Journal of Gambling Studies

, Volume 31, Issue 4, pp 1671–1693 | Cite as

Decoding Problem Gamblers’ Signals: A Decision Model for Casino Enterprises

  • Sandra Ifrim
Original Paper


The aim of the present study is to offer a validated decision model for casino enterprises. The model enables those users to perform early detection of problem gamblers and fulfill their ethical duty of social cost minimization. To this end, the interpretation of casino customers’ nonverbal communication is understood as a signal-processing problem. Indicators of problem gambling recommended by Delfabbro et al. (Identifying problem gamblers in gambling venues: final report, 2007) are combined with Viterbi algorithm into an interdisciplinary model that helps decoding signals emitted by casino customers. Model output consists of a historical path of mental states and cumulated social costs associated with a particular client. Groups of problem and non-problem gamblers were simulated to investigate the model’s diagnostic capability and its cost minimization ability. Each group consisted of 26 subjects and was subsequently enlarged to 100 subjects. In approximately 95 % of the cases, mental states were correctly decoded for problem gamblers. Statistical analysis using planned contrasts revealed that the model is relatively robust to the suppression of signals performed by casino clientele facing gambling problems as well as to misjudgments made by staff regarding the clients’ mental states. Only if the last mentioned source of error occurs in a very pronounced manner, i.e. judgment is extremely faulty, cumulated social costs might be distorted.


Problem gambling Business ethics Viterbi algorithm Decoding Model error Model validation Decision model 

