Limit setting and player choice in most intense online gamblers: an empirical study of online gambling behaviour

Social responsibility in gambling has become a major issue for the gaming industry. The possibility for online gamblers to set voluntary time and money limits is a social responsibility practice that is now widespread among online gaming operators. The main issue concerns whether the voluntary setting of such limits has any positive impact on subsequent gambling behaviour and whether such measures are of help to problem gamblers. In this paper, this issue is examined through data collected from a representative random sample of 100,000 players who gambled on the win2day gambling website. When opening an account at the win2day site, there is a mandatory requirement for all players to set time and cash-in limits (that cannot exceed 800 Euros per week). During a three-month period, all voluntary time and/or money limit setting behaviour by a subsample of online gamblers (n=5000) within this mandatory framework was tracked and recorded for subsequent data analysis. From the 5,000 gamblers, the 10% most intense players (as measured by theoretical loss) were further investigated. Voluntary spending limits had the highest significant effect on subsequent monetary spending among casino and lottery gamblers.. Monetary spending among poker players significantly decreased after setting a voluntary time limit.. The highest significant decrease in playing duration was among poker players after setting a voluntary playing duration limit. The results of the study demonstrated that voluntary limit setting had a specific and significant effect on the studied gamblers. Therefore, voluntary limits appear to show voluntary limit setting had an appropriate effect in the desired target group (i.e., the most gaming intense players)

Theoretical Loss and Gambling Intensity (Revisited): A Response to Braverman et al. (2013)

In this paper, we provide a brief response to Braverman and colleagues’ (2013) critique of our ‘Theoretical Loss’ metric as a measure of monetary gambling intensity (Auer & Griffiths, 2013; Auer, Schneeberger & Griffiths, 2012). We argue that ‘gambling intensity’ and ‘gambling involvement’ are essentially the same construct as descriptors of monetary gambling activity. Additionally, we acknowledge that playing duration (i.e., the amount of time – as opposed to money – actually spent gambling) is clearly another important indicator of gambling involvement – something that we have consistently noted in our previous studies including our empirical studies on gambling using behavioural tracking data. Braverman and colleagues claim that the concept of Theoretical Loss is nullified when statistical analysis focuses solely on one game type as the house edge is constant across all games. In fact, they state, the correlation between total amount wagered and Theoretical Loss is perfect. Unfortunately, this is incorrect. To disprove the claim made, we demonstrate that in sports betting (i.e., a single game type), the amount wagered does not reflect monetary gambling involvement using actual payout percentage data (based on 52,500 independent bets provided to us by an online European bookmaker). After reviewing the arguments presented by Braverman and colleagues, we are still of the view that when it comes to purely monetary measures of ‘gambling intensity’, the Theoretical Loss metric is a more robust and accurate measure than other financial proxy measures such as ‘amount wagered’ (i.e., bet size) as a measure of what players are prepared to financially risk while gambling

Personalised feedback in the promotion of responsible gambling: a brief overview

Research into gambling has shown that irrational gambling-related cognitions linked to randomness and probabilities contribute to the initiation and maintenance of problematic gambling. A small body of empirical research has shown that educational programs about erroneous beliefs in gambling can successfully help change such cognitions. Studies have also shown that the way information is presented to gamblers is significant. Personalized behavioral feedback has been studied in many other areas outside of the gambling area (e.g., cigarette smoking). These examples from related areas suggest that behavioral feedback could also work in promoting responsible gambling. These approaches aim to change a person’s behavior via behavioral feedback. Such approaches are based on both the ‘stages of change’ model and motivational interviewing. Therefore, in order to change people’s gambling behavior using behavioral tracking data, player feedback should also be presented in a tailored and motivational way, and take into account the stages of change model

Theoretical loss and gambling intensity: a simulation study

Many recent studies of internet gambling—particularly those that have analysed behavioural tracking data—have used variables such as ‘bet size’ and ‘number of games played’ as proxy measures for ‘gambling intensity.’ In this paper, it is argued that the best and most stable measure for Gambling Intensity is the ‘Theoretical Loss’ (a product of total bet size and house advantage). In the long run, Theoretical Loss corresponds with the Gross Gaming Revenue generated by commercial gaming operators. For shorter periods of time, Theoretical Loss is the most stable measure of gambling intensity as it is not distorted by gamblers’ occasional wins. Even for single bets, the Theoretical Loss reflects the amount a player is willing to risk. Using a simulation study, with up to 300,000 players playing as many as 13 different games, this paper demonstrates that the bet size and the number of games do not explain the theoretical loss entirely. In fact, there is a large proportion of variance which remains unexplained by measures of ‘bet size’ and ‘number of games’ played. Bet size and the number of games played do not equate to or explain theoretical loss, as neither of these two measures takes into account the house advantage

Simulation and visualization of face seal motion stability by means of computer generated movies

A computer aided design method for mechanical face seals is described. Based on computer simulation, the actual motion of the flexibly mounted element of the seal can be visualized. This is achieved by solving the equations of motion of this element, calculating the displacements in its various degrees of freedom vs. time, and displaying the transient behavior in the form of a motion picture. Incorporating such a method in the design phase allows one to detect instabilities and to correct undesirable behavior of the seal. A theoretical background is presented. Details of the motion display technique are described, and the usefulness of the method is demonstrated by an example of a noncontacting conical face seal

The effect of trade with low-income countries on U.S. industry

When labor-abundant nations grow, their exports increase more in labor-intensive sectors than in capital-intensive sectors. We utilize this sectoral difference in how exports are affected by growth to identify the causal effect of trade with low-income countries (LICs) on U.S. industry. Our framework relates differences in sectoral inflation rates to differences in comparative advantageinduced import growth rates and abstracts from aggregate fluctuations and sector specific trends.> ; In a panel covering 325 manufacturing industries from 1997 to 2006, we find that LIC exports are associated with strong downward pressure on U.S. producer prices and a large effect on productivity. When LIC exporters capture 1% U.S. market share, producer prices decrease by 3.1%, which is nearly fully accounted by a 2.4% increase in productivity and a 0.4% decrease in markups. We also document that while LICs on average find it easier to penetrate sectors with elastic demand, the price and productivity response to import competition is much stronger in industries with inelastic demand. Overall, between 1997 and 2006, the effect of LIC trade on manufacturing PPI inflation was around two percentage points per year, far too large to be neglected in macroeconomic analysis.Globalization ; International trade ; Manufacturing industries ; Prices