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Science to inform and models to engage

Chapter


Abstract


  • Scientific evidence and evidence-based reasoning are likely to face epistemological

    challenges when brought into societal debate if their foundational assumptions

    generate cognitive dissonance among key elements of the community. The risk of

    dissonance is even greater when scientific demonstrations and models are concerned

    with the decisions and behaviours of people interacting with an environment of

    interest. In this case, scientific information is often perceived as distorted or biased

    due to the inherent uncertainties attached to human ecosystems

    Human ecosystems are complex and adaptive, largely due to our individual cognitive

    capacities and communication skills. Complex systems science aims to track

    uncertainties attached to these systems by exploring metaphoric models of reality.

Authors


  •   Alford, Kristin (external author)
  •   Manderson, Lenore (external author)
  •   Boschetti, Fabio (external author)
  •   Davies, Jocelyn (external author)
  •   Hatfield Dodds, Steve (external author)
  •   Lowe, Ian (external author)
  •   Perez, Pascal

Publication Date


  • 2012

Citation


  • Alford, K., Manderson, L., Boschetti, F., Davies, J., Hatfield Dodds, S., Lowe, I. & Perez, P. (2012). Science to inform and models to engage. In M. Raupach, A. J. Mcmichael, J. Finnigan, L. Manderson & B. Walker (Eds.), Negotiating our future: living scenarios for Australia to 2050 (Volume 2) (pp. 147-160). Australia: Australian Academy of Science.

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/115

Book Title


  • Negotiating our future: living scenarios for Australia to 2050 (Volume 2)

Start Page


  • 147

End Page


  • 160

Abstract


  • Scientific evidence and evidence-based reasoning are likely to face epistemological

    challenges when brought into societal debate if their foundational assumptions

    generate cognitive dissonance among key elements of the community. The risk of

    dissonance is even greater when scientific demonstrations and models are concerned

    with the decisions and behaviours of people interacting with an environment of

    interest. In this case, scientific information is often perceived as distorted or biased

    due to the inherent uncertainties attached to human ecosystems

    Human ecosystems are complex and adaptive, largely due to our individual cognitive

    capacities and communication skills. Complex systems science aims to track

    uncertainties attached to these systems by exploring metaphoric models of reality.

Authors


  •   Alford, Kristin (external author)
  •   Manderson, Lenore (external author)
  •   Boschetti, Fabio (external author)
  •   Davies, Jocelyn (external author)
  •   Hatfield Dodds, Steve (external author)
  •   Lowe, Ian (external author)
  •   Perez, Pascal

Publication Date


  • 2012

Citation


  • Alford, K., Manderson, L., Boschetti, F., Davies, J., Hatfield Dodds, S., Lowe, I. & Perez, P. (2012). Science to inform and models to engage. In M. Raupach, A. J. Mcmichael, J. Finnigan, L. Manderson & B. Walker (Eds.), Negotiating our future: living scenarios for Australia to 2050 (Volume 2) (pp. 147-160). Australia: Australian Academy of Science.

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/115

Book Title


  • Negotiating our future: living scenarios for Australia to 2050 (Volume 2)

Start Page


  • 147

End Page


  • 160