Advertisement

Climatic Change

, Volume 89, Issue 1–2, pp 155–172 | Cite as

Negative learning

  • Michael Oppenheimer
  • Brian C. O’Neill
  • Mort Webster
Open Access
Article
First Online:

Abstract

New technical information may lead to scientific beliefs that diverge over time from the a posteriori right answer. We call this phenomenon, which is particularly problematic in the global change arena, negative learning. Negative learning may have affected policy in important cases, including stratospheric ozone depletion, dynamics of the West Antarctic ice sheet, and population and energy projections. We simulate negative learning in the context of climate change with a formal model that embeds the concept within the Bayesian framework, illustrating that it may lead to errant decisions and large welfare losses to society. Based on these cases, we suggest approaches to scientific assessment and decision making that could mitigate the problem. Application of the tools of science history to the study of learning in global change, including critical examination of the assessment process to understand how judgments are made, could provide important insights on how to improve the flow of information to policy makers.

Keywords

Ozone Climate Sensitivity Total Fertility Rate Ozone Depletion Expert Elicitation 
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.
Download to read the full article text

Supplementary material

10584_2008_9405_MOESM1_ESM.doc (2 mb)
ESM 1 (DOC 1.95 MB)

References

  1. Abbott A (2005) Gut feeling secures medical Nobel for Australian doctors. Nature 437:801CrossRefGoogle Scholar
  2. Alley RB, Clark PU, Huybrechts P, Joughin I (2005) Ice-sheet and sea-level changes. Science 310:456–460 DOI  10.1126/science.1114613 CrossRefGoogle Scholar
  3. Andronova NG, Schlesinger ME (2001) Objective estimation of the probability density function for climate sensitivity. J Geophys Res 106(D19):22,605–22,611CrossRefGoogle Scholar
  4. Arrow KJ, Fisher AC (1974) Environmental protection, uncertainty, and irreversibility. Q J Econ 88:312–319CrossRefGoogle Scholar
  5. Benedick R (1998) Ozone diplomacy: new directions in safeguarding the planet. Harvard Univ Press, Cambridge, MAGoogle Scholar
  6. Bongaarts J, Bulatao RA (eds) (2000) Beyond six billion: forecasting the world’s population. National Academy Press, Washington, DCGoogle Scholar
  7. Christie M (2001) Ozone layer: a philosophy of science perspective. Cambridge Univ Press, Cambridge, UK, New YorkGoogle Scholar
  8. Craig PP, Gadgil A, Koomey JG (2002) What can history teach us? A retrospective examination of long-term energy forecasts for the United States. Annu Rev Energy Environ 27:83–118CrossRefGoogle Scholar
  9. Crutzen P, Oppenheimer M (2008) Learning about ozone depletion. Clim Change (this issue). DOI  10.1007/s10584-008-9400-6
  10. Draper D (1995) Assessment and propagation of model uncertainty. J R Stat Soc Ser B Stat Methodol 57:45–97Google Scholar
  11. Frederick S, Loewenstein G, O’Donoghue T (2002) Time discounting and time preference: a critical review. J Econ Lit XL:351–401CrossRefGoogle Scholar
  12. Funtowicz SO, Ravetz JR (1992) The emergence of postnormal science. In: von Schomberg R (ed) Science, politics, and morality. Kluwer Academic, Dordrecht, pp 85–123Google Scholar
  13. Füssel H-M (2007) Methodological and empirical flaws in the design and application of simple climate-economy models. Clim Change 81:161–185CrossRefGoogle Scholar
  14. Ha-Duong M, Mégie G, Hauglustaine D (2003) A pro-active stratospheric ozone protection scenario. Global Environ Change 13:43–49CrossRefGoogle Scholar
  15. Hall J, Fu G, Lawry J (2007) Imprecise probabilities of climate change: aggregation of fuzzy scenarios and model uncertainties. Clim Change 81:265–281CrossRefGoogle Scholar
  16. Hansen JE (2005) Slippery slope: How much global warming constitutes “dangerous anthropogenic interference”? Clim Change 68:269–279CrossRefGoogle Scholar
  17. Henrion M, Fischhoff B (1986) Assessing uncertainty in physical constants. Am J Phys 54:791–797CrossRefGoogle Scholar
  18. Huntington HG (1994) Oil price forecasting in the 1980s: what went wrong? Energy J 15:1–22Google Scholar
  19. Jones RA, Ostroy JM (1984) Flexibility and uncertainty. Revs Econ Studies L1:13–32CrossRefGoogle Scholar