JEL Classification

D80 M140 I120 


  1. Abdin, E., Subramaniam, M., Vaingankar, J., & Chong, S. A. (2012). Reliability and validity of the English version of the South Oaks Gambling Screen in a multiracial Asian community sample in Singapore. International Gambling Studies, 12(3), 27–293.CrossRefGoogle Scholar
  2. Ahlheim, M., & Zahn, A. (2007). Macht Geld glücklich? Verbraucherpolitische Überlegungen zum fiskalischen Ziel der staatlichen Glücksspielregulierung. Wirtschaftsdienst, 87(6), 370–377.CrossRefGoogle Scholar
  3. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.Google Scholar
  4. Anderson, B. (2001). Forgetting properties for hidden Markov models. In D. Cochran, B. Moran, & L. White (Eds.), Defence applications of signal processing: Proceedings of the US/Australia joint workshop on defence applications of signal processing (pp. 26–39). Amsterdam: Elsevier.Google Scholar
  5. Australasian Gaming Council (AGC). (2011/2012). A database on Australia’s gambling industry 2011/12. Accessed September 2, 2013.
  6. Becker, T. (2011). Soziale Kosten des Glücksspiels in Deutschland. Frankfurt am Main: Peter Lang.Google Scholar
  7. Blaszczynski, A., & Farrell, E. (1998). A case series of 44 completed gambling-related suicides. Journal of Gambling Studies, 14(2), 93–109.CrossRefPubMedGoogle Scholar
  8. Blaszczynski, A., Ladouceur, R., & Shaffer, H. (2004). A science-based framework for responsible gambling: The Reno model. Journal of Gambling Studies, 20(3), 301–317.CrossRefPubMedGoogle Scholar
  9. Blaszczynski, A., Steel, Z., & McConaghy, N. (1997). Impulsivity in pathological gambling: The antisocial impulsivist. Addiction, 92(1), 75–87.CrossRefPubMedGoogle Scholar
  10. Boening, J. A. L. (2001). Neurobiology of an addiction memory. Journal of Neural Transmission, 108(6), 755–765.CrossRefPubMedGoogle Scholar
  11. Bondolfi, G., Osiek, C., & Ferrero, F. (2002). Pathological gambling: An increasing and underestimated disorder. Schweizer Archiv für Neurologie und Psychiatrie, 153(3), 116–122.Google Scholar
  12. Carlton, P., & Manowitz, P. (1987). Physiologica factors as determinants of patho1ogical gambling. Journal of Gambling Behavior, 3(4), 274–285.CrossRefGoogle Scholar
  13. Castellani, B., & Rugle, L. (1995). Comparison of pathological gamblers to alcoholics and cocaine misusers on impulsivity, sensation seeking, and craving. International Journal of the Addictions, 30(3), 275–289.PubMedGoogle Scholar
  14. Chamberlain, L. (2004). Understanding and diagnosing compulsive gambling. In Coombs, R. (Ed): Handbook of addictive disordersA practical guide to diagnosis and treatment (pp. 129–160). Hoboken, NJ: Wiley.Google Scholar
  15. Christie, T., Groarke, L., & Sweet, W. (2008). Virtue ethics as an alternative to deontological and consequential reasoning in the harm reduction debate. International Journal of Drug Policy, 19(1), 52–58.CrossRefPubMedGoogle Scholar
  16. Cotton, R. (2013). Learning R. Beijing: O’Reilly.Google Scholar
  17. Curran, G. M., Ounpraseuth, S. T., Allee, E., Small, J., & Booth, B. M. (2011). Trajectories in use of substance abuse and mental health services among stimulant users in rural areas. Psychiatric Services, 62(10), 1230–1232.CrossRefPubMedGoogle Scholar
  18. Davies, B. (2007). iCare: Integrating responsible gaming into casino operation. International Journal of Mental Health and Addiction, 5(4), 307–310.CrossRefGoogle Scholar
  19. de la Pena, V., Rivera, M., & Ruiz-Mata, J. (2006/2007). Quality control of risk measures: backtesting VAR models. Journal of Risk, 9(2), 39–54.Google Scholar
  20. Delfabbro, P., Borgas, M., & King, D. (2012). Venue staff knowledge of their patrons’ gambling and problem gambling. Journal of Gambling Studies, 28(2), 155–169.CrossRefPubMedGoogle Scholar
  21. Delfabbro, P., Osborn, A., Nevile, M., Skelt, L., & McMillen, J. (2007). Identifying problem gamblers in gambling venues: Final report. Report prepared for gambling research Australia.Google Scholar
  22. Devlin, J., Kamali, M., Subramanian, K., Prasad, R., & Natarajan, P. (2012). Statistical machine translation as a language model for handwriting recognition. In Università degli Studi di Bari Aldo Moro: 2012 International conference on frontiers in handwriting recognition (pp. 291–296). Piscataway, NJ: IEEE.Google Scholar
  23. Durbin, R., Eddy, S., Krogh, A., & Mitchison, G. (2002). Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge: Cambridge University Press.Google Scholar
  24. Eid, M., Gollwitzer, M., & Schmitt, M. (2010). Statistik und Forschungsmethoden. Weinheim: Beltz.Google Scholar
  25. Evans, R. (2003). Some theoretical models and constructs generic to substance abuse prevention programs for adolescents: Possible relevance and limitations for problem gambling. Journal of Gambling Studies, 19(3), 287–302.CrossRefPubMedGoogle Scholar
  26. Evenden, L. (1999). Varieties of impulsivity. Psychopharmacology (Berl), 146(4), 348–361.CrossRefGoogle Scholar
  27. Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.CrossRefPubMedGoogle Scholar
  28. Ferrão, Y. A., Almeida, V. P., Bedin, N. R., Rosa, R., & D’Arrigo Busnello, E. (2006). Impulsivity and compulsivity in patients with trichotillomania or skin picking compared with patients with obsessive-compulsive disorder. Comprehensive Psychiatry, 47(4), 282–288.CrossRefGoogle Scholar
