Bibliographic record
Abstract
Dixon and colleagues demonstrate compelling evidence of the important role played by the inherent structural characteristics of electronic gambling machines (EGMs) in achieving key design goals, notably maximum revenue per customer and maximum time on device [1–3]. The capacity of contemporary EGMs for multi-line play, appears to be an effective strategy to increase average wagers, and to increase net revenue for EGM operators [4]. This aspect of EGM design permits the collation of multiple events into a single wager – in the case of a 15 line bet, it is as though 15 successive wagers were collapsed into a single event. EGMs commonly permit wagers on as many as 50 lines, and patented reel betting technology now permits wagers to be placed on up to 1,024 ‘ways’ of winning on a single spin of the virtual reels of an EGM. The effect of these innovations is to increase the price of play by many times the minimum credit value of the specific game. A one cent credit value game played at 50 lines will cost 50 cents per bet, which repeated at intervals of two to three seconds can quickly become an expensive pastime. The frequent occurrence of what appear to be ‘near misses’ on lines not being wagered on provides an incentive for most experienced EGM users to bet on as many lines as possible – the so-called ‘maxi-min’ strategy (i.e., placing a minimum credit bet on all available lines) which seeks to cover all possible winning combinations [4,5]. Dixon and colleagues have elaborated this by careful analysis of the game mathematics presented in so-called ‘PAR’ sheets (probability accounting reports) presenting the theoretical probabilities for game outcomes. In most jurisdictions, these have hitherto been protected as commercially confidential documents. The structure of the Canadian gambling industry (where gambling corporations are state-owned) has allowed the authors to gain access to these data using freedom of information legislation. By undertaking this analysis, the authors have begun the process of analysing systematically the structural characteristics of electronic gaming machine (EGM) games. The importance of this work should not be underestimated. Up to this point, most attempts to examine the effect of EGM structural characteristics have been relatively piecemeal, mainly utilizing self-reports of user perceptions, observations of EGM use, use of simulated games or measurement of arousal. In almost all existing research, however, actual game mathematics or machine data are absent [6–9]. Such studies have failed to link specific EGM design features convincingly to gambler behaviour. EGMs utilize intermittent reinforcement to encourage excessive and often harmful levels of use. In a sociological sense, EGMs appear to affect the agency of many of those who use them regularly, being productive of a specific subjectivity associated frequently with significant harm [10]. A recent report by the Australian Productivity Commission estimated that 40% of EGM revenue was derived from problem gamblers [i.e. those scoring 8 or more on the Canadian Problem Gambling Index (CPGI)] and a further 20% from those at moderate risk (CPGI score 3–7) [11]. Further careful analysis of EGM game mathematics, as Dixon et al. have demonstrated, has the potential to shed considerable light on the relationship between the core technology of EGM games and the production of harmful effects in EGM users. It is also likely to provide much important evidence for public health and other initiatives to reduce or minimize gambling-related harm, with positive consequences for more effective regulation of this common and highly harmful form of gambling, and for the development of improved approaches to treatment and prevention. The author received no funding in relation to this commentary. There are no connections between the author and alcohol, tobacco, pharmaceutical or gambling industries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".