Authors:
Jussi Palomäki Health and Well-Being Promotion Unit, Finnish Institute for Health and Welfare, P.O. Box 30, FI-00271, Helsinki, Finland

Search for other papers by Jussi Palomäki in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-6063-0926
,
Nigel Turner Institute for Mental Health Policy Research, and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

Search for other papers by Nigel Turner in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-1035-2064
,
Joseph Macey Centre of Excellence in Game Culture Studies, University of Turku, Turku, Finland
Gamification Group, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland

Search for other papers by Joseph Macey in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-9770-739X
, and
Sari Castrén Health and Well-Being Promotion Unit, Finnish Institute for Health and Welfare, P.O. Box 30, FI-00271, Helsinki, Finland
Department of Psychology and Speech-Language Pathology, University of Turku, Turku, Finland
Faculty of Medicine, University of Helsinki, Finland

Search for other papers by Sari Castrén in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-0492-9610
Open access

Abstract

Objective and Method

Electronic gambling machines are a prominent cause of significant gambling harms globally. We use simulations of a simplified video poker game to show how changes in game volatility, defined primarily by the size of the main prize, affect patterns of wins and losses as well as winning streaks.

Results

We found that in low- and medium volatility games the proportion of winning players quickly drops to zero after about 30 h of play, while in the high volatility game 5% of players are still winning after playing for 100 h. However, the proportion of winning streaks was significantly higher in the low- and medium volatility games compared with high volatility: the simulated players were on a winning streak about 26.3, 25.6 and 18% of the time in the low-, medium- and high volatility games, respectively.

Conclusions

Fast-paced video poker with varying volatility levels but identical return-to-player rates and win frequencies can yield highly different result patterns across individuals. These patterns may be counter-intuitive for players and difficult to realize without simulations and visualizations. We argue that the findings have relevance for responsible gambling communication and for building a better understanding of how cognitive biases influence gambling behaviour.

Abstract

Objective and Method

Electronic gambling machines are a prominent cause of significant gambling harms globally. We use simulations of a simplified video poker game to show how changes in game volatility, defined primarily by the size of the main prize, affect patterns of wins and losses as well as winning streaks.

Results

We found that in low- and medium volatility games the proportion of winning players quickly drops to zero after about 30 h of play, while in the high volatility game 5% of players are still winning after playing for 100 h. However, the proportion of winning streaks was significantly higher in the low- and medium volatility games compared with high volatility: the simulated players were on a winning streak about 26.3, 25.6 and 18% of the time in the low-, medium- and high volatility games, respectively.

Conclusions

Fast-paced video poker with varying volatility levels but identical return-to-player rates and win frequencies can yield highly different result patterns across individuals. These patterns may be counter-intuitive for players and difficult to realize without simulations and visualizations. We argue that the findings have relevance for responsible gambling communication and for building a better understanding of how cognitive biases influence gambling behaviour.

Fast-paced electronic gambling machines (EGMs) such as slot machines or video poker games are a significant reason for gambling harms globally (Browne et al., 2023; Järvinen-Tassopoulos, Marionneau, & Nikkinen, 2021). They are played by tens of millions of people, both online and offline, and constitute about 60–70% of the global market value of gambling in total (CPRG, 2018). The popularity of EGMs can partly be attributed to their various reinforcement design elements, such as the pleasantly rewarding sounds, losses disguised as wins, and near-misses, which reel the players in and keep them immersed. Over time, most EGM players experience a steady monetary decline into debt (Barton et al., 2017).

There is a wide range of EGMs offered that suit most everyone's preference, and, consequently, the differences between EGMs are so large it is difficult to define what a typical EGM is. Rather there are several general game types including spinning reel games with progressive prizes that increase over time, flat top spinning reels (without progressive prizes), video poker and less common games such as keno, and blackjack (Turner & Horbay, 2003). Recent years have seen the introduction of numerous table games that have been modified into single- or multiplayer EGM games (Turner, 2011b, 2019). Generally, however, EGMs can be described by their reinforcement or reward schedules in terms of volatility and return-to-player (RTP) rates (Yücel, Carter, Harrigan, van Holst, & Livingstone, 2018). RTP refers to what the player is mathematically expected to lose by playing the game, and volatility refers to the variability or fluctuation of wins and losses across multiple bets. According to Turner (2011a), volatility is a measure of the variation in potential outcome from bet to bet. It is computed using the 90% confidence interval (z = 1.65) of the theoretical standard deviation of the outcome of a bet after 10,000 spins (see Harrigan & Dixon, 2009; Kilby, Fox, & Lucas, 2005; Turner, 2011a). Both concepts, especially volatility, are difficult for players to understand and for the game providers to effectively communicate to the players (Newall, Russell, & Hing, 2021; Newall, Russell, Sharman, & Walasek, 2021).

