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Sustainability Science

, Volume 12, Issue 1, pp 45–64 | Cite as

Testing the consistency between goals and policies for sustainable development: mental models of how the world works today are inconsistent with mental models of how the world will work in the future

  • Claire RichertEmail author
  • Fabio Boschetti
  • Iain Walker
  • Jennifer Price
  • Nicola Grigg
Original Article
Part of the following topical collections:
  1. Climate Change Mitigation, Adaption, and Resilience

Abstract

Understanding complex problems such as climate change is difficult for most non‐scientists, with serious implications for decision making and policy support. Scientists generate complex computational models of climate systems to describe and understand those systems and to predict the future states of the systems. Non-scientists generate mental models of climate systems, perhaps with the same aims and perhaps with other aims too. Often, the predictions of computational models and of mental models do not correspond with important implications for human decision making, policy support, and behaviour change. Recent research has suggested non-scientists’ poor appreciation of the simple foundations of system dynamics is at the root of the lack of correspondence between computational and mental models. We report here a study that uses a simple computational model to ‘run’ mental models to assess whether a system will evolve according to our aspirations when considering policy choices. We provide novel evidence of a dual-process model: how we believe the system works today is a function of ideology and worldviews; how we believe the system will look in the future is related to other, more general, expectations about the future. The mismatch between these different aspects of cognition may prevent establishing a coherent link between a mental model’s assumptions and consequences, between the present and the future, thus potentially limiting decision making, policy support, and other behaviour changes.

Keywords

Mental models Climate change Beliefs about the future 

Notes

Acknowledgments

The research reported in this paper was supported by funds from CSIRO’s Climate Adaptation Flagship.