  29. Ferris, J., & Wynne, H. (2001). The Canadian problem gambling index: Final report. Submitted to the Canadian Centre on Substance Abuse. Ottawa, Ontario: CCSA.Google Scholar
  30. First Amendment to the State Treaty on Gambling (2012). Published in Gesetz- und Verordnungsblatt für das Land Nordrhein-Westfalen – No. 29, November 22, 2012, pp. 534–544. Accessed 21 Aug 2013.
  31. First Amendment to the State Treaty on Gambling—North Rhine-Westphalia (2012). Published in Gesetz- und Verordnungsblatt für das Land Nordrhein-Westfalen – No. 29, November 22, 2012, pp. 523–534. Accessed 10 June 2014.
  32. Gambino, B. (2012). The validation of screening tests: Meet the new screen same as the old screen? Journal of Gambling Studies, 28(4), 573–605.CrossRefPubMedGoogle Scholar
  33. Gauthier, C. (2005). The virtue of moral responsibility and the obligations of patients. Journal of Medicine and Philosophy, 30(2), 153–166.CrossRefPubMedGoogle Scholar
  34. Griffiths, M., Wood, R., & Parke, J. (2009). Social responsibility tools in online gambling: A survey of attitudes and behavior among internet gamblers. Cyber Psychology & Behavior, 12(4), 413–421.CrossRefGoogle Scholar
  35. Häfeli, J., & Schneider, C. (2005). Identifikation von Problemspielern im Kasino – Ein Screeninginstrument (ID-PS). Luzern.Google Scholar
  36. Hancock, L., Schellinck, T., & Schrans, T. (2008). Gambling and corporate social responsibility (CSR): Re-defining industry and state roles on duty of care, host responsibility and risk management. Policy and Society, 27(1), 55–68.CrossRefGoogle Scholar
  37. Herriff, J. (2009). Gambling: The hidden addiction. Michigan Bar Journal, 88(5), 54–56.Google Scholar
  38. Independent Gambling Authority of South Australia (2013). South Australian gambling codes of practice prescription notice. Accessed 21 April 2014.
  39. Jia, Z., Wang, Y., Hu, Y., McLaren, C., Yu, Y., Ye, K., et al. (2013). A sample selection strategy to boost the statistical power of signature detection in cancer expression profile studies. Anti-Cancer Agents in Medicinal Chemistry, 13(2), 203–211.Google Scholar
  40. Jollimore, T. (2010). Impartiality, Stanford Encyclopedia of Philosophy/Winter 2010 Edition. Accessed May 10, 2014.
  41. Jurafsky, D., & Martin, J. (2009). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition (2nd ed.). Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
  42. Kim, S. W., & Grant, J. E. (2001). Personality dimensions in pathological gambling disorder and obsessive compulsive disorder. Psychiatry Research, 104(3), 205–212.CrossRefPubMedGoogle Scholar
  43. Kleiman, M. A. R. (2004). Costs and benefits of immunotherapies or depot medications for the treatment of drug abuse. In National Research Council (US) and Institute of Medicine (US) Committee on Immunotherapies and Sustained-Release Formulations for Treating Drug Addiction; Harwood, H. J. & Myers, T. G. (Eds.), New treatments for addiction: Behavioral, ethical, legal, and social questions (pp. 213–240). Washington, DC: National Academies Press.Google Scholar
  44. Lai, F., Ip, A., & Lee, T. (2011). Impulsivity and pathological gambling among Chinese: Is it a state or a trait problem? BMC Research Notes, 4, 492.PubMedCentralCrossRefPubMedGoogle Scholar
  45. Lantos, G. P. (2001). The boundaries of strategic corporate social responsibility. Journal of Consumer Marketing, 18(7), 595–632.CrossRefGoogle Scholar
  46. Lee, C.-K., Song, H.-J., Lee, H.-M., Lee, S., & Bernhard, B. (2013). The impact of CSR on casino employees’ organizational trust, job satisfaction, and customer orientation: An empirical examination of responsible gambling strategies. International Journal of Hospitality Management, 33, 406–415.CrossRefGoogle Scholar
  47. Lesieur, H., & Rosenthal, R. (1991). Pathological gambling: A review of the literature (prepared for the American Psychiatric Association Task Force on DSM-IV committee on disorders of impulse control not elsewhere classified). Journal of Gambling Studies, 7(1), 5–39.CrossRefPubMedGoogle Scholar
  48. Lorenz, V., & Shuttlesworth, D. (1983). The impact of pathological gambling on the spouse of the gambler. Journal of Community Psychology, 11(1), 67–76.CrossRefGoogle Scholar
  49. Lorenz, V., & Yaffee, R. (1988). Pathological gambling: Psychosomatic, emotional, and marital difficulties as reported by the spouse. Journal of Gambling Behavior, 4(1), 13–26.CrossRefGoogle Scholar
  50. Marshall, K., & Wynne, H. (2003). Fighting the odds. Perspectives on Labour and Income, 4(12), 5–13.Google Scholar
  51. McCarl, B. A. (1984). Model validation: An overview with some emphasis on risk models. Review of Marketing and Agricultural Economics, 52(3), 153–173.Google Scholar
  52. Meyer, G., & Bachmann, M. (2011). Spielsucht: Ursachen, Therapie und Prävention von glücksspielbezogenem Suchtverhalten (3rd edn). Berlin: Springer.Google Scholar
  53. Neal, P., Delfabbro, P., & O’Neil, M. (2005). Problem gambling and harm: Towards a national definition. Report commissioned by gambling research Australia for the Ministerial Council on Problem Gambling. Melbourne: Victorian Department of Justice.Google Scholar
  54. Nower, L., Derevensky, J., & Gupta, R. (2001). The relationship of impulsivity, sensation seeking, coping, and substance use in youth gamblers. Psychology of Addictive Behaviors, 18(1), 49–55.CrossRefGoogle Scholar
  55. Petry, N., Blanco, C., Auriacombe, M., Borges, G., Bucholz, K., Crowley, T. J., et al. (2014). An overview of and rationale for changes proposed for pathological gambling in DSM-5. Journal of Gambling Studies 30(2), 493–502.Google Scholar
  56. Phillips, D. (2005). Gambling: The hidden addiction. Behavioral Health Management, 25(5), 32–37.Google Scholar
  57. Pinkerton, S., Johnson-Masottia, A., Derseb, A., & Laydec, P. (2002). Ethical issues in cost-effectiveness analysis. Evaluation and Program Planning, 25(1), 71–83.CrossRefGoogle Scholar
  58. Productivity Commission. (2010). Gambling, report no. 50. Canberra.Google Scholar
  59. Quinn, F. L. (2001). First do no harm: What could be done by casinos to limit pathological gambling. Managerial and Decision Economics, 22(1/3), 133–142.CrossRefGoogle Scholar
  60. Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286.CrossRefGoogle Scholar
  61. Rosenthal, R. (1986). The pathological gambler’s system for self-deception. Journal of Gambling Behaviour, 2(2), 108–120.CrossRefGoogle Scholar
  62. Sargent, R. (2005). Verification and validation of simulation models. In M. E. Kuhl, et al. (Eds.), Proceedings of the 2005 winter simulation conference (pp. 130–143). New York: Association for Computing Machinery.CrossRefGoogle Scholar
  63. Schellinck, T., & Schrans, T. (2007). Assessment of the behavioral impact of responsible gaming device (RGD) features: Analysis of Nova Scotia player-card data: Windsor Trial: Final report.Google Scholar
  64. Schellinck, T., & Schrans, T. (2011). Intelligent design: How to model gambler risk assessment by using loyalty tracking data. Journal of Gambling Issues, 26, 51–68.CrossRefGoogle Scholar
  65. Schwer, K., Thompson, W., & Nakamuro, D. (2003). Beyond the limits of recreation: Social costs of gambling in Southern Nevada. A paper presented to annual meeting of the far west and American popular culture association, Las Vegas.Google Scholar
  66. Scull, S., Butler, D., & Mutzelberg, M. (2004). Problem gambling in non-English speaking background communities in Queensland: A pilot study, University of Queensland. Accessed 2 Oct 2013.
  67. Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.CrossRefPubMedGoogle Scholar
  68. Spunt, B., Dupont, I., Lesieur, H., Liberty, H., & Hunt, D. (1998). Pathological gambling and substance misuse: A review of the literature. Substance Use and Misuse, 33(13), 2535–2560.CrossRefPubMedGoogle Scholar
  69. Stoll, C., Kapfhammer, H. P., Rothenhäusler, H. B., Haller, M., Briegel, J., Schmidt, M., et al. (1999). Sensitivity and specificity of a screening test to document traumatic experiences and to diagnose post-traumatic stress disorder in ARDS patients after intensive care treatment. Intensive Care Medicine, 25(7), 697–704.CrossRefPubMedGoogle Scholar
  70. Sutton, A. J., Cooper, N. J., Goodacre, S., & Stevenson, M. (2008). Integration of meta-analysis and economic decision modeling for evaluating diagnostic tests. Medical Decision Making, 28(5), 650–667.CrossRefPubMedGoogle Scholar
  71. Thompson, W., Gazel, R., & Rickman, D. (1997). Social and legal costs of compulsive gambling. Gaming Law Review, 1(1), 81–89.CrossRefGoogle Scholar
  72. Toneatto, T., & Millar, G. (2004). Assessing and treating problem gambling: empirical status and promising trends. Canadian Journal of Psychiatry, 49(8), 517–525.Google Scholar
  73. Victorian Responsible Gambling Foundation. (2012). The Victorian gambling study—A longitudinal study of gambling and public health: Wave three findings. Melbourne: Author.Google Scholar
  74. Vitaro, F., Arseneault, L., & Tremblay, R. (1999). Impulsivity predicts problem gambling in low SES adolescent males. Addiction, 94(4), 565–575.CrossRefPubMedGoogle Scholar
  75. Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13(2), 260–269.CrossRefGoogle Scholar
  76. Volberg, R. A., Moore, W. L., Christiansen, E., Cummings, W., & Banks, S. (1998). Unaffordable losses: Estimating the proportion of gambling revenues derived from problem gamblers. Gaming Law Review, 2(4), 349–360.CrossRefGoogle Scholar
  77. Walker, D. M. (2011). Overview of the economic and social impacts of gambling in the United States. Accessed June 26, 2013.
  78. Wendemuth, A. (2004). Grundlagen der stochastischen Sprachverarbeitung. München: Oldenbourg.CrossRefGoogle Scholar
  79. Whetstone, J. (2001). How virtue fits within business ethics. Journal of Business Ethics, 33(2), 101–114.CrossRefGoogle Scholar
  80. Whyte, K. (2012). The state of responsible gaming in the United States. International Gambling Studies, 12(1), 1–3.CrossRefGoogle Scholar
  81. Windsor, D. (2006). Corporate social responsibility: Three key approaches. Journal of Management Studies, 43(1), 93–114.CrossRefGoogle Scholar
  82. Wynne, H. (2002). Gambling and problem gambling in Saskatchewan. Final report, Canadian Centre on Substance Abuse. Ottawa, Ontario. Accessed April 25, 2014.

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of FinanceHeinrich-Heine-University DüsseldorfDüsseldorfGermany

Personalised recommendations