Turner (2011a) shows that commercial games are characterized by different combinations of volatility and RTP rates with three main types: (1) lotteries, which have significant volatility including a number of small prizes, a very rare enormous prize and a low RTP rate, (2) multiple level prize games such as slot machines and video poker, which have both small and large prizes, and (3) even money games that have a very low volatility and relatively high RTP rates. There are also variations in between these game types including volatility-increasing side bets in blackjack, or a game of daily keno that allows players to choose any combination of bets from low volatility with frequent wins, to high volatility for the grand prize.

Existing research is inconclusive on what type of reinforcement schedules players generally prefer, though the topic has been extensively discussed (e.g., Delfabbro, King, & Parke, 2023; Newall, Byrne, Russell, & Rockloff, 2022; Newall, Walasek, & Ludvig, 2022; Newall, Walasek, Ludvig, & Jenkins, 2022). Turner (2011a) suggests that different players prefer different configurations of rewards: some prefer the wild ride of a moderate volatility game (EGM), others like to dream of the very rare grand prize (lottery), and still others prefer the high frequency wins of low volatility games (blackjack, baccarat). Some games such as roulette and craps provide options for moderate and low volatility games. This is also the case with many EGMs where players can choose between a more volatile experience by betting heavily on only one line and a less volatile experience by covering all possible lines (Turner & Shi, 2015). The gaming industry offers games that meet the preferred reward schedule for many different individuals.

While the players' game experience is influenced by RTP rates and volatility, the players are often unable to tell merely by playing what the game's reward schedule is (Turner, 2011a). It is possible for a player to reverse engineer the actual pay structure, but it would take careful observation of all outcomes over numerous bets (for an example, see wizardofodds.com). In other words, it is often not clear to the players what kind of results they might expect should they continue playing the game for an extended period. Statistical simulations are an efficient way to gain insights into the volatility profiles of long-term play across hundreds of players in slow-, medium-, and fast-paced gambling. For example, Turner (2011a) used simulations to illustrate and demystify gambling games to reduce common misunderstandings, and to explore variability in betting results over the short and long-term depending on the prize structure of the game.

In this paper, we build on earlier simulation work, but instead of focusing on multiple different EGMs to capture their diversity (as in Turner, 2011a), we use a simplified simulated game to illustrate how changes in the prize structure, primarily the main prize, affect patterns of profit and loss all else being equal. Specifically, we illustrate patterns of short- and long-term volatility, including the proportion of winning players and the likelihood of winning streaks, in simulated games with (near) identical RTP rates, identical win frequencies but different win size distributions. That is, the probabilities of each win are the same, as are the overall RTP rates, but the individual win sizes differ across three levels of low, medium, and high volatility, defined primarily by the size of the main prize.

As we will show, these differences in game configurations have significant effects on patterns of profit and loss. Moreover, we argue these patterns are relevant with respect to various cognitive biases in gambling behaviour. Early wins and winning streaks for example are more commonly reported in people with gambling problems (Turner, Littman-Sharp, & Zangeneh, 2006, 2008c). Problem gamblers commonly hold erroneous beliefs about random chance (Ejova, Delfabbro, & Navarro, 2015; Goodie & Fortune, 2013; Leonard, Williams, & Vokey, 2015; Toneatto, Blitz-Miller, Calderwood, Dragonetti, & Tsanos, 1997), and people in general hold biases to frequent or recent information (Tversky & Kahneman, 1990). Other erroneous beliefs include the gambler's fallacy, the belief in luck, the illusion of control, insensitivity to sample sizes, the hot hand fallacy, and base rate neglect (Ejova et al., 2015; Gilovich, Vallone, & Tversky, 1985; Goodie & Fortune, 2013; Leonard et al., 2015; Leonard & Williams, 2016; Turner et al., 2022). These errors are not limited to problem gamblers, but there is ample evidence that problem gamblers hold more erroneous beliefs (Turner et al., 2022).

