Deciding to Disclose: A Decision Theoretic Agent Model of Pregnancy and Alcohol Misuse

  • Jonathan Gray
  • Jakub Bijak
  • Seth Bullock
Part of the The Springer Series on Demographic Methods and Population Analysis book series (PSDE, volume 41)


We draw together methodologies from game theory, agent based modelling, decision theory, and uncertainty analysis to explore the process of decision making in the context of pregnant women disclosing their drinking behaviour to their midwives. We employ a game theoretic framework to define a signalling game. The game represents a scenario where pregnant women decide the extent to which they disclose their drinking behaviours to their midwives, and midwives employ the information provided to decide whether to refer their patients for costly specialist treatment. This game is then recast as two games played against “nature”, to permit the use of a decision theoretic approach where both classes of agent use simple rules to decide their moves. Four decision rules are explored – a lexicographic heuristic which considers only the link between moves and payoffs, a Bayesian risk minimisation agent that uses the same information, a more complex Bayesian risk minimiser with full access to the structure of the decision problem, and a Cumulative Prospect Theory (CPT) rule. In simulation, we recreate two key qualitative trends described in the midwifery literature for all the decision models, and investigate the impact of introducing a simple form of social learning within agent groups. Finally a global sensitivity analysis using Gaussian Emulation Machines (GEMs) is conducted, to compare the response surfaces of the different decision rules in the game.


Decision Rule Social Learning Heavy Drinker Agent Base Modelling Alcohol Misuse 
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.



This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/H021698/1 Care Life Cycle, funded within the Complexity Science in the Real World theme. The authors also gratefully acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work.


