Environment Systems and Decisions

, Volume 34, Issue 1, pp 88–97 | Cite as

Estimating expected value of information using Bayesian belief networks: a case study in fish consumption advisory

  • Patrycja L. Gradowska
  • Roger M. Cooke


A recent international collaborative effort was directed at quantifying the risks and benefits of fish consumption. A nonparametric continuous–discrete Bayesian belief network was constructed to support these calculations. The same Bayesian belief network has enabled calculation of the expected benefits of further research directed at shrinking the uncertainties and prioritization of possible research efforts.


Bayesian belief networks Decision making under uncertainty Health risk assessment Value of information 



The authors wish to thank Mr. Olli Leino, Dr. Jouni T. Tuomisto, Dr. Anna K. Karjalainen and other participants in BENERIS project for guidance and providing data required to quantify the BBN model used in this study.


  1. Bellanger M, Pichery C, Aerts D, Berglund M, Castaño A, Čejchanová M, Crettaz P, Davidson F, Esteban M, Fischer ME, Gurzau AE, Halzlova K, Katsonouri A, Knudsen LE, Kolossa-Gehring M, Koppen G, Ligocka D, Miklavčič A, Reis MF, Rudnai P, Tratnik JS, Weihe P, Budtz-Jørgensen E, Grandjean P; DEMO/COPHES (2013) Economic benefits of methylmercury exposure control in Europe: monetary value of neurotoxicity prevention. Environ Health 12:1–10Google Scholar
  2. Cohen JT, Bellinger DC, Connor WE, Shaywitz BA (2005a) A quantitative analysis of prenatal intake of n-3 polyunsaturated fatty acids and cognitive development. Am J Prev Med 29(4):366–374CrossRefGoogle Scholar
  3. Cohen JT, Bellinger DC, Shaywitz BA (2005b) A quantitative analysis of prenatal methyl mercury exposure and cognitive development. Am J Prev Med 29(4):353–365CrossRefGoogle Scholar
  4. Cohen JT, Bellinger DC, Connor WE, Kris-Etherton PM, Lawrence RS, Savitz DA, Shaywitz BA, Teutsch SM, Gray GM (2005c) A quantitative risk-benefit analysis of changes in population fish consumption. Am J Prev Med 29(4):325–334CrossRefGoogle Scholar
  5. Cooke RM (1991) Experts in uncertainty. Oxford University Press, New YorkGoogle Scholar
  6. Cooke RM, Kurowicka D, Hanea AM, Morales O, Ababei DA, Ale B, Roelen A (2007) Continuous/discrete non parametric Bayesian belief nets with UNICORN and UNINET. In: Bedford T, Quigley J, Walls L, Babakalli A (eds) Proceedings of the Mathematical Methods for Reliability ConferenceGoogle Scholar
  7. Cowell RG, Dawid P, Lauritzen SL, Spiegelhalter DJ (1999) Probabilistic networks and expert systems. Springer-Verlag, New YorkGoogle Scholar
  8. FAO/WHO (2010) Report of the joint FAO/WHO expert consultation on the risks and benefits of fish consumption, 25-29 January, 2010, Rome, ItalyGoogle Scholar
  9. Felli JC, Hazen GB (1998) Sensitivity analysis and the expected value of perfect information. Med Decis Making 18:95–109CrossRefGoogle Scholar
  10. Felli JC, Hazen GB (2003) Correction: sensitivity analysis and the expected value of perfect information. Med Decis Making 23:97CrossRefGoogle Scholar
  11. Fineli (2008) Fineli - Finnish Food Composition Database National Institute for Health and Welfare, Nutrition Unit. Accessed October 2008
  12. Genius SJ (2008) To sea or not to sea: benefits and risks of gestational fish consumption. Reprod Toxicol 26(2):81–85CrossRefGoogle Scholar
  13. Ginsberg GL, Toal BF (2000) Development of a single-meal fish consumption advisory for methyl mercury. Risk Anal 20(1):41–47CrossRefGoogle Scholar
  14. Gradowska PL (2013) Food benefit-risk assessment with Bayesian belief networks and multivariable exposure-response. Dissertation, Delft University of TechnologyGoogle Scholar
  15. Hanea A (2008) Algorithms for non-parametric Bayesian belief nets. Dissertation, Delft University of TechnologyGoogle Scholar
  16. Hanea A, Kurowicka D, Cooke RM (2006) Hybrid method for quantifying and analyzing Bayesian belief nets. Qual Reliab Eng Int 22:709–729CrossRefGoogle Scholar
  17. Jensen FV (1996) An introduction to Bayesian networks. UCL Press, LondonGoogle Scholar
  18. Jensen FV (2001) Bayesian networks and decision graphs. Springer-Verlag, New YorkCrossRefGoogle Scholar
  19. Joe H (1997) Multivariate models and dependence concepts. Chapman & Hall, LondonCrossRefGoogle Scholar
  20. Kurowicka D, Cooke RM (2005) Distribution-free continuous Bayesian belief nets. In: Wilson A, Limnios N, Keller-McNulty S, Armijo Y (eds) Modern statistical and mathematical methods in reliability. World Scientific Publishing Co. Pte. Ltd, Singapore pp. 309–322CrossRefGoogle Scholar
  21. Kurowicka D, Cooke RM (2006) Uncertainty analysis with high dimensional dependence modelling. John Wiley and Sons, West Sussex, EnglandCrossRefGoogle Scholar
  22. Leino O, Karjalainen AK, Tuomisto JT (2011) Effects of docosahexaenoic acid and methylmercury on child’s brain development due to consumption of fish by Finnish mother during pregnancy: a probabilistic modeling approach. Food Chem Toxicol. doi: 10.1016/j.fct.2011.06.052
  23. Morales O, Kurowicka D, Roelen A (2008) Eliciting conditional and unconditional rank correlations from conditional probabilities. Reliab Eng Syst Saf 93(5):699–710CrossRefGoogle Scholar
  24. Nelsen RB (2006) An introduction to copulas, 2nd edn. Springer-Verlag, New YorkGoogle Scholar
  25. Nylander E (ed) (2006) Finnish Fisheries Statistics 2006. Finnish Game and Fisheries Research Institute, Helsinki, FinlandGoogle Scholar
  26. Official Statistics of Finland (OSF) (2012) Births. Statistics Finland, Helsinki. Accessed May 2013
  27. Oken E, Bellinger DC (2008) Fish consumption, methylmercury and child neurodevelopment. Curr Opin Pediatr 20(2):178–183CrossRefGoogle Scholar
  28. Oostenbrink JB, Al MJ, Oppe M, Rutten-van Mölken MP (2008) Expected value of perfect information: an empirical example of reducing decision uncertainty by conducting additional research. Value Health 11(7):1070–1080CrossRefGoogle Scholar
  29. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers, San Mateo, CaliforniaGoogle Scholar
  30. Raiffa H (1968) Decision analysis: introductory lectures on choices under uncertainty. Random House, New YorkGoogle Scholar
  31. Raiffa H, Schlaifer R (1961) Applied statistical decision theory. Cambridge (MA): Division of Research, Graduate School of Business Administration, Harvard UniversityGoogle Scholar
  32. Samson D, Wirth A, Rickard J (1989) The value of information from multiple sources of uncertainty in decision analysis. Eur J Oper Res 39(3):254–260CrossRefGoogle Scholar
  33. Shachter RD, Kenley CR (1989) Gaussian influence variables. Manage Sci 35(5):527–550CrossRefGoogle Scholar
  34. US EPA (2001) Methylmercury (MeHg) (CASRN 22967-92-6). U.S. Environmental Protection Agency, Integrated Risk Information System. Accessed October 2008
  35. Venäläinen ER, Hallikainen A, Parmanne R, Vuorinen PJ (2004) Heavy metal contents in Finnish sea and fresh water fish. National Food Agency Publication 3/2004. Helsinki, FinlandGoogle Scholar
  36. Westöö G (1973) Methylmercury as percentage of total mercury in flesh and viscera of salmon and sea trout of various ages. Science 181(4099):567–568CrossRefGoogle Scholar
  37. Yokota F, Thompson KM (2004a) Value of information analysis in environmental health risk management decisions: past, present, and future. Risk Anal 24(3):635–650CrossRefGoogle Scholar
  38. Yokota F, Thompson KM (2004b) Value of information literature analysis: a review of applications in health risk management. Med Decis Making 24(3):287–298CrossRefGoogle Scholar
  39. Zeilmaker MJ, Hoekstra J, van Eijkeren JCH, de Jong N, Hart A, Kennedy M, Owen H, Gunnlaugsdottir H (2013) Fish consumption during child bearing age: a quantitative risk-benefit analysis on neurodevelopment. Food Chem Toxicol 54:30–34. doi: 10.1016/j.fct.2011.10.068 Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Applied MathematicsDelft University of TechnologyDelftThe Netherlands
  2. 2.Resources for the FutureWashingtonUSA
  3. 3.University of StrathclydeGlasgowUK
  4. 4.Delft University of TechnologyDelftThe Netherlands

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