The results from the simulations will help us understand how fast-paced gambling games with the same RTP rates and even the same win frequencies can yield different patterns of results across individuals. We discuss the potential impact of these differences for risk of gambling harm via various known cognitive biases, and their implications for responsible gambling communication.

Method

Sample and game details

Our samples are fully based on simulations of a hypothetical video poker game. The simulated game is a simplified form of video poker where the “players” are assumed to play 5 rounds per minute for N rounds with a constant 1 euro bet size. The return to player (RTP) on optimal strategy is set at ∼90%, which has been shown to be roughly the international average RTP for EGMs (Schwartz, 2013; Woolley, Livingstone, Harrigan, & Rintoul, 2013). In other words, for every 1 euro bet made, the players are expected to lose 10 cents on average when playing with optimal strategy.

We will treat the simulated game as if there was no skill involved. In recent versions of video poker, the game suggests the best cards to hold for the player, essentially removing the skill element from the game; if people follow the suggested cards, they are playing an optimal strategy. There are three types of games simulated based on volatility level: low, medium, and high.1 The volatility level -based payouts, their probabilities, and the (near-identical) RTPs are presented in Table 1. Commercial video poker pay tables vary from game to game with larger prizes offered for less common outcomes. The three pay tables given in Table 1 are not identical to any specific commercial game, but similar (as a reference, we used video poker odds reported by the online gaming website pinnacle.com).

Table 1.

Volatility level-based payouts, their probabilities and RTPs

HandProbability (%)Payout (euros)
Low volatilityMedium volatilityHigh volatility
Royal flush0.0021005002,000
Straight flush0.011506550
Four of a kind0.236304533
Full house1.151151314
Flush1.101877
Straight1.123765
Three of a kind7.445333
Two pair12.928222
Nothing76.003000
RTP89.955%89.934%89.965%

Note. RTP = Return to player.

Based on the above payouts and probabilities, we simulated the winnings across N rounds of play and P simulated players. Here were report the results for iterated simulations for P = 100 and Ns = 150, 300, 3,000, 9,000, 15,000, 22,500 or 30,000, which equal 0.5, 1, 10, 30, 50, 75, and 100 h of play, respectively. We provide visualizations of example simulations. All simulations were run using R (version 4.2.1, R Core Team, 2022), and the code is freely available at https://doi.org/10.6084/m9.figshare.24131466.

We also calculate the mean- and standard deviation values for proportion of winning players after N rounds played for all three levels of volatility. This is done by rerunning the simulations 500, 100, 75 or 50 times depending on the number of rounds being simulated and computing time requirements.

Finally, we use a custom sliding-window regression analysis to evaluate the proportion of winning streaks for each simulated player. For each player, betting rounds were regressed on euros won/lost for width-300 (one hour of play) sliding windows: regression models were fit for betting rounds 1 through 301, then for rounds 2 through 302, and so on, until rounds N–300 through N. This yielded N–299 separate slope coefficients for each simulated player, which represent the linear trends (winning trend vs. losing trend) of a series of overlapping but progressing one-hour game periods. The proportion of positive slope coefficients was calculated for each player, and these were averaged across all players, resulting in an overall estimate of the average proportion of time players were having a winning streak (and the standard deviation of this proportion). For proof-of-concept, see Fig. 2. Winning streaks may include both wins and losses, but the overall trend of wins and losses will be positive (i.e., more wins than losses on average over a specific period).

Results

The mean number of players (among 100 simulated players, thus also representing the mean proportion of players) who are winning after 0.5, 1, 10, 30, 50, 75 or 100 h of play depends heavily on the game's volatility level. Table 2 and Fig. 1 present the proportion of profiting players across three volatility levels (low, medium, and high) and length of play in hours. As can be seen in Fig. 1, the proportion of players in profit (“lucky winners”) significantly depends on volatility. For the high volatility version of the game, on average, 5.16% of the players were winning even after 100 h of continued play. Importantly, these differences are not explained by RTP rates or win frequencies, which are near-identical across volatility levels.

Table 2.

