The Mismeasure of Risk

  • PeterR. Taylor


Tsunamis, volcanic ash clouds, financial crashes, and oil blowouts are recent disasters that have caught us by surprise and dominated world headlines. It seems that we are just not very good at predicting such risks or dealing with their consequences. This chapter looks at one reason this might be so – that we are not measuring risk appropriately, either in how we assess a risk or how we then make decisions about risk-related problems. Our measurements are conditioned by expectations of precision and simplicity which, we will argue, are too often lacking in the real world.

In assessing a risk we can miss potential outcomes, especially when a system changes over time. We can treat our theories as true irrespective of empirical support, and we can set impractical thresholds of acceptable risk. When we do measure the risk, we may not have enough data of sufficient quality, or behavioral responses may reduce the measured risk yet increase risk that is not measured. In short, our current assessments of risk are too blinkered.

The associated blunders in decision-making are also all too familiar – disregarding a risk because the model says the chance of the event occurring is low even though we have little confidence in the model; conversely, taking an unduly “precautionary” approach to avoid risks when the potential for harm is negligible; worst of all, perhaps, missing the wider decision-making context needed for a sound judgment.

Put together, these factors mean that simple measures of risk can be a poor guide for decision-makers. This chapter advocates five extensions to current methods in order to avoid these pitfalls in the future. In risk assessment, we need to:
  1. 1.

    Estimate unknown unknown risk

  2. 2.

    Quantify model risk

  3. 3.

    Use multiple measures and thresholds; and in decision support

  4. 4.

    Balance risk and reward; and

  5. 5.

    Examine ROB (Risk Outside the Box)


We will describe examples where, had certain measures of risk been in place, disasters might have been averted. We will then show how each of the extended methods may be applied. Two particular applications of the proposed approach will be provided – “RAG” statuses in project and risk management, and Solvency II in the insurance industry.


Return Period Model Risk Insurance Industry Banking Crisis Black Swan 
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.



The “Risk” section was adapted from “What is Risk?” (unpublished) by Rafaela Hillerband and Peter Taylor developed for the Risk Seminar Series at the James Martin School in Hilary Term 2008. I would also like to thank Rafaela for her editorial comments on the manuscript.

My thanks to David Shipley for the Terrorism Risk illustration, to Milan Vukelic and Andrew Baddeley for discussions on the use of reverse stress tests in insurance, to Professor Aurora Plomer for introducing me to Denis Noble’s “Music of Life” and the “Hand Formula,” and Chris Taylor, Milan Vukelic, David Shipley, Henry Ashton, Ben Matharu, and John Thirlwell for kindly commenting on drafts of the paper. Most of all, though, to Ian Nicol for his careful reading, questioning, and suggestions of re-expression for which the reader can be most grateful!


