The DOI matches its Crossref record.
Addiction by Design: Machine Gambling in Las Vegas
Abstract
Recent decades have seen a dramatic shift away from social forms of gambling played around roulette wheels and card tables to solitary gambling at electronic terminals. Slot machines, revamped by ever more compelling digital and video technology, have unseated traditional casino games as the gambling industry's revenue mainstay. Addiction by Design takes readers into the intriguing world of machine gambling, an increasingly popular and absorbing form of play that blurs the line between human and machine, compulsion and control, risk and reward. Drawing on fifteen years of field research in Las Vegas, anthropologist Natasha Dow Schüll shows how the mechanical rhythm of electronic gambling pulls players into a trancelike state they call the "machine zone," in which daily worries, social demands, and even bodily awareness fade away. Once in the zone, gambling addicts play not to win but simply to keep playing, for as long as possible--even at the cost of physical and economic exhaustion. In continuous machine play, gamblers seek to lose themselves while the gambling industry seeks profit. Schüll describes the strategic calculations behind game algorithms and machine ergonomics, casino architecture and "ambience management," player tracking and cash access systems--all designed to meet the market's desire for maximum "time on device." Her account moves from casino floors into gamblers' everyday lives, from gambling industry conventions and Gamblers Anonymous meetings to regulatory debates over whether addiction to gambling machines stems from the consumer, the product, or the interplay between the two. Addiction by Design is a compelling inquiry into the intensifying traffic between people and machines of chance, offering clues to some of the broader anxieties and predicaments of contemporary life. At stake in Schüll's account of the intensifying traffic between people and machines of chance is a blurring of the line between design and experience, profit and loss, control and compulsion.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Extended Self in a Digital World: Table 1.
Belk, R. W. (2013) · Journal of Consumer Research · doi:10.1086/671052 · Cited by 1,698
Ethics of the Attention Economy: The Problem of Social Media Addiction
Bhargava, V. R., & Velasquez, M. (2021) · Business Ethics Quarterly · doi:10.1017/beq.2020.32 · Cited by 292
Roles of "wanting" and "liking" in motivating behavior: gambling, food, and drug addictions
Robinson, M. J. F., Fischer, A. M., Ahuja, A., Lesser, E. N., & Maniates, H. (2015) · Current topics in behavioral neurosciences · doi:10.1007/7854_2015_387 · Cited by 247
Ethical issues in service robotics and artificial intelligence
Belk, R. (2021) · The Service Industries Journal · doi:10.1080/02642069.2020.1727892 · Cited by 228
What Makes Smartphone Use Meaningful or Meaningless?
Lukoff, K., Yu, C., Kientz, J., & Hiniker, A. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3191754 · Cited by 205
Batching smartphone notifications can improve well-being
Fitz, N., Kushlev, K., Jagannathan, R., Lewis, T., Paliwal, D., & Ariely, D. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.07.016 · Cited by 195
How the Design of YouTube Influences User Sense of Agency
Lukoff, K., Lyngs, U., Zade, H., Liao, J. V., Choi, J., Fan, K., Munson, S. A., & Hiniker, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445467 · Cited by 190
Rethinking (Dis)Engagement in Human-Computer Interaction
O'Brien, H. L., Roll, I., Kampen, A., & Davoudi, N. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107109 · Cited by 139
van Doorn, N., & Chen, J. Y. (2021) · Socio-Economic Review · doi:10.1093/ser/mwab028 · Cited by 138
A Multivariate Evaluation of 25 Proximal and Distal Risk-Factors for Gambling-Related Harm
Browne, M., Hing, N., Rockloff, M., Russell, A. M. T., Greer, N., Nicoll, F., & Smith, G. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8040509 · Cited by 71
The concept of “harm” in Internet gaming disorder
King, D. L., & Delfabbro, P. H. (2018) · Journal of Behavioral Addictions · doi:10.1556/2006.7.2018.24 · Cited by 67
Rethinking how Technologies Harm
Wood, M. A. (2021) · The British Journal of Criminology · doi:10.1093/bjc/azaa074 · Cited by 60
Intensive online videogame involvement: A new global idiom of wellness and distress
Snodgrass, J. G., Dengah, H. J. F., Polzer, E., & Else, R. (2019) · Transcultural Psychiatry · doi:10.1177/1363461519844356 · Cited by 42
How algorithmic cultural recommendation influence the marketing of cultural collections
Wilson-Barnao, C. (2017) · Consumption Markets & Culture · doi:10.1080/10253866.2017.1331910 · Cited by 20
Learning to Like the Likes and the Hate: The Labor of Internet Fame in the New Attention Economy
Mears, A., & Beauvais, T. (2026) · Social Problems · doi:10.1093/socpro/spaf028 · Cited by 14
The Dark Addiction Patterns of Current AI Chatbot Interfaces
Shen, M. K., & Yoon, D. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3720003 · Cited by 8
Metadata
- Venue
- Princeton University Press eBooks
- Publisher
- Princeton University Press
- Published
- 2012-12-31
- OpenAlex
- W2156036907
- Cited by
- 600
- References
- 0
- FWCI
- 31.57
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.