Mean proportion (%) of players (standard deviation in brackets) in profit separately for varying length of play and volatility of the simulated video poker game

VolatilityHours of play
0.5110305075100
Low28.72 (4.56)23.52 (4.14)2.03 (1.39)0.02 (0.14)0 (0)0 (0)0 (0)
Medium27.8 (4.55)23.74 (4.15)7.11 (2.56)1.46 (1.25)0.31 (0.54)0.06 (0.24)0 (0)
High22.67 (4.06)16.76 (3.71)6.33 (2.45)16.37 (4.06)11.68 (3.41)7.38 (2.31)5.16 (2.06)

Note. The return to player rate is near-identical (about 90%) across volatility levels.

Fig. 1.
Fig. 1.

Mean proportion (%) of players in profit across three levels of game volatility (low, medium, high)

Note. Error bars represent 95% confidence intervals. The return to player rate is near-identical (about 90%), and the win frequencies are identical across volatility levels.

Citation: Journal of Behavioral Addictions 12, 3; 10.1556/2006.2023.00048

In terms of winning streak proportions, in the low, medium, and high volatility simulations, the average proportions of one-hour upswings experienced were 26.3, 25.65 and 18.07% (SDs 10.7, 12.2 and 9%), respectively. The difference between low and medium is a small effect size (Cohen's d = 0.056); the effect size of medium and high and low and high are large effect sizes (Cohen's ds = 0.83 and 0.70 respectively). Winning streak proportions were calculated only for 10-h (3,000 round) periods across 100 simulated players (which, for the winning streak calculations, is the same as simulating 6,000 rounds across 50 players, or 12,000 rounds for 25 players, and so on). This process was repeated three times, and the results were pooled. Thus, upswings were 7–8% more frequent in the low and medium volatility games, compared with the high volatility game, despite the high volatility games having, on average, a higher proportion of winning players. Figure 2 visualizes the winning streak modelling for two example simulations as a proof-of-concept. As can be seen in Fig. 2, upwards trending periods are colored in shades of blue, and downwards trending periods in shades of red, while periods without clear up- or downwards trends are colored in shades of light blue.

Fig. 2.
Fig. 2.

Proof of concept for the sliding-window regression for upswing detection

Note. The curves represent two individual simulated players (playing the low-volatility version of the simulated game), with upswings and downswings colored based on the sliding-window regression modelling output. Shades of blue and red denote model-predicted upswing and downswing periods, respectively. Shades of light blue denote periods of no clear up- or downswings. The streak strength values (ranging from −0.25 to 0.25 in this example) refer to the unstandardized sliding-window regression model coefficients and can be interpreted as the linear slope steepness at a given time point.

Citation: Journal of Behavioral Addictions 12, 3; 10.1556/2006.2023.00048

As can be seen in Fig. 3A–C, playing the low volatility version of the game (top left panel), most players are losing after having played the game for 10 h. For medium and high volatility (top right and bottom left panels), while most players are losing after having played game for 10 h, there are quite a few who are still winning. Rerunning these simulations 500 times (i.e., calculating 500 figures as seen in Fig. 3A–C), the mean numbers of players (SD in brackets) who are still winning after 10 h are 2.032 (1.395) for low-, 7.11 (2.561) for medium-, and 6.332 (2.45) for high volatility.

Fig. 3.
Fig. 3.

A–C. Simulations for 10 h of play (3,000 rounds of betting)

Note. Example simulations for a) low, b) medium, and c) high volatility and 3,000 rounds (10 h of play) for 100 players, with the “luckiest” and “unluckiest” players highlighted in blue and red color, respectively. The red dashed line is the expected value for all players and represents a return to player rate of 89.955% (low volatility), 89.934% (medium volatility) and 89.965% (high volatility).

Citation: Journal of Behavioral Addictions 12, 3; 10.1556/2006.2023.00048

For simulations of 30 h of play we do not visualize the low volatility version of the game since it is very unlikely to have made a profit after 9,000 rounds with low volatility. For low volatility, the mean number of players winning after 30 h of play is 0.02 with a SD of 0.14. Simulations for medium and high volatility are visualized below. Figure 4A and B shows that as hours of play increase, the number of players still winning approaches zero; though after 30 h of play, there are quite a few more “winners” in the high volatility game compared with medium volatility. Rerunning these simulations 100 times (i.e., calculating 100 figures as seen in Fig. 4A and B), the mean numbers of players (SD in brackets) who are still winning after 30 h are 1.46 (1.25) for medium-, and 16.37 (4.06) for high volatility.