References

  1. Bain PG, Hornsey MJ, Bongiorno R, Jeffries C (2012) Promoting pro-environmental action in climate change deniers. Nat Clim Change 2(8):600–603CrossRefGoogle Scholar
  2. Boschetti F (2012a) Causality, emergence, computation and unreasonable expectations. Synthese 185(2):187–194CrossRefGoogle Scholar
  3. Boschetti F (2012b) A computational model of a mental model used to reason about climate change. Environ Sci Policy 15(1):125–135CrossRefGoogle Scholar
  4. Boschetti F, de La Tour A, Fulton E, Little R (2010) Interactive modelling for natural resource management. Environ Model Softw 25(10):1075–1085CrossRefGoogle Scholar
  5. Boschetti F, Richert C, Walker I, Price J, Dutra L (2012) Assessing attitudes and cognitive styles of stakeholders in environmental projects involving computer modelling. Ecol Model 247:98–111CrossRefGoogle Scholar
  6. Boschetti F, Fulton E, Grigg N (2014) Citizens’ views of Australia’s future to 2050. Sustainability 7(1):222–247CrossRefGoogle Scholar
  7. Boschetti F, Walker I, Price J (2016) Modelling and attitudes towards the Future. Ecol Model 322:71–81CrossRefGoogle Scholar
  8. Boyd JN, Zimbardo PG (1997) Constructing time after death—the transcendental-future time perspective. Time Soc 6(1):35–54CrossRefGoogle Scholar
  9. Cohen J (1996) How many people can the earth support?. Norton & Company, New YorkGoogle Scholar
  10. Costanza R (2000) Social goals and the valuation of ecosystem services. Ecosystems 3(1):4–10CrossRefGoogle Scholar
  11. Craik KJW (1943) The nature of explanation. University Press, Cambridge EngGoogle Scholar
  12. Cronin MA, Gonzalez C, Sterman JD (2009) Why don’t well-educated adults understand accumulation? A challenge to researchers, educators, and citizens. Organ Behav Hum Decis Process 108(1):116–130CrossRefGoogle Scholar
  13. Crutchfield JP (1994) The calculi of emergence: computation, dynamics, and induction. Physica D 75:11–54CrossRefGoogle Scholar
  14. D’Argembeau A (2012) Autobiographical memory and future thinking. Understanding autobiographical memory: theories and approaches. Berntsen D, Rubin DC (eds). Cambridge, Cambridge University PressGoogle Scholar
  15. D’Argembeau A, Van der Linden M (2007) “Emotional aspects of mental time travel.” Behav Brain Sci 30(3):320Google Scholar
  16. D’Argembeau A, Lardi C, Van der Linden M (2012) Self-defining future projections: exploring the identity function of thinking about the future. Memory 20(2):110–120CrossRefGoogle Scholar
  17. Dessai S, Adger WN, Hulme M, Turnpenny J, Köhler J, Warren R (2004) Defining and experiencing dangerous climate change. Clim Change 64(1–2):11–25CrossRefGoogle Scholar
  18. Dorner D (1996) The logic of failure: recognizing and avoiding error in complex situations. Metropolitan Books, Ney YorkGoogle Scholar
  19. Dunlap RE, Van Liere KD, Mertig AG, Jones RE (2000) Measuring endorsement of the new ecological paradigm: a revised NEP scale. J Soc Issues 56(3):425–442CrossRefGoogle Scholar
  20. Eagly AH, Chaiken S (1993) The psychology of attitudes, Harcourt Brace Jovanovich College PublishersGoogle Scholar
  21. Ehrlich I, Lui F (1997) The problem of population and growth: a review of the literature from Malthus to contemporary models of endogenous population and endogenous growth. J Econ Dyn Control 21(1):205–242CrossRefGoogle Scholar
  22. Feygina I, Jost JT, Goldsmith RE (2010) System justification, the denial of global warming, and the possibility of “system-sanctioned change”. Pers Soc Psychol Bull 36(3):326–338CrossRefGoogle Scholar
  23. Gagnon Thompson SC, Barton MA (1994) Ecocentric and anthropocentric attitudes toward the environment. J Environ Psychol 14(2):149–157CrossRefGoogle Scholar
  24. Gerardi K, Goette L, Meier S (2013) Numerical ability predicts mortgage default. Proc Natl Acad Sci USA 110(28):11267–11271CrossRefGoogle Scholar
  25. Greca IM, Moreira MA (2000) Mental models, conceptual models, and modelling. Int J Sci Educ 22(1):1–11CrossRefGoogle Scholar
  26. Grigg NJ, Boschetti F, Brede M, Finnigan JJ (2011) A probabilistic approach to exploring low-dimensional global dynamics. Procedia Environ Sci 6:122–135CrossRefGoogle Scholar
  27. Groesser SN, Schaffernicht M (2012) Mental models of dynamic systems: taking stock and looking ahead. Syst Dyn Rev 28(1):46–68CrossRefGoogle Scholar
  28. Guy S, Kashima Y, Walker I, O’Neill S (2014) Investigating the effects of knowledge and ideology on climate change beliefs. Eur J Soc Psychol 44(5):421–429CrossRefGoogle Scholar
  29. Halford GS, Baker R, McCredden JE, Bain JD (2005) How many variables can humans process? Psychol Sci 16(1):70–76CrossRefGoogle Scholar