  20. Joos F, Müller-Fürstenberger G, Stephan G (1999) Correcting the carbon cycle representation: how important is it for the economics of climate change? Environ Model Assess 4:133–140CrossRefGoogle Scholar
  21. Kaufmann R (1997) Assessing the dice model: uncertainty associated with the emission and retention of greenhouse gases. Clim Change 35:435–448CrossRefGoogle Scholar
  22. Keilman N (1999) How accurate are United Nations population forecasts? In: Lutz W, Vaupel JW, Ahlburg DA (eds) Issue supplement: frontiers of population forecasting, Pop Develop Rev 24:15–41Google Scholar
  23. Keller K, McInerney D (2007) The dynamics of learning about a climate threshold. Clim Dyn 30:321–332 DOI  10.1007/s00382–007–0290–5 Google Scholar
  24. Keller K, Bolker BM, Bradford DF (2004) Uncertain climate thresholds and optimal economic growth. J Environ Econ Manage 48:723–741CrossRefGoogle Scholar
  25. Kelly DL, Kolstad CD (1999) Bayesian learning, growth, and pollution. J Econ Dyn Control 23:491–518CrossRefGoogle Scholar
  26. Kitcher P (1993) The advancement of science: science without legends, objectivity without illusions. Oxford Univ Press, New YorkGoogle Scholar
  27. Kolstad CD (1996) Learning and stock effects in environmental regulation: the case of greenhouse gas emissions. J Environ Econ Manage 31:1–18CrossRefGoogle Scholar
  28. Kuhn TS (1962) The structure of scientific revolutions. Univ of Chicago Press, ChicagoGoogle Scholar
  29. Lange A (2003) Climate change and the irreversibility effect – combining expected utility and maximin. Environ Res Econ 25:417–434CrossRefGoogle Scholar
  30. Lee R, Tuljapurkari S, Lee RD, Auerback AJ (eds) (2000) Demographic change and fiscal policy. Cambridge Univ Press, Cambridge, UKGoogle Scholar
  31. Lempert RJ, Schlesinger ME, Bankes SC (1996) When we don’t know the costs or the benefits: adaptive strategies for abating climate change. Clim Change 33:235–274CrossRefGoogle Scholar
  32. Lempert RJ, Popper SW, Bankes SC (2003) Shaping the next one hundred years: new methods for quantitative, long-term policy analysis. Rand Corporation, Santa MonicaGoogle Scholar
  33. Lenton TM et al (2008) Tipping elements in the earth’s climate system. PNAS 105:1786–1793CrossRefGoogle Scholar
  34. Little C et al (2007) Toward a new generation of ice sheet models. Eos 88:578–579CrossRefGoogle Scholar
  35. Lutz W, Sanderson W, Scherbov S (2001) The end of world population growth. Nature 412:543–545CrossRefGoogle Scholar
  36. MacAyeal DR (1992) Irregular oscillations of the West Antarctic ice sheet. Nature 359:29–32CrossRefGoogle Scholar
  37. Morgan MG, Henrion M (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge Univ Press, Cambridge, New YorkGoogle Scholar
  38. Morgan MG, Kandlikar M, Risbey J, Dowlatabadi H (1999) Why conventional tools for policy analysis are often inadequate for problems of global change. Clim Change 41:271–281CrossRefGoogle Scholar
  39. Moss RH, Schneider S (2000). In: Pachauri R, Taniguchi T, Tanaka K (eds) Guidance papers on the cross cutting issues of the Third Assessment Report of the IPCC. IPCC, GenevaGoogle Scholar
  40. Nordhaus WD, Boyer J (2000) Warming the world: economic models of global warming. MIT Press, Cambridge, MassGoogle Scholar
  41. NRC (1984) Causes and effects of changes in stratospheric ozone: update 1983. National Academy Press, Washington, DCGoogle Scholar
  42. O’Neill BC, Scherbov S, Lutz W (1999) The long-term effect of the timing of fertility decline on population size. Pop Develop Rev 25:749–756CrossRefGoogle Scholar
  43. Oppenheimer M (1998) Global warming and the stability of the West Antarctic ice sheet. Nature 393:325–332CrossRefGoogle Scholar
  44. Oppenheimer M, Alley RB (2004) The West Antarctic ice sheet and long term climate policy. Clim Change 64:1–10CrossRefGoogle Scholar
  45. Oppenheimer M, O’Neill BC, Webster M, Agrawala S (2007) The limits of consensus. Science 317:1505–1506CrossRefGoogle Scholar
  46. Oreskes N, Shrader-Frechette K, Belitz K (1994) Verification, validation, and confirmation of numerical models in the earth sciences. Science 263:641–646CrossRefGoogle Scholar
  47. Overpeck JT, Otto-Bliesner BL, Miller GH et al (2006) Paleoclimatic evidence for future ice-sheet instability and rapid sea-level rise. Science 311:1747–1750CrossRefGoogle Scholar
  48. Parson E (2003) Protecting the ozone layer: science and strategy. Oxford Univ Press, Oxford, New YorkGoogle Scholar
  49. Patt AG (1999) Extreme outcomes: the strategic treatment of low probability events in scientific assessments. Risk Decis Policy 4:1–15CrossRefGoogle Scholar
  50. Patt AG (2007) Assessing model-based and conflict-based uncertainty. Glob Environ Change 17:37–46CrossRefGoogle Scholar
  51. Pielke RA Jr (2001) Room for doubt. Nature 410:151CrossRefGoogle Scholar
  52. Popp D (2004) ENTICE: endogenous technological change in the DICE model of global warming. J Environ Econ Manage 48:742–768CrossRefGoogle Scholar
  53. Rahmstorf S (2007) A semi-empirical approach to projecting future sea-level rise. Science 315:368–370CrossRefGoogle Scholar
  54. Rahmstorf S, Cazenave A, Church JA, Hansen JE, Keeling RF, Parker DE, Somerville RCJ (2007) Recent climate observations compared to projections. Science 316:709CrossRefGoogle Scholar
  55. Rowland FS (1989) Chlorofluorocarbons and the depletion of stratospheric ozone. Am Sci 77:36–45Google Scholar
  56. Schelling TC (1994) Intergenerational discounting. Energy Policy 23:395–402CrossRefGoogle Scholar
  57. Schneider SH, Turner BL, Garriga HM (1998) Imaginable surprise in global change science. J Risk Res 1:165–185CrossRefGoogle Scholar
  58. Shlyakhter AI (1994) An improved framework for uncertainty analysis: accounting for unsuspected errors. Risk Anal 14:441–447CrossRefGoogle Scholar
  59. Small MJ, Fischbeck PS (1999) False precision in Bayesian updating with incomplete models. J Hum Ecol Risk Assess 5(2):291–304CrossRefGoogle Scholar
  60. Somerville R, Le Treut H, Cubasch U, Ding Y, Mauritzen C, Mokssit A, Peterson T, Prather M (2007) Historical overview of climate change. In: Solomon S, Qin D et al (eds) Climate change 2007: the physical science basis. Contribution of working group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ Press, Cambridge, UK, New YorkGoogle Scholar
  61. Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185:1124–1131CrossRefGoogle Scholar
  62. UN Department of Economic and Social Affairs, Population Division (2005) World population prospects: the 2004 revision. United Nations, New YorkGoogle Scholar
  63. van de Kaa DJ (1987) Europe’s second demographic transition. Population Bulletin 42(1), The Population Reference Bureau, Washington, DCGoogle Scholar
  64. van Vuuren D, O’Neill BC (2006) The consistency of IPCC’s SRES scenarios to 1990–2000 trends and recent projections. Clim Change 75:9–46CrossRefGoogle Scholar
  65. Vaughan DG (2008) West Antarctic ice sheet collapse − the fall and rise of a paradigm. Clim Change, in pressGoogle Scholar
  66. Vaughan DG, Spouge JR (2002) Risk estimation of collapse of the West Antarctic ice sheet. Clim Change 52:65–91CrossRefGoogle Scholar
  67. Webster MD (2002) The curious role of learning: should we wait for more data? Energy J 23:97–119Google Scholar
  68. Webster MD, Jakobovits L, Norton J (2008) Learning about climate change and implications for near-term policy. Clim Change (this issue). DOI  10.1007/s10584-008-9406-0
  69. WMO (1986) Atmospheric ozone 1985: assessment of our understanding of the processes controlling its present distribution and change. Rep No 16, WMO, GenevaGoogle Scholar
  70. WMO (1988) Report of the international ozone trends panel-1988. Global Ozone Research and Monitoring Project, Rep No18, WMO, GenevaGoogle Scholar
  71. WMO (1991) Scientific assessment of stratospheric ozone 1991. Rep No 25, WMO, GenevaGoogle Scholar
  72. WMO (2006) Scientific assessment of ozone depletion: 2006, executive summary. WMO and UN Environment Program, GenevaGoogle Scholar
  73. Yohe G, Andronova NG, Schlesinger ME (2004) To hedge or not against an uncertain climate future? Science 306:416–417CrossRefGoogle Scholar

Copyright information

© The Author(s) 2008

Authors and Affiliations

  • Michael Oppenheimer
    • 1
    • 2
  • Brian C. O’Neill
    • 3
    • 4
  • Mort Webster
    • 5
  1. 1.Woodrow Wilson School of Public and International AffairsPrinceton UniversityPrincetonUSA
  2. 2.Department of GeosciencesPrinceton UniversityPrincetonUSA
  3. 3.International Institute for Applied Systems AnalysisLaxenburgAustria
  4. 4.Institute for the Study of Society and EnvironmentNational Center for Atmospheric ResearchBoulderUSA
  5. 5.MIT Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations