contemporary societies. London: Sage.

    Google Scholar 

  • Gigerenzer, G., & Selten, R. (Eds.). (2001). Bounded rationality: The adaptive toolbox. Cambridge, MA: MIT Press.

    Google Scholar 

  • Gilbert, N., & Troitzsch, K. (1999). Simulation for the social scientist. Berkshire: Open University Press.

    Google Scholar 

  • Higgins, A., & Klein, S. (2011). Introduction to the living lab approach. In Y. H. Tan, N. Björn-Andersen, S. Klein, & B. Rukanova (Eds.), Accelerating global supply chains with IT-innovation (pp. 31–36). Berlin: Springer.

    Chapter  Google Scholar 

  • Hulme, M. (2009). Why we disagree about climate change. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Jager, W. (2000). Modelleing consumer behaviour. Groningen: University of Groningen.

    Google Scholar 

  • Jager, W., & Janssen, M. (2003). The need for and development of behaviourally realistic agents. In J. Simão Sichman, F. Bousquet, & P. Davidsson (Eds.), Multi-agent-based simulation II (Vol. 2581, pp. 36–49). Lecture Notes in Computer Science Berlin: Springer.

    Chapter  Google Scholar 

  • Oosterbeek, H., Sloof, R., & van de Kuilen, G. (2004). Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics, 7(2), 171–188. doi:10.1023/B:EXEC.0000026978.14316.74.

    Article  Google Scholar 

  • Pahl-Wostl, C. (2002a). Participative and stakeholder-based policy design, evaluation and modeling processes. Integrated Assessment, 3(1), 3–14.

    Article  Google Scholar 

  • Pahl-Wostl, C. (2002b). Towards sustainability in the water sector—The importance of human actors and processes of social learning. Aquatic Sciences, 64, 394–411.

    Article  Google Scholar 

  • Pahl-Wostl, C. (2009). A conceptual framework for analysing adaptive capacity and multi-level learning processes in resource governance regimes. Global Environmental Change, 19, 354–365.

    Article  Google Scholar 

  • Pahl-Wostl, C., & Ebenhöh, E. (2004). An adaptive toolbox model: A pluralistic modelling approach for human behaviour based on observation. Journal of Artificial Societies and Social Simulation, 7(1), http://jasss.soc.surrey.ac.uk/7/1/3.html.

  • Pahl-Wostl, C., & Hare, M. (2004). Processes of social learning in integrated resources management. Journal of Community and Applied Social Psychology, 14(3), 193–206. doi:10.1002/casp.774.

    Article  Google Scholar 

  • Pahl-Wostl, C., Craps, M., Dewulf, A., Mostert, E., Tabara, D., & Taillieu, T. (2007). Social learning and water resources management. Ecology and Society, 12(2), 5. [online] URL:http://www.ecologyandsociety.org/vol12/iss12/art15/.

  • Pahl-Wostl, C., Vörösmarty, C., Bhaduri, A., Bogardi, J., Rockström, J., & Alcamo, J. (2013). Towards a sustainable water future: shaping the next decade of global water research. Current Opinion in Environmental Sustainability, 5(6), 708–714. doi:10.1016/j.cosust.2013.10.012.

    Article  Google Scholar 

  • Rittel, H., & Webber, M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4, 155–169.

    Article  Google Scholar 

  • Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143–186.

    Article  Google Scholar 

  • Schneidewind, U., & Singer-Brodowski, M. (2013). Transformative Wissenschaft—Klimawandel im deutschen Wissenschafts- und Hochschulsystem. Marburg: Metropolis.

    Google Scholar 

  • Schneidewind, U., & Singer-Brodowski, M. (2015). Vom experimentellen Lernen zum transformativen Experimentieren—Reallabore als Katalysator für eine lernende Gesellschaft auf dem Weg zu einer Nachhaltigen Entwicklung. Zeitschrift für Wirtschafts- und Unternehmensethik, 16(1).

    Google Scholar 

  • Scholz, G. (2014). How participatory methods facilitate social learning in natural resources management. Osnabrück: Osnabrück University.

    Google Scholar 

  • Scholz, G., Dewulf, A., & Pahl-Wostl, C. (2013). An analytical framework of social learning facilitated by participatory methods. Systemic Practice and Action Research, 1–17, doi:10.1007/s11213-013-9310-z.

  • Scholz, G., Pahl-Wostl, C., & Dewulf, A. (2014). An agent-based model of consensus building. In Social Simulation Conference, Barcelona, 1–5(09), 2014.

    Google Scholar 

  • Scholz, G., Austermann, M., Kaldrack, K., & Pahl-Wostl, C. (2015a). A method to evaluate Group Model Building sessions by comparing externalized mental models and group models. System Dynamics Review, in press.

    Google Scholar 

  • Scholz, G., Dewulf, A., & Pahl-Wostl, C. (2015b). Social learning in an agent based model: Using cognitive biases to simulate learning and consensus finding in group discussions. Journal of Artificial Societies and Social Simulation.

    Google Scholar 

  • Simon, H. (1982). Models of bounded rationality. Cambridge, MA, USA: MIT Press.

    Google Scholar 

  • Sol, J., Beers, P. J., & Wals, A. E. J. (2013). Social learning in regional innovation networks: Trust, commitment and reframing as emergent properties of interaction. Journal of Cleaner Production, 49, 35–43. doi:10.1016/j.jclepro.2012.07.041.

    Article  Google Scholar 

  • Sterman, J. D. (2000). Business dynamics: System thinking and modeling for a complex world. United States: McGraw-Hill.

    Google Scholar 

  • van den Belt, M. (2004). Mediated modeling. A system dynamics approach to environmental consensus building. Washington: Island Press.

    Google Scholar 

  • Vennix, J. A. M. (1996). Group model-building: Facilitating team learning using system dynamics. Chichester: Wiley.

    Google Scholar 

  • Vennix, J. A. M. (1999). Group model-building: Tackling messy problems. System Dynamics Review, 15(4), 379–401.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Environmental Systems Research, University of Osnabrück, Osnabrück, Germany

    Claudia Pahl-Wostl

Authors
  1. Claudia Pahl-Wostl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claudia Pahl-Wostl .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Pahl-Wostl, C. (2015). Virtual and Real World Experimentation. In: Water Governance in the Face of Global Change. Water Governance - Concepts, Methods, and Practice. Springer, Cham. https://doi.org/10.1007/978-3-319-21855-7_11

Download citation

Publish with us

Policies and ethics

Search

Navigation