  1. Ainslie, G. (1991). Derivation of “rational” economic behavior from hyperbolic discount curves. The American Economic Review, 81(2), 334–340.Google Scholar
  2. Alvik, A., Haldorsen, T., Groholt, B., & Lindemann, R. (2006). Alcohol consumption before and during pregnancy comparing concurrent and retrospective reports. Alcoholism: Clinical & Experimental Research, 30(3), 510–515.CrossRefGoogle Scholar
  3. Axelrod, R. (1997). Advancing the art of simulation in the social sciences. Complexity, 12(3), 1–13.Google Scholar
  4. Bijak, J., Hilton, J., Silverman, E., & Cao V D (2013) Reforging the wedding ring. Demographic Research, 29, 729–766.CrossRefGoogle Scholar
  5. Booij, A. S., Praag, B. M. S., & Kuilen, G. (2009). A parametric analysis of prospect theory’s functionals for the general population. Theory and Decision, 68(1–2), 115–148.Google Scholar
  6. Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125(3), 367–383.CrossRefGoogle Scholar
  7. Carnell, R. (2012). LHS: Latin Hypercube Samples. R package version 0.10. Accessed 1 July 2014.
  8. Department of health. (2008). Pregnancy and alcohol. London: Department of Health.Google Scholar
  9. Epstein, J. M. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11(4), 12.Google Scholar
  10. Epstein, J. M. (2014). Agent_Zero: Toward neurocognitive foundations for generative social science. Princeton: Princeton University Press.CrossRefGoogle Scholar
  11. Epstein, J. M., & Axtell, R. L. (1994). Growing artificial societies: Social science from the bottom up. Complex adaptive systems. Washington, DC: Brookings Institution Press.Google Scholar
  12. Feltovich, N., Harbaugh, R., & To, T. (2002). Too cool for school? Signalling and countersignalling. RAND Journal of Economics, 33(4), 630–649.CrossRefGoogle Scholar
  13. Fox, C. R., & Tversky, A. (1998). A belief-based account of decision under uncertainty. Management Science, 44(7), 879–895.CrossRefGoogle Scholar
  14. Gigerenzer, G. (2004). Fast and frugal heuristics: The tools of bounded rationality. In: D. J. Koehler, N. Harvey (Eds.), Blackwell handbook of judgment and decision making (pp. 62–88). Oxford: Blackwell Publishing.CrossRefGoogle Scholar
  15. Gilbert, N. (1999). Simulation: A new way of doing social science. American Behavioral Scientist, 42(10), 1485–1487.CrossRefGoogle Scholar
  16. Glöckner, A., & Pachur, T. (2012). Cognitive models of risky choice: Parameter stability and predictive accuracy of prospect theory. Cognition, 123(1), 21–32.CrossRefGoogle Scholar
  17. Gomberg, E. S. (1988). Alcoholic women in treatment: The question of stigma and age. Alcohol and Alcoholism, 23(6), 507–14.Google Scholar
  18. Hau, R., Pleskac, T. J., Kiefer, J., & Hertwig, R. (2008). The description-experience gap in risky choice: the role of sample size and experienced probabilities. Journal of Behavioral Decision Making, 21(5), 493–518.CrossRefGoogle Scholar
  19. Humphriss, R., Hall, A., May, M., Zuccolo, L., & Macleod, J. (2013). Prenatal alcohol exposure and childhood balance ability: Findings from a UK birth cohort study. BMJ Open, 3(6), 1–8.CrossRefGoogle Scholar
  20. Insua, D. R., Rios, J., & Banks, D. (2009). Adversarial risk analysis. Journal of the American Statistical Association, 104(486), 841–854.CrossRefGoogle Scholar
  21. Kennedy, M. C. (2004). CTCD: GEM Software. Accessed: 10 July 2014.Google Scholar
  22. Kreps, D. M., & Cho, I. (1987). Signalling games and stable equilibria. The Quarterly Journal of Economics, 102(2), 179–221.CrossRefGoogle Scholar
  23. Loewenstein, G., & Prelec, D. (1992). Anomalies in intertemporal choice: Evidence and an interpretation. The Quarterly Journal of Economics, 107(2), 573–597.CrossRefGoogle Scholar
  24. Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28(1), 143–166CrossRefGoogle Scholar
  25. National Institute for Health and Care Excellence (2010a). Antenatal care. CG62. London: NICE.Google Scholar
  26. National Institute for Health and Care Excellence (2010b). Pregnancy and complex social factors. CG110. Manchester: NICE.Google Scholar
  27. Neilson, W., & Stowe, J. (2002). A further examination of cumulative prospect theory parameterizations. Journal of Risk and Uncertainty, 24(1), 31–47.CrossRefGoogle Scholar
  28. Nilsson, H., Rieskamp, J., & Wagenmakers, E. J. (2011). Hierarchical bayesian parameter estimation for cumulative prospect theory. Journal of Mathematical Psychology, 55(1), 84–93.CrossRefGoogle Scholar
  29. Oakley, A., Strange, V., Bonell, C., Allen, E., & Stephenson, J. (2006). Process evaluation in randomised controlled trials of complex interventions. British Medical Journal, 332(7538), 413–416.CrossRefGoogle Scholar
  30. Oakley, J., & O’Hagan, A. (2002). Bayesian inference for the uncertainty distribution of computer model outputs. Biometrika, 89(4), 769–784.CrossRefGoogle Scholar
  31. Oakley, J. E., & O’Hagan, A. (2004). Probabilistic sensitivity analysis of complex models: A bayesian approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(3), 751–769.CrossRefGoogle Scholar
  32. Padoa-Schioppa, C, & Assad, J. A. (2006). Neurons in the orbitofrontal cortex encode economic value. Nature, 441(7090), 223–226.CrossRefGoogle Scholar
  33. Padoa-Schioppa, C, & Assad, J. A. (2008). The representation of economic value in the orbitofrontal cortex is invariant for changes of menu. Nature Neuroscience, 11(1), 95–102.CrossRefGoogle Scholar
  34. Phillips, D., Thomas, K., Cox, H., Ricciardelli, L. A., Ogle, J., Love, V., Steele, A., et al. (2007). Factors that influence women’s disclosures of substance use during pregnancy: A qualitative study of ten midwives and ten pregnant women. The Journal of Drug Issues, 37(2), 357–376.CrossRefGoogle Scholar
  35. R Core Team (2014). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
  36. Radcliffe, P. (2011). Substance-misusing women: Stigma in the maternity setting. British Journal of Midwifery, 19(8), 497–506.CrossRefGoogle Scholar
  37. Redshaw, M., & Henderson, J. (2014). Safely delivered: A national survey of women’s experiences of maternity care 2014. Oxford: The National Perinatal Epidemiology Unit.Google Scholar
  38. Resnick, M. (1994) Turtles, termites and traffic jams: Explorations in massively parallel microworlds. Cambridge: MIT Press.Google Scholar
  39. Rustichini, A. (2009). Neuroeconomics: what have we found, and what should we search for. Current Opinion in Neurobiology 19(6), 672–677.CrossRefGoogle Scholar
  40. Samuelson, P. A. (1937). Note on measurement of utility. The Review of Economic Studies, 4(2), 155–161CrossRefGoogle Scholar
  41. Silverman, E., Bijak, J., & Noble, J. (2011). Feeding the beast: Can computational demographic models free us from the tyranny of data? In: T. Lenaerts, M. Giacobini, H. Bersini, P. Bourgine, M. Dorigo, & R. Doursat (Eds.), Advances in artificial life, ECAL 2011 (Vol. 276, pp. 747–754). Cambridge: MIT Press.Google Scholar
  42. Silverman, E., Bijak, J., Hilton, J., Cao, V. D., & Noble, J. (2013). When demography met social simulation: A tale of two modelling approaches. Journal of Artificial Societies and Social Simulation, 16(4), 9.CrossRefGoogle Scholar
  43. Sokol, R. J., Martier, S. S., & Ager, J. W. (1989). The t-ace questions: Practical prenatal detection of risk-drinking. American Journal of Obstetrics and Gynecology, 160(4), 863–870.CrossRefGoogle Scholar
  44. Thaler, R. (1981). Some empirical evidence on dynamic inconsistency. Economics Letters, 8(3), 201–207.CrossRefGoogle Scholar
  45. Thiele, J. C., Kurth, W., & Grimm, V. (2014). Facilitating parameter estimation and sensitivity analysis of agent-based models: A cookbook using NetLogo and R. Journal of Artificial Societies and Simulation, 17(3), 11.CrossRefGoogle Scholar
  46. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.CrossRefGoogle Scholar
  47. Waldherr, A., & Wijermans, N. (2013). Communicating social simulation models to sceptical minds. Journal of Artificial Societies and Social Simulation, 16(4), 13.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Department of Social Statistics and DemographyUniversity of SouthamptonSouthamptonUK
  2. 2.Department of Computer ScienceUniversity of BristolBristolUK

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