pp 1–13 | Cite as

Structuring Decisions Under Deep Uncertainty

  • Casey HelgesonEmail author


Innovative research on decision making under ‘deep uncertainty’ is underway in applied fields such as engineering and operational research, largely outside the view of normative theorists grounded in decision theory. Applied methods and tools for decision support under deep uncertainty go beyond standard decision theory in the attention that they give to the structuring (also called framing) of decisions. Decision structuring is an important part of a broader philosophy of managing uncertainty in decision making, and normative decision theorists can both learn from, and contribute to, the growing deep uncertainty decision support literature.


Uncertainty Decision support Decision theory Structuring Framing 



This work was supported by the Arts and Humanities Research Council through the Managing Severe Uncertainty Project (AH/J006033/1), the Agence Nationale de la Recherche through Decision-Making & Belief Change Under Severe Uncertainty: A Confidence-Based Approach (DUSUCA) (ANR-14-CE29-0003-01), and the National Science Foundation through the Network for Sustainable Climate Risk Management (SCRiM) (GEO-1240507).

Compliance with Ethical Standards

Conflicts of interest

The author declares no conflicts of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. American Meteorological Society (2015) A policy statement of the American Meteorological Society: Climate services. Technical report, The AMS Council,
  2. Baron J (2008) Thinking and deciding, 4th edn. Cambridge University Press, CambridgeGoogle Scholar
  3. Betz G (2013) In defence of the value free ideal. Eur J Philos Sci 3(2):207–220CrossRefGoogle Scholar
  4. Bhave AG, Conway D, Dessai S, Stainforth DA (2016) Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world. Clim Risk Manag 14:1–10CrossRefGoogle Scholar
  5. Binmore K (2009) Rational decisions. Princeton University Press, PrincetonGoogle Scholar
  6. Binmore K (2015) A minimal extension of Bayesian decision theory. Theory Decis 80(3):341–362CrossRefGoogle Scholar
  7. Bradley R (2017) Decision theory with a human face. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  8. Bradley R, Drechsler M (2014) Types of uncertainty. Erkenntnis 79(6):1225–1248CrossRefGoogle Scholar
  9. Bradley R, Steele K (2015) Making climate decisions. Philos Compass 10(11):799–810CrossRefGoogle Scholar
  10. Brown C, Ghile Y, Laverty M, Li K (2012) Decision scaling: linking bottom-up vulnerability analysis with climate projections in the water sector. Water Resour Res. 48:011212Google Scholar
  11. Brown C, Wilby RL (2012) An alternate approach to assessing climate risks. Eos Trans Am Geophys Union 93(41):401–402CrossRefGoogle Scholar
  12. Bryant BP, Lempert RJ (2010) Thinking inside the box: a participatory, computer-assisted approach to scenario discovery. Technol Forecast Soc Change 77(1):34–49CrossRefGoogle Scholar
  13. Carlsen H, Dreborg KH, Wikman-Svahn P (2013) Tailor-made scenario planning for local adaptation to climate change. Mitig Adapt Strateg Global Change 18(8):1239–1255CrossRefGoogle Scholar
  14. Carlsen H, Eriksson EA, Dreborg KH, Johansson B, Bodin Ö (2016) Systematic exploration of scenario spaces. Foresight 18(1):59–75CrossRefGoogle Scholar
  15. Carlsen H, Lempert R, Wikman-Svahn P, Schweizer V (2016) Choosing small sets of policy-relevant scenarios by combining vulnerability and diversity approaches. Environ Model Softw 84:155–164CrossRefGoogle Scholar
  16. Chateauneuf A, Faro JH (2009) Ambiguity through confidence functions. J Math Econ 45(9):535–558CrossRefGoogle Scholar