Fig. 4.
Fig. 4.

A and B. Simulations for 30 h of play (9,000 rounds of betting)

Note. Example simulations for a) medium, and b) high volatility and 9,000 rounds (30 h of play) for 100 players, with the “luckiest” and “unluckiest” players highlighted in blue and red color, respectively. The red dashed line is the expected value for all players and represents a return to player rate of 89.934% (medium volatility) and 89.965% (high volatility).

Citation: Journal of Behavioral Addictions 12, 3; 10.1556/2006.2023.00048

Figure 5A and B shows that after 100 h of play, it is extremely unlikely to still be in profit when playing the medium volatility version of the game. In the high volatility simulations about 5% of players are still likely to be winning. Rerunning these simulations 50 times (i.e., calculating 50 figures as seen in Fig. 5A and B), the mean numbers of players (SD in brackets) who are still winning after 100 h is 5.16 (2.06) for high volatility, and 0 for medium validity.

Fig. 5.
Fig. 5.

A and B. Simulations for 100 h of play (30,000 rounds of betting)

Note. Example simulations for a) medium, and b) high volatility and 30,000 rounds (100 h of play) for 100 players, with the “luckiest” and “unluckiest” players highlighted in blue and red color, respectively. The red dashed line is the expected value for all players and represents a return to player rate of 89.934% (medium volatility) and 89.965% (high volatility).

Citation: Journal of Behavioral Addictions 12, 3; 10.1556/2006.2023.00048

Discussion

We investigated the role of prize volatility on patterns of profit and loss, when controlling for RTP rates and win frequency, using simulations of a simplified video poker game. Results indicate that both lower and medium volatility games have a higher percentage of wins in the (very) short term and a relatively high number of winning streaks (26.3 and 25.7%, respectively), whereas high volatility games are characterised by fewer winning streaks (18.1%), but a larger proportion of individuals in profit after 30 h of play.

The influence of cognitive biases on the development of problematic gambling behaviour is well established (Blaszczynski & Nower, 2002; Devos et al., 2020), yet such biases do not exist in a vacuum. Rather, they stem from a range of flawed or maladaptive cognition, including misinterpretation and misattribution among others (e.g., Donati et al., 2018; Ejova & Ohtsuka, 2020; Ledgerwood et al., 2020). It is likely that the game experiences can contribute to the development of cognitive biases, either at the point of origin or by reinforcing pre-existing biases. The erroneous belief with the most relevance for this paper is the gambler's fallacy. Though often difficult for players to realize, random chance does not correct itself and short-term outcome variation such as losing streaks do not predict future wins. Rather over the long term the house edge emerges, as clearly shown by the simulations. Moreover, short periods of play may lead to a distorted sense of players' long-term chances of winning (as detailed in Turner, 2011a). Winning over the short term is relatively frequent (in our simulations, almost 30% of the players are winning after playing for 30 min) but cumulative sessions significantly reduce the likelihood of coming out ahead.

The longer winning streaks evident in low and medium volatility games may reinforce overconfidence and illusion of control, inflated perception of skill, and consequently, superstitious behaviour. The illusion of control is the belief that one's actions can influence random events (Leonard & Williams, 2016). The gambling industry encourages this belief with stop buttons, bonus rounds with choices, or by allowing players to choose their own numbers or the number of lines they play. Selecting more lines does change the game experience by decreasing the volatility or their outcome (Turner & Shi, 2015). Covering more buttons decreases volatility and increases the hit rate of the game by introducing losses disguised as wins (Dixon, Harrigan, Sandhu, Collins, & Fugelsang, 2010); it also mitigates the fear of missing out on combinations on other lines that were not bet on. Furthermore, the upswings in fortune may help convince players in low volatility games that the game can be beaten, with the upswing being misattributed to either skilled play or an unconnected “lucky” action, as imagined by the individual player, or that they are “learning” the machine (e.g., Ohtsuka, 2013; Zhou et al., 2012).