  30. Halford GS, Cowan N, Andrews G (2007) Separating cognitive capacity from knowledge: a new hypothesis. Trends Cognitive Sci 11(6):236–242CrossRefGoogle Scholar
  31. Heath Y, Gifford R (2006) Free-market ideology and environmental degradation. Environ Behav 38(1):48–71CrossRefGoogle Scholar
  32. Heaven PCL, Quintin DS (2003) Personality factors predict racial prejudice. Personality Individ Differ 34(4):625–634CrossRefGoogle Scholar
  33. Hohwy J (2013) The predictive mind, Oxford University PressGoogle Scholar
  34. Inayatullah S (2004) Causal Layered Analysis: Theory, historical context, and case studies. The causal layered analysis (CLA) Reader: theory and case studies of an integrative and transformative methodology. Inayatullah S. Taipei, Taiwan, Tamkang University Press: 8–49Google Scholar
  35. Johnson-Laird PN (1983) Mental models: towards a cognitive science of language, inference and consciousness. Cambridge University Press, Cambridge Cambridgeshire; New YorkGoogle Scholar
  36. Johnson-Laird PN (2001) Mental models and deduction. Trends in Cognitive Sciences 5(10):434–442CrossRefGoogle Scholar
  37. Johnson-Laird PN (2013) Mental models and cognitive change. J Cognitive Psychol 25(2):131–138CrossRefGoogle Scholar
  38. Johnson-Laird PN, Khemlani SS, Goodwin GP (2015) Logic, probability, and human reasoning. Trends Cogn Sci 19(4):201–214CrossRefGoogle Scholar
  39. Jones, N. A., H. Ross, T. Lynam, P. Perez and A. Leitch (2011). “Mental Models: An Interdisciplinary Synthesis of Theory and Methods.” Ecology and Society. 16(1):46. [online] URL: http://wwwecologyandsociety.org/vol16/iss1/art46/. Accessed 2015
  40. Jost JT, Amodio DM (2012) Political ideology as motivated social cognition: behavioral and neuroscientific evidence. Motivation and Emotion 36(1):55–64CrossRefGoogle Scholar
  41. Jost JT, Glaser J, Kruglanski AW, Sulloway FJ (2003) Political conservatism as motivated social cognition. Psychol Bull 129(3):339CrossRefGoogle Scholar
  42. Kahan D (2013) Ideology, motivated reasoning, and cognitive reflection: an experimental study. Judgment Decision Making 8(4):407–424Google Scholar
  43. Kahan DM, Peters E, Wittlin M, Slovic P, Ouellette LL, Braman D, Mandel G (2012) The polarizing impact of science literacy and numeracy on perceived climate change risks. Nat Clim Change 2(10):732–735CrossRefGoogle Scholar
  44. Kahneman D (2011) Thinking, fast and slow. Farrar, Straus and Giroux, New YorkGoogle Scholar
  45. Kashima Y, Shi JQ, Tsuchiya K, Kashima ES, Cheng SYY, Manchi M, Shin SH (2011) Globalization and folk theory of social change: how globalization relates to societal perceptions about the past and future. J Soc Issues 67(4):696–715CrossRefGoogle Scholar
  46. Kunda Z (1990) The case for motivated reasoning. Psychol Bull 108(3):480–498CrossRefGoogle Scholar
  47. Leiserowitz A (2006) Climate change risk perception and policy preferences: the role of affect, imagery, and values. Clim Change 77(1–2):45–72CrossRefGoogle Scholar
  48. Leviston Z, Walker IA (2010) Baseline survey of australian attitudes to climate change: preliminary report. N. R. F. C. Adaptation, CSIRO Ecosystem Sciences, PerthGoogle Scholar
  49. Liberman N, Trope Y (1998) The role of feasibility and desirability considerations in near and distant future decisions: a test of temporal construal theory. J Pers Soc Psychol 75(1):5–18CrossRefGoogle Scholar
  50. Moxnes E (1998) Overexploitation of renewable resources: the role of misperceptions. J Econ Behav Organ 37(1):107–127CrossRefGoogle Scholar
  51. Moxnes E (2000) Not only the tragedy of the commons: misperceptions of feedback and policies for sustainable development. Syst Dyn Rev 16(4):325–348CrossRefGoogle Scholar
  52. Moxnes E, Saysel AK (2009) Misperceptions of global climate change: information policies. Clim Change 93(1–2):15–37CrossRefGoogle Scholar
  53. New M, Liverman D, Schroder H, Anderson K (2011) Four degrees and beyond: the potential for a global temperature increase of four degrees and its implications. Philosophical Trans Royal Soc A Math, Phys Eng Sci 369(1934):6–19CrossRefGoogle Scholar
  54. Norman DA (1987) Some observations on mental models. Human-computer interaction. In: Baecker RM, Buxton WAS (eds) Readings in human-computer interaction: a multidisciplinary approach. Morgan Kaufman Publishers, Inc, Los Altos, CA, pp 241–244Google Scholar
  55. Nowak A, Rychwalska A, Borkowski W (2013) Why simulate? To develop a mental model. J Artif Soc Soc Simul 16(3):12Google Scholar