  17. Clemen RT, Reilly T (2013) Making hard decisions with decision tools. Cengage Learning, MasonGoogle Scholar
  18. Dessai S, Hulme M (2004) Does climate adaptation policy need probabilities? Clim Policy 4(2):107–128CrossRefGoogle Scholar
  19. Dessai S, Hulme M, Lempert R, Pielke R (2009) Do we need better predictions to adapt to a changing climate? EOS Trans Am Geophys Union 90(13):111–112CrossRefGoogle Scholar
  20. Dessai S, Sluijs JP (2007) Uncertainty and climate change adaptation: a scoping study. Copernicus Institute for Sustainable Development and Innovation, Department of Science Technology and Society, UtrechtGoogle Scholar
  21. Douglas H (2009) Science, policy, and the value-free ideal. University of Pittsburgh Press, PittsburghCrossRefGoogle Scholar
  22. Douglas H (2016) Values in science. In: Humphreys P (ed) The Oxford handbook of philosophy of science. Oxford University Press, Oxford, pp 609–630Google Scholar
  23. Fishburn PC (1964) Decision and value theory. Wiley, New YorkGoogle Scholar
  24. Gabrel V, Murat C, Thiele A (2014) Recent advances in robust optimization: an overview. Eur J Oper Res 235(3):471–483CrossRefGoogle Scholar
  25. Galaabaatar T, Karni E (2012) Expected multi-utility representations. Math Soc Sci 64(3):242–246CrossRefGoogle Scholar
  26. Galaabaatar T, Karni E (2013) Subjective expected utility with incomplete preferences. Econometrica 81(1):255–284CrossRefGoogle Scholar
  27. Gärdenfors P, Sahlin N-E (1982) Unreliable probabilities, risk taking, and decision making. Synthese 53(3):361–386CrossRefGoogle Scholar
  28. Garner G, Keller K (2018) When tails wag the decision. manuscriptGoogle Scholar
  29. Ghirardato P, Maccheroni F, Marinacci M (2004) Differentiating ambiguity and ambiguity attitude. J Econ Theory 118(2):133–173CrossRefGoogle Scholar
  30. Gilboa I, Marinacci M (2013) Ambiguity and the Bayesian paradigm. In: Advances in economics and econometrics, Tenth World CongressGoogle Scholar
  31. Gilboa I, Schmeidler D (1989) Maxmin expected utility with non-unique prior. J Math Econ 18(2):141–153CrossRefGoogle Scholar
  32. Gilboa I, Schmeidler D (2001) A theory of case-based decisions. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  33. Groves DG, Lempert RJ (2007) A new analytic method for finding policy-relevant scenarios. Global Environ Change 17(1):73–85CrossRefGoogle Scholar
  34. Haasnoot M, Kwakkel JH, Walker WE, ter Maat J (2013) Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world. Global Environ Change 23(2):485–498CrossRefGoogle Scholar
  35. Haasnoot M, Middelkoop H, Offermans A, Van Beek E, Van Deursen WP (2012) Exploring pathways for sustainable water management in river deltas in a changing environment. Clim Change 115(3–4):795–819CrossRefGoogle Scholar
  36. Haasnoot M, Middelkoop H, Van Beek E, Van Deursen W (2011) A method to develop sustainable water management strategies for an uncertain future. Sustain Dev 19(6):369–381CrossRefGoogle Scholar
  37. Hadka D, Herman J, Reed P, Keller K (2015) An open source framework for many-objective robust decision making. Environ Model Softw 74:114–129CrossRefGoogle Scholar
  38. Hall JW, Lempert RJ, Keller K, Hackbarth A, Mijere C, McInerney DJ (2012) Robust climate policies under uncertainty: a comparison of robust decision making and info-gap methods. Risk Anal 32(10):1657–1672CrossRefGoogle Scholar
  39. Hansson S (2005) Decision theory: a brief introduction. Royal Institute of Technology, StockholmGoogle Scholar
  40. Hansson SO (1996) Decision making under great uncertainty. Philos Soc Sci 26(3):369–386CrossRefGoogle Scholar