It is noteworthy that in addition to EGMs this potential consequence of low volatility structure can be extended to other commercially available low volatility games (e.g., baccarat, blackjack) which often have a lower house edge (higher RTP), and which also require higher minimum bets (Turner, 2011a). The higher RTP in even money games (baccarat) compensates for absence of a big win. As a result, there is often a trade-off between the potential big win (high volatility) and winning streaks. Given the lower house edge and larger bets involved in low volatility games, the result can be wild swings of fortune during the long winning streaks despite low volatility.

In respect to higher volatility games, the bigger individual wins and higher proportion of winning players may reinforce an illusion about “others' wins” and bias players' memory; others' big wins are promoted, both by individuals and the operators, thereby increasing visibility and giving rise to unrealistic expectations (Binde, 2014). Furthermore, individuals' big wins are remembered while constant small losses are not (or they are disguised as somewhat inconsequential wins; Thaler & Johnson, 1990), and therefore, those who do not win the big prize continue to lose even more than the RTP rate suggests, while the winners may have inflated sense of profitability and an inaccurate perception of odds of winning. This, in turn, may result in chasing after losses or wins (Chen, Doekemeijer, Noël, & Verbruggen, 2022; Thaler & Johnson, 1990).

When assessing the potential association between volatility and gambling harm, the question is one of which configuration is more dangerous, games characterised by more winning streaks with smaller wins, or infrequent bigger wins? Endorsing a greater number of cognitive biases related to gambling has been found to be positively associated with the likelihood of developing problematic gambling behaviours (Ejova et al., 2015; Goodie & Fortune, 2013; Leonard et al., 2015; Turner et al., 2022). However, no specific cognitive biases have been found to be especially influential in the development of problematic behaviours, with different individuals endorsing different combinations of biases. It is likely that the two profiles identified above have a similar relationship in that they are more likely to contribute to problematic gambling behaviours for different types of players; it is a reasonable assumption that the more volatile EGM games appeal to people who dream about the big win in the same way as lotteries do, while low volatility games appeal more to those motivated by more prolonged play and, potentially, perception of skilled play. Part of the appeal of different game configurations, therefore, is a trade-off between a focus on the grand prize (higher volatility) or a focus on the short-term win and winning streaks (lower volatility). As a result, the relationship between volatility and problem gambling is not a simple one, but dependent on player preferences.

Volatility is just one aspect of game design and experience, indeed, other structural characteristics, such as ability to reinvest winnings, or contextual factors like presence of alcohol and escapism may subsume or reduce the overall impact of volatility (see also Percy, Tsarvenkov, Dragicevic, Delfabbro, & Parke, 2021). For example, lotteries are extremely volatile but commonly recognised as being among the least problematic forms of gambling (Castrén, Perhoniemi, Kontto, Alho, & Salonen, 2018; Salonen, Kontto, Perhoniemi, Alho, & Castrén, 2018), with their impact mitigated by such structural characteristics as event frequency and ease of reinvesting winnings (Ariyabuddhiphongs, 2011). EGMs are perhaps the perfect balance of volatility and speed to maximize addictiveness; the combination of reinforced cognitive biases, the fast pace, and automatic reinvestment of wins being potential explanations of EGMs' association with increased experience of gambling-related harms. Volatility, therefore, is likely to be more indirectly connected to problem gambling than being a direct predictor of gambling harm experienced by individuals, for example through the etiology of maladaptive cognitions, with the effects being enhanced or degraded through combining volatility with different levels of payback and bet size. Moreover, different combinations of bet size, volatility and RTP rates may appeal to, and be addictive to, different people.

The relationship between volatility and problem gambling is further confused by the lack of readily available, intuitively understandable information on RTP rates. Indeed Turner (2011a) argues that in all cases commercial games are set up to make it hard for the players to realize the house edge (even if it is textually described as a percentage). Lotteries have a substantial house edge and are characterised by a combination of extreme volatility and well advertised winners (availability heuristic). Alternatively, table games have a small house edge, large bets, a strong illusion of skill and low volatility which support the illusion of control. EGMs, however, have enough volatility to hide a moderate house edge (e.g., 10%), but still are low enough in volatility to produce winning streaks, potentially influencing a range of cognitive biases (misattribution of skill vs. luck, superstitious behaviours). Our current results add to the existing body of literature informing of the ways in which game design can obscure the long-term outcomes for players, specifically how a series of small wins experienced as streaks mask long term losses (Turner & Shi, 2015).

