Science Advice under Deep Uncertainty

Authors

  • Gabriel Gray Author

Keywords:

science advice, deep uncertainty, public policy, evidence, adaptive decisions

Abstract

This review examines science advice under deep uncertainty. The organizing question is how advisers can communicate scenarios, disagreement, value judgments, and adaptive options without manufacturing certainty. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to compressing contested evidence into a single forecast detached from decision values. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in evidence-informed public policy.

References

  1. Beek, L. V., Hajer, M., Pelzer, P., Vuuren, D. V., & Cassen, C. (2020). Anticipating futures through models: the rise of Integrated Assessment Modelling in the climate science-policy interface since 1970. Global Environmental Change, 65, 102191. https://doi.org/10.1016/j.gloenvcha.2020.102191
  2. Birch, J. (2021). Science and policy in extremis: the UK’s initial response to COVID-19. European Journal for Philosophy of Science, 11(3), 90. https://doi.org/10.1007/s13194-021-00407-z
  3. Cairney, P., & Weible, C. M. (2017). The new policy sciences: combining the cognitive science of choice, multiple theories of context, and basic and applied analysis. Policy Sciences, 50(4), 619-627. https://doi.org/10.1007/s11077-017-9304-2
  4. Chindarkar, N., & Grafton, R. Q. (2019). India's depleting groundwater: When science meets policy. Asia & the Pacific Policy Studies, 6(1), 108-124. https://doi.org/10.1002/app5.269
  5. Grennfelt, P., Engleryd, A., Forsius, M., Hov, Ø., Rodhe, H., & Cowling, E. (2019). Acid rain and air pollution: 50 years of progress in environmental science and policy. AMBIO, 49(4), 849-864. https://doi.org/10.1007/s13280-019-01244-4
  6. Hetland, P. (2016). Models in Science Communication Policy. Nordic Journal of Science and Technology Studies, 2(2), 5-17. https://doi.org/10.5324/njsts.v2i2.2144
  7. Moomaw, W. R., Chmura, G. L., Davies, G. T., Finlayson, C. M., Middleton, B. A., Natali, S. M., Perry, J. E., Roulet, N., & Sutton-Grier, A. E. (2018). Wetlands In a Changing Climate: Science, Policy and Management. Wetlands, 38(2), 183-205. https://doi.org/10.1007/s13157-018-1023-8
  8. Pearce, W., Mahony, M., & Raman, S. (2017). Science advice for global challenges: Learning from trade-offs in the IPCC. Environmental Science & Policy, 80, 125-131. https://doi.org/10.1016/j.envsci.2017.11.017
  9. Saltelli, A., & Giampietro, M. (2017). What is wrong with evidence based policy, and how can it be improved?. Futures, 91, 62-71. https://doi.org/10.1016/j.futures.2016.11.012
  10. Schleussner, C. F., Rogelj, J., Schaeffer, M., Lissner, T., Licker, R., Fischer, E. M., Knutti, R., Levermann, A., Frieler, K., & Hare, W. (2016). Science and policy characteristics of the Paris Agreement temperature goal. Nature Climate Change, 6(9), 827-835. https://doi.org/10.1038/nclimate3096

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Published

2026-08-08

Issue

Vol. 1 (2026): Archive

Section

Review

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Copyright (c) 2026 Convergence in Science and Society

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