  56. Pratto F, Çidam A, Stewart AL, Zeineddine FB, Aranda M, Aiello A, Chryssochoou X, Cichocka A, Cohrs JC, Durrheim K, Eicher V, Foels R, Górska P, Lee I-C, Licata L, Liu JH, Li L, Meyer I, Morselli D, Muldoon O, Muluk H, Papastamou S, Petrovic I, Petrovic N, Prodromitis G, Prati F, Rubini M, Saab R, Stekelenburg Jv, Sweetman J, Zheng W, Henkel KE (2013) Social Dominance in Context and in Individuals: Contextual Moderation of Robust Effects of Social Dominance Orientation in 15 Languages and 20 Countries. Soc Psychol Personal Sci 4:587–599CrossRefGoogle Scholar
  57. Price JC, Walker IA, Boschetti F (2014) Measuring cultural values and beliefs about environment to identify their role in climate change responses. J Environ Psychol 37:8–20CrossRefGoogle Scholar
  58. Quinn N (2005) How to reconstruct schemas people share, from what they say, CiteseerGoogle Scholar
  59. Raupach MR, Marland G, Ciais P, Le Quere C, Canadell JG, Klepper G, Field CB (2007) Global and regional drivers of accelerating CO2 emissions. Proc Natl Acad Sci USA 104(24):10288–10293CrossRefGoogle Scholar
  60. Raupach MR, Canadell JG, Ciais P, Friedlingstein P, Rayner PJ, Trudinger CM (2011) The relationship between peak warming and cumulative CO2 emissions, and its use to quantify vulnerabilities in the carbon–climate–human system. Tellus B: no-noGoogle Scholar
  61. Schwartz SH (1992) Universals in the content and structure of values: theoretical advances and empirical tests in 20 countries. Adv Exp Soc Psychol 25(1):1–65CrossRefGoogle Scholar
  62. Shalizi CR, Crutchfield JP (2001) Computational mechanics: pattern and prediction, structure and simplicity. J Stat Phys 104(3–4):817–879CrossRefGoogle Scholar
  63. Spreng RN, Levine B (2006) The temporal distribution of past and future autobiographical events across the lifespan. Memory Cognition 34(8):1644–1651CrossRefGoogle Scholar
  64. Stanovich K (1999) Who is rational? studies of individual differences in reasoning. Mahwah, Lawrence Erlbaum Associates, NJGoogle Scholar
  65. Stawarczyk D, Cassol H, D’Argembeau A (2013) Phenomenology of future-oriented mind-wandering episodes. Front Psychol 4:425CrossRefGoogle Scholar
  66. Sterman JD (2008) Risk communication on climate: mental models and mass balance. Science 322(5901):532–533CrossRefGoogle Scholar
  67. Sterman JD, Sweeney LB (2002) Cloudy skies: assessing public understanding of global warming. Syst Dyn Rev 18(2):207–240CrossRefGoogle Scholar
  68. Sterman JD, Sweeney LB (2007) Understanding public complacency about climate change: adults’mental models of climate change violate conservation of matter. Clim Change 80(3–4):213–238CrossRefGoogle Scholar
  69. Stern PC, Raimi KT (2015) Simple mental models for informing climate choices. Soc Res Int Quarterly 82:3Google Scholar
  70. Strathman A, Gleicher F, Boninger DS, Edwards CS (1994) The consideration of future consequences—weighing immediate and distant outcomes of behavior. J Pers Soc Psychol 66(4):742–752CrossRefGoogle Scholar
  71. Suddendorf, T. and M. C. Corballis (2007). “The evolution of foresight: What is mental time travel, and is it unique to humans?” Behavioral and Brain Sciences 30(3):299Google Scholar
  72. Sweeney LB, Sterman JD (2000) Bathtub dynamics: initial results of a systems thinking inventory. Syst Dynamics Rev 16(4):249–286CrossRefGoogle Scholar
  73. Tarantola A (1987) Inverse problem theory. Elsevier, AmsterdamGoogle Scholar
  74. Trope Y, Liberman N (2003) Temporal construal. Psychol Rev 110(3):403–421CrossRefGoogle Scholar
  75. Wiek A, Iwaniec D (2014) Quality criteria for visions and visioning in sustainability science. Sustain Sci 9(4):497–512CrossRefGoogle Scholar
  76. Wiek A, Withycombe L, Redman CL (2011) Key competencies in sustainability: a reference framework for academic program development. Sustain Sci 6(2):203–218CrossRefGoogle Scholar
  77. Zimbardo PG, Boyd JN (1999) Putting time in perspective: a valid, reliable individual-differences metric. J Pers Soc Psychol 77(6):1271–1288CrossRefGoogle Scholar

Copyright information

© Springer Japan 2016

Authors and Affiliations

  • Claire Richert
    • 1
    • 5
    Email author
  • Fabio Boschetti
    • 2
    • 3
  • Iain Walker
    • 2
    • 4
  • Jennifer Price
    • 2
  • Nicola Grigg
    • 2
  1. 1.IRSTEA UMR G-EAUMontpellierFrance
  2. 2.Commonwealth Scientific and Industrial OrganisationCanberraAustralia
  3. 3.School of Earth and Geographical SciencesThe University of Western AustraliaCrawleyAustralia
  4. 4.School of PsychologyThe University of Western AustraliaCrawleyAustralia
  5. 5.MontpellierFrance

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