Limitations and future directions

Given the simplicity of our simulated game, it is unclear how well the results generalize across the different varieties of actual gambling games. The finding that winning streaks are more common for low volatility games is interesting and novel. There are games that are relatively low in volatility (such as multi line games covering all the lines), and their appeal may be partly linked with longer winning streaks. Still, the parameters of actual games should be tested to assess whether our current results do generalize. In this paper we have visualized simulations with 10 or more hours of play. Most people play for short periods of time, but go back after short sessions, which results in more cumulative hours played. Our simulations are applicable not only for “non-stop” playing but also for accumulated hours of play over longer time periods (in terms of multiple sessions over many days, months, or years). Thus, the outcome of 30 h of play would be essentially the same if done in 10 sets of three hours, in two sets of 15 h, or in one set of 30 h.

To further flesh out the nature of EGM winning streaks, future studies should program actual EGM games and measure their streak frequency, length, and intensity across different game decisions (e.g., covering a single line, or all lines). Gamblers could also be interviewed on their gambling habits and experiences of winning streaks, and whether winning streaks are experienced more frequently when playing low volatility games.

Simulations also have significance for games with an element of skill (e.g., sports betting, poker), but the topic is severely under-studied. Recent developments in online sports betting, including in-play betting and combination bets (Parke & Parke, 2019) have resulted in increased experience of gambling harm. It may be that these products are becoming more akin to EGMs than traditional forms of sports betting (see also Newall, Russell, & Hing, 2021; Newall, Russell, Sharman et al., 2021). However, few studies have explored the volatility of sports betting, likely because there is no standardized gambling experience (other than maybe parlay bets) to study. That is, people can make a variety of different within game bets so each player's experience (and the volatility) would be different. Sports betting also theoretically has an element of skill, though this is mitigated by the way the industry sets the odds or the line. Studies of sports gamblers suggest they typically overestimate the role of skill in the game (see Cantinolli, Ladouceur, Jacques, 2014; Mercier et al., 2018). To our knowledge there are very few existing works that apply simulations to understand skill in sports betting, which would be an intriguing venue for future work.

Based on simulations, Palomäki, Laakasuo, Cowley, and Lappi (2020) argued that in poker, equally skilled players (based on pre-defined theoretical long-term win rates and their standard deviations) will likely have widely different results over tens or even hundreds of thousands of hands played. The ability to accurately recognize one's “true skill” is profoundly masked by outcome variability, and simulations are an excellent way to highlight this. In addition, skill in a poker game as well as other skilled games is always relative to the skill of the other players. That is, someone with mediocre skill will likely win against novice players but be easily beaten by highly skilled players (Turner & Fritz, 2001).

Finally, the graphical output presented herein offer a comprehensible and easy to access method by which such information can be communicated to EGM players, thereby illustrating the overall futility of the game. Exposing individuals to the experience of random chance was shown to effectively improve their understanding of it (Donati, Primi, & Chiesi, 2014; Primi & Donati, 2022; Turner, Macdonald, & Somerset, 2008, Turner, Macdonald, Bartoshuk, & Zangeneh, 2008). Indeed, such methods have been found to be effective in altering players' perceptions and improving overall understanding of how slot machine games operate (Newall, Russell, & Hing, 2021; Newall, Russell, Sharman et al., 2021; Newall, Byrne et al., 2022; Newall, Walasek, & Ludvig, 2022; Newall, Walasek, Ludvig et al., 2022; Turner, Robinson, Harrigan, Ferentzy, & Jindani, 2018). Utilising the graphical output of this work (see also Turner et al., 2018) would build upon recent work examining effectiveness of gambling messages which include information about volatility and RTP, offering an alternative strategy to traditional textual messaging (Newall, Byrne et al., 2022; Newall, Walasek, & Ludvig, 2022; Newall, Walasek, Ludvig et al., 2022). In addition to public, or untargeted messaging, they may also be a useful aid in more targeted treatment interventions, for example those which address maladaptive cognitions.

Funding sources

Authors' JP and SC daily work at the Finnish Institute for Health and Welfare is funded by the Ministry of Social Affairs and Health, Finland, within the objectives of the §52 Appropriation of the Lotteries Act. The funder had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. Author JM received funding from the Academy of Finland via the Centre of Excellence in Game Cultures, University of Turku (Grant 353268). Within the previous three years he has been awarded a personal grant from the Finnish Foundation for Alcohol Studies (Alkoholitutkimussäätiö) and been part of a successful project grant application from the Kone Foundation.

Authors' contribution

JP: Conceptualization (lead); formal analysis (lead); methodology (lead); visualization (lead); writing—original draft (equal); writing—review and editing (equal). NT: Conceptualization (supporting); formal analysis (supporting); methodology (supporting); writing—original draft (equal); writing—review and editing (equal). JM: Conceptualization (supporting); writing—original draft (supporting); writing – review and editing (supporting). SC: Conceptualization (supporting); writing – original draft (supporting); writing—review and editing (supporting). All authors had full access to the (simulated) data and take responsibility for the integrity of the data and the accuracy of the data analysis.

Conflicts of interest

Authors JP and NT have no conflicts of interest. Author SC works a part time private practitioner clinical psychologist at Addiktum Clinic Helsinki, Finland, treating mainly individuals with addiction problems, and at Mehiläinen Medical Center, Forum Helsinki, where she offers treatments to various psychological issues. She is a clinical advisor to the Canadian company Alavida, Vancouver (remote/internet treatment for alcohol disorder). She also trains professionals to treat gambling disorder with evidence-based methods as a part of her duty at the Finnish Institute for Health and Welfare, and addictions in general privately. She has received fees from Helsinki University, Tampere City, Vocational school Stadi, Lundbeck, the Finnish Association of Addiction Medicine, the Finnish Association on Intellectual and Developmental Disabilities (FAIDD), and Mehiläinen for her lectures on behavioural addictions and for training professionals, and writer's fees from the Finnish Medical Society Duodecim, Finnish Medical Journal and Myllyhoitoyhdistys ry. She received fees from Svenska Spel (Sweden) for evaluating grant proposals, and Tampere University for preliminary examination of PhD work. She declares no conflict of interest in relation to this manuscript. JM has received fees from the Finnish NGO EHYT and the Euro Lotteries association for speaking on the convergence between video games and gambling.

References

  • Ariyabuddhiphongs, V. (2011). Lottery gambling: A review. Journal of Gambling Studies, 27, 1533. https://doi.org/10.1007/s10899-010-9194-0.

    • Search Google Scholar
    • Export Citation
  • Barton, K. R., Yazdani, Y., Ayer, N., Kalvapalle, S., Brown, S., Stapleton, J., … Harrigan, K. A. (2017). The effect of losses disguised as wins and near misses in electronic gaming machines: A systematic review. Journal of Gambling Studies, 33, 12411260. https://doi.org/10.1007/s10899-017-9688-0.

    • Search Google Scholar
    • Export Citation
  • Binde, P. (2014). Gambling advertising: A critical research review. London: Responsible gambling trust.

  • Blaszczynski, A., & Nower, L. (2002). A pathways model of gambling and problem gambling. Addiction, 97, 487499. https://doi.org/10.1046/j.1360-0443.2002.00015.x.

    • Search Google Scholar
    • Export Citation
  • Browne, M., Delfabbro, P., Thorne, H. B., Tulloch, C., Rockloff, M. J., Hing, N., … Stevens, M. (2023). Unambiguous evidence that over half of gambling problems in Australia are caused by electronic gambling machines: Results from a large-scale composite population study. Journal of Behavioral Addictions, 12(1), 182193. https://doi.org/10.1556/2006.2022.00083.

    • Search Google Scholar
    • Export Citation
  • Canadian Partnership for Responsible Gambling (CPRG) (2018). Canadian gambling digest 2016–2017. Canadian Partnership for Responsible Gambling (CPRG).

    • Search Google Scholar
    • Export Citation
  • Cantinotti, M., Ladouceur, R., & Jacques, C. (2004). Sports betting: Can gamblers beat randomness? Psychology of Addictive Behaviors, 18(2), 143147. https://doi.org/10.1037/0893-164X.18.2.143.

    • Search Google Scholar
    • Export Citation
  • Castrén, S., Perhoniemi, R., Kