Journal of Gambling Studies

, Volume 31, Issue 2, pp 359–366 | Cite as

Tilting at Windmills: A Comment on Auer and Griffiths

  • Julia Braverman
  • Matthew Tom
  • Howard J. Shaffer
Review Paper


In their review of Internet gambling studies, Auer and Griffiths (Soc Sci Comput Rev 20(3):312–320, 2013) question the validity of using bet size as an indicator of gambling intensity. Instead, Auer and Griffiths suggest using “theoretical loss” as a preferable measure of gambling intensity. This comment identifies problems with their argument and suggests a convergent rather than an exclusionary approach to Internet gambling measures and analysis.


Online gambling Bet size Theoretical loss Betting behavior 


Acknowledgments provided primary support for the preparation of this manuscript. The Division on Addiction also receives support from the National Institute on Alcohol and Alcohol Abuse, National Institute of Mental Health, National Institute on Drug Abuse, The Massachusetts Council on Compulsive Gambling, the Century Council, Saint Francis House, ABMRF/Foundation of Alcohol Research and others. The authors extend thanks to Debi A. LaPlante, Sarah E. Nelson, and Heather M. Gray for their support and thoughtful comments on previous drafts of the paper. None of the supporters or any of the authors has personal interests in or its associated companies that would suggest a conflict of interest.


  1. Afifi, T. O., LaPlante, D. A., & Shaffer, H. J. (in press). Types of gambling, gambling involvement, and gambling-related problems. International Journal of Mental Health and Addiction.Google Scholar
  2. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders: DSM-IV-TR (4th ed., text revision ed.). Washington, DC: American Psychiatric Association.Google Scholar
  3. Auer, M., & Griffiths, M. D. (2012). Theoretical loss and gambling intensity: A simulation study. Gaming Law Review and Economics, 16, 269–273.CrossRefGoogle Scholar
  4. Auer, M., & Griffiths, M. D. (2013). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies. doi: 10.1007/s10899-013-9376-7.PubMedGoogle Scholar
  5. Billings, D., Davidson, A., Schaeffer, J., & Szafron, D. (2002). The challenge of poker. Artificial Intelligence, 134(1–2), 201–240. doi: 10.1016/S0004-3702(01)00130-8.CrossRefGoogle Scholar
  6. Braverman, J., LaBrie, R. A., & Shaffer, H. J. (2011). A taxometric analysis of actual internet sports gambling behavior. Psychological Assessment, 23(1), 234–244. doi: 10.1037/a0021404.CrossRefPubMedGoogle Scholar
  7. Braverman, J., LaPlante, D. A., Nelson, S. E., & Shaffer, H. J. (2013). Using cross-game behavioral markers for early identification of high-risk Internet gamblers. Psychology of Addictive Behaviors, 27(3), 868–877.Google Scholar
  8. Braverman, J., & Shaffer, H. J. (2012). How do gamblers start gambling: Identifying behavioural markers for high-risk Internet gambling. European Journal of Public Health, 22(2), 273–278.Google Scholar
  9. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.CrossRefPubMedGoogle Scholar
  10. Dragicevic, S., Tsogas, G., & Kudic, A. (2011). Analysis of casino online gambling data in relation to behavioural risk markers for high-risk gambling and player protection. International Gambling Studies, 11(3), 377–391.CrossRefGoogle Scholar
  11. Gainsbury, S., Sadeque, S., Mizerski, D., & Blaszczynski, A. (2012). Wagering in Australia: A retrospective behavioural analysis of betting patterns based on player account data. Journal of Gambling Business and Economics, 6(2), 50–68.Google Scholar
  12. Galfond, P. (2007, 2007/03/29/). G-Bucks. Bluff Magazine.Google Scholar
  13. Ganzfried, S., & Sandholm, T. (2008, 2008/05/12). Computing an approximate jam/fold equilibrium for 3-player no-limit Texas Hold’em tournaments.Google Scholar
  14. Gebauer, L., LaBrie, R. A., & Shaffer, H. J. (2010). Optimizing DSM IV classification accuracy: A brief bio-social screen for detecting current gambling disorders among gamblers in the general household population. Canadian Journal of Psychiatry, 55(2), 82–90.Google Scholar
  15. Gray, H. M., LaPlante, D. A., & Shaffer, H. J. (2012). Behavioral characteristics of Internet gamblers who trigger corporate responsible gambling interventions. Psychology of Addictive Behaviors. doi: 10.1037/a0028545. Online First.PubMedGoogle Scholar
  16. Griffiths, M. D., & Parke, J. (2002). The social impact of Internet gambling. Social Science Computer Review, 20(3), 312–320.CrossRefGoogle Scholar
  17. Humphreys, B. R., Lee, Y. S., & Soebbing, B. P. (2011). Modelling consumers’ participation in gambling markets and frequency of gambling. Journal of Gambling Business and Economics, 5, 1.Google Scholar
  18. King, S. A., & Barak, A. (1999). Compulsive Internet gambling: A new form of an old clinical pathology. Cyberpsychology & Behavior, 2(5), 441–456.CrossRefGoogle Scholar
  19. LaBrie, R. A., Kaplan, S. A., LaPlante, D. A., Nelson, S. E., & Shaffer, H. J. (2008). Inside the virtual casino: A prospective longitudinal study of Internet casino gambling. European Journal of Public Health, 18(4), 410–416.CrossRefPubMedGoogle Scholar
  20. LaPlante, D. A., Afifi, T. O., & Shaffer, H. J. (2013). Games and gambling involvement among casino patrons. Journal of Gambling Studies, 29(2), 191–203.Google Scholar
  21. LaPlante, D. A., Kleschinsky, J. H., LaBrie, R. A., Nelson, S. E., & Shaffer, H. J. (2009). Sitting at the virtual poker table: A prospective epidemiological study of actual Internet poker gambling behavior. Computers in Human Behavior, 25(3), 711–717.CrossRefGoogle Scholar
  22. LaPlante, D. A., Schumann, A., LaBrie, R. A., & Shaffer, H. J. (2008). Population trends in Internet sports gambling. Computers in Human Behavior, 24(5), 2399–2414.CrossRefGoogle Scholar
  23. Moshman, C. (2007). Sit ‘n go strategy: Expert advice for beating one table poker tournaments. Henderson, NV: Two Plus Two Publishing.Google Scholar
  24. Nelson, S. E., LaPlante, D. A., Peller, A. J., Schumann, A., LaBrie, R. A., & Shaffer, H. J. (2008). Real limits in the virtual world: Self-limiting behavior of Internet gamblers. Journal of Gambling Studies, 24(4), 463–477. doi: 10.1007/s10899-008-9106-8.CrossRefPubMedGoogle Scholar
  25. Peller, A. J., LaPlante, D. A., & Shaffer, H. J. (2008). Parameters for safer gambling behavior: Examining the empirical research. Journal of Gambling Studies, 24(4), 519–534. doi: 10.1007/s10899-008-9097-5.CrossRefPubMedGoogle Scholar
  26. Shaffer, H. J., & Martin, R. (2011). Disordered gambling: Etiology, trajectory, and clinical considerations. Annual Review of Clinical Psychology, 7(April), 483–510. doi: 10.1146/annurev-clinpsy-040510-143928.CrossRefPubMedGoogle Scholar
  27. Sklansky, D. (1987). The theory of poker: A professional poker player teaches you how to think like one. Henderson, NV: Two Plus Two Publishing.Google Scholar
  28. Trochim, W. M. (2000). The research methods knowledge base (2nd ed.). Cincinnati, OH: Atonic Dog Publishing.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Julia Braverman
    • 1
    • 2
  • Matthew Tom
    • 1
  • Howard J. Shaffer
    • 1
    • 2
  1. 1.Division on AddictionThe Cambridge Health AllianceMedfordUSA
  2. 2.Harvard Medical SchoolBostonUSA

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