  1. Adams J (1995) Risk. UCL Press, LondonGoogle Scholar
  2. AIR (2003) Secondary uncertainty in AIR models 24 July 2003. Technical Note, Applied Insurance Research, BostonGoogle Scholar
  3. Alfred R (2010) June 22, 1783: Icelandic volcano disrupts Europe’s economy
  4. Atkinson D, Peijnenburg J (2006) Probability without certainty? Foundationalism Lewis Reichenbach debate. Stud Hist Philos Sci 37:442–453CrossRefGoogle Scholar
  5. Beck U (1992) Risk society: towards a new modernity. Sage, LondonGoogle Scholar
  6. Bedford T, Cooke J (2001) Probabilistic risk analysis. Cambridge University Press, see the reference quoted therein – Versteeg M (1977) Estimating common cause failure probabilities in reliability and risk analyses. J Hazard Mater 17:215–221Google Scholar
  7. Beinhocker ED (2006) Complex systems and the origin of wealth. Random House Business, LondonGoogle Scholar
  8. Bernstein PL (1996) The remarkable story of risk. Wiley, New YorkGoogle Scholar
  9. Cooke Y (2010) Yvonne Cooke’s article in The Independent on 6th July 2010Google Scholar
  10. Cruz M (ed) (2009) The Solvency II handbook. Risk Books, LondonGoogle Scholar
  11. Economist (2010) The Economist, May 2010Google Scholar
  12. Einhorn (2008) Private profits and socialized risk, GARP Risk Review (June/July 2008)Google Scholar
  13. Ellsberg D (1961) Risk, ambiguity and the savage axioms. Q J Econ 75:643–669CrossRefGoogle Scholar
  14. Gould S (1985) The mismeasure of man. Penguin, New YorkGoogle Scholar
  15. Gregoriou GN, Hoppe C, Wehn CS (eds) (2010) The risk modelling evaluation handbook. McGraw-Hill, New YorkGoogle Scholar
  16. Grossman PZ, Cearley RW, Cole DH (2006) Insurance, and the learned hand formula. Law Probability Risk 5(1):1–18CrossRefGoogle Scholar
  17. Hubbard D (2009) The failure of risk management: why it’s broken and how to fix it. Wiley, New YorkGoogle Scholar
  18. Hume D (1748) Enquiry concerning the principles of human understanding. Oxford Univerasity Press, OxfordGoogle Scholar
  19. JSB (2010) JSB guidelines for the assessment of general damages in personal injury cases. Oxford University Press, OxfordGoogle Scholar
  20. Kaplan S, Garrick BJ (1981) On the quantitative definition of risk. Risk Anal 1(1):11–27CrossRefGoogle Scholar
  21. Kendrick M (2008) The great cholesterol con. John Blake, LondonGoogle Scholar
  22. Keynes JM (1937) The general theory of employment. Q J Econ 51(1):209–223CrossRefGoogle Scholar
  23. Knight FH (1921) Risk uncertainty and profit. Houghton Mifflin, BostonGoogle Scholar
  24. Lanchester J (2010) Whoops!: why everyone owes everyone and no one can pay. Allen Lane, LondonGoogle Scholar
  25. Maudsley H (1867) The physiology and pathology of the mind (BiblioBazaar, LLC 2009)Google Scholar
  26. Montross F (2010) Model mania, GenRe pamphletGoogle Scholar
  27. Noble D (2008) The music of life: biology beyond genes. Oxford University Press, OxfordGoogle Scholar
  28. Oxford Economics (2010) The economic impact of air travel restrictions, Oxford Economics.
  29. Pilkey O, Pilkey-Jones L (2006) Useless arithmetic. Columbia University Press, ColumbiaGoogle Scholar
  30. POST (2009) The dual-use dilemma, Parliamentary Office of Science and Technology, Number 340, LondonGoogle Scholar
  31. RMS (2001) RMS Secondary uncertainty methodology. Risk Management Solutions, CaliforniaGoogle Scholar
  32. RMS (2010) Managing terrorism risk. Risk Management Solutions, CaliforniaGoogle Scholar
  33. Rumsfeld D (2002) DoD news briefing – secretary Rumsfeld and Gen. Myers 12 Feb 2002,
  34. Salmon F (2009) Recipe for disaster: the formula that killed wall street, Wired Magazine 17.03.
  35. Schrödinger E (1935) The present situation in quantum mechanics. In: Wheeler JA and Zurek WH (1983) Quantum theory and measurement. Princeton University Press, Princeton, p. 137Google Scholar
  36. Shackle GLS (1961) Decision, order and time in human affairs. Cambridge University Press, CambridgeGoogle Scholar
  37. Shipley (2009) Probably wrong – misapplications of probability and statistics to real-life uncertainty, presentations given by Taylor P and Shipley D at Oxford in 2008 and 2009.Google Scholar
  38. Smith L (2010) Uncertainty, ambiguity and risk in forming climate policy, handling uncertainty in science, royal society (Audio recording
  39. Society R (1992) Risk: analysis perception and management. Royal Society, LondonGoogle Scholar
  40. Taleb NN (2006) Fooled by randomness. Penguin, New YorkGoogle Scholar
  41. Taleb NN (2007) The black swan. Random House, New YorkGoogle Scholar
  42. Tett G (2009) Fool’s gold: how unrestrained greed corrupted a dream, shattered global markets and unleashed a catastrophe. Little Brown, LondonGoogle Scholar
  43. The Stationery Office (2007) Compensation for injury and death (Ogden tables) (ISBN 9-78-011560125-5).
  44. Turner (2009) Turner Report, FSA.
  45. Wack P (1985) Scenarios: shooting the rapids. Harvard Business Rev 63(6):139–150Google Scholar
  46. Wilde GJS (1994) Target risk – dealing with the danger of death, disease and damage in everyday decisions. PDE, TorontoGoogle Scholar
  47. Woo G (2004) Understanding terrorism risk, The Risk Report Jan 2004Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Oxford Martin SchoolUniversity of Oxford, Old Indian InstituteOxfordUK

Personalised recommendations