  41. Herman JD, Reed PM, Zeff HB, Characklis GW (2015) How should robustness be defined for water systems planning under change? J Water Resour Plan Manag 141(10):04015012CrossRefGoogle Scholar
  42. Herman JD, Zeff HB, Lamontagne JR, Reed PM, Characklis GW (2016) Synthetic drought scenario generation to support bottom-up water supply vulnerability assessments. J Water Resour Plan Manag 142(11):04016050CrossRefGoogle Scholar
  43. Hewitt C, Mason S, Walland D (2012) The global framework for climate services. Nat Clim Change 2(12):831CrossRefGoogle Scholar
  44. Hill B (2013) Confidence and decision. Games Econ Behav 82:675–692CrossRefGoogle Scholar
  45. Hill B (2016) Incomplete preferences and confidence. J Math Econ 65:83–103CrossRefGoogle Scholar
  46. Joyce JM (1999) The foundations of causal decision theory. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  47. Karni E, Vierø M-L (2013) “Reverse Bayesianism”: a choice-based theory of growing awareness. Am Econ Rev 103(7):2790–2810CrossRefGoogle Scholar
  48. Karni E, Vierø M-L (2014) Awareness of unawareness: a theory of decision making in the face of ignorance. Technical report, Queen’s Economics Department Working PaperGoogle Scholar
  49. Kasprzyk JR, Nataraj S, Reed PM, Lempert RJ (2013) Many objective robust decision making for complex environmental systems undergoing change. Environ Model Softwe 42:55–71CrossRefGoogle Scholar
  50. Kasprzyk JR, Reed PM, Characklis GW, Kirsch BR (2012) Many-objective de novo water supply portfolio planning under deep uncertainty. Environ Model Softw 34:87–104CrossRefGoogle Scholar
  51. Klibanoff P, Marinacci M, Mukerji S (2005) A smooth model of decision making under ambiguity. Econometrica 73(6):1849–1892CrossRefGoogle Scholar
  52. Kwakkel J, Haasnoot M, Walker W (2012) Computer assisted dynamic adaptive policy design for sustainable water management in river deltas in a changing environment. International Environmental Modelling and Software Society, MannoGoogle Scholar
  53. Kwakkel JH, Haasnoot M, Walker WE (2015) Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world. Clim Change 132(3):373–386CrossRefGoogle Scholar
  54. Kwakkel JH, Walker WE, Marchau V (2010) Adaptive airport strategic planning. Eur J Trans Infrastruct Res 10(3):2010Google Scholar
  55. Lempert R (2013) Scenarios that illuminate vulnerabilities and robust responses. Clim Change 117(4):627–646CrossRefGoogle Scholar
  56. Lempert R, Nakicenovic N, Sarewitz D, Schlesinger M (2004) Characterizing climate-change uncertainties for decision-makers. Clim Change 65(1):1–9CrossRefGoogle Scholar
  57. Lempert RJ, Collins MT (2007) Managing the risk of uncertain threshold responses: comparison of robust, optimum, and precautionary approaches. Risk Anal 27(4):1009–1026CrossRefGoogle Scholar
  58. Lempert RJ, Groves DG, Popper SW, Bankes SC (2006) A general, analytic method for generating robust strategies and narrative scenarios. Manag Sci 52(4):514–528CrossRefGoogle Scholar
  59. Maccheroni F, Marinacci M, Rustichini A (2006) Ambiguity aversion, robustness, and the variational representation of preferences. Econometrica 74(6):1447–1498CrossRefGoogle Scholar
  60. Mitchell SD (2009) Unsimple truths: science, complexity, and policy. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  61. Morgan MG, Dowlatabadi H, Henrion M, Keith D, Lempert R, McBride S, Small M, Wilbanks T (contributing authors) (2009) Best practice approaches for characterizing, communicating and incorporating scientific uncertainty in climate decision making. A report by the climate change science program and the subcommittee on global change research, National Oceanic and Atmospheric AdministrationGoogle Scholar
  62. Parker W (2014) Values and uncertainties in climate prediction, revisited. Stud Hist Philos Sci A 46:24–30CrossRefGoogle Scholar
  63. Parker WS, Winsberg E (2018) Values and evidence: how models make a difference. Eur J Philos Sci 8:135–142Google Scholar
  64. Peterson M (2009) An introduction to decision theory. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  65. Phillips LD (1984) A theory of requisite decision models. Acta Psychol 56(1):29–48CrossRefGoogle Scholar
  66. Popper SW (2016) What is decision making under deep uncertainty and how does it work? In: Conference presentation at the annual meeting of the Society for Decision Making under Deep UncertaintyGoogle Scholar
  67. Quinn J, Reed P, Giuliani M, Castelletti A (2017) Rival framings: a framework for discovering how problem formulation uncertainties shape risk management trade-offs in water resources systems. Water Resour Res 53(8):7208–7233CrossRefGoogle Scholar
  68. Quinn JD, Reed PM, Keller K (2017) Direct policy search for robust multi-objective management of deeply uncertain socio-ecological tipping points. Environ Model Softw 92(Supplement C):125–141CrossRefGoogle Scholar
  69. Ranger N, Reeder T, Lowe J (2013) Addressing ‘deep’ uncertainty over long-term climate in major infrastructure projects: four innovations of the Thames Estuary 2100 project. EURO J Decis Process 1(3–4):233–262CrossRefGoogle Scholar
  70. Rounsevell MD, Metzger MJ (2010) Developing qualitative scenario storylines for environmental change assessment. Wiley Interdiscip Rev 1(4):606–619Google Scholar
  71. Savage LJ (1954) The foundations of statistics. Wiley, New YorkGoogle Scholar
  72. Schwartz P (1996) The art of the long view: planning in an uncertain world. Currency-Doubleday, New YorkGoogle Scholar
  73. Sprenger J (2012) Environmental risk analysis: robustness is essential for precaution. Philos Sci 79(5):881–892CrossRefGoogle Scholar
  74. Steele K (2012) The scientist qua policy advisor makes value judgments. Philos Sci 79(5):893–904CrossRefGoogle Scholar
  75. Tsoukiàs A (2008) From decision theory to decision aiding methodology. Eur J Oper Res 187(1):138–161CrossRefGoogle Scholar
  76. Vezér M, Bakker A, Keller K, Tuana N (2017) Epistemic and ethical trade-offs in decision analytical modelling: flood risk management in coastal Louisiana. Clim Change 147:1–10CrossRefGoogle Scholar
  77. Walker O, Dietz S (2011) A representation result for choice under conscious unawareness. Technical Report 59, Grantham research institute on climate change and the environment working paperGoogle Scholar
  78. Walker WE, Haasnoot M, Kwakkel JH (2013) Adapt or perish: a review of planning approaches for adaptation under deep uncertainty. Sustainability 5(3):955–979CrossRefGoogle Scholar
  79. Walker WE, Lempert RJ, Kwakkel JH (2013) Encyclopedia of operations research and management science. Springer, New York, pp 395–402CrossRefGoogle Scholar
  80. Walker WE, Rahman SA, Cave J (2001) Adaptive policies, policy analysis, and policy-making. Eur J Oper Res 128(2):282–289CrossRefGoogle Scholar
  81. Weaver CP, Lempert RJ, Brown C, Hall JA, Revell D, Sarewitz D (2013) Improving the contribution of climate model information to decision making: the value and demands of robust decision frameworks. Wiley Interdiscip Rev 4(1):39–60Google Scholar
  82. Wilby RL, Dessai S (2010) Robust adaptation to climate change. Weather 65(7):180–185CrossRefGoogle Scholar
  83. Zeleny M (1989) Cognitive equilibirum: a new paradigm of decision making? Hum Syst Manag 8(3):185–188Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Pennsylvania State UniversityUniversity ParkUSA

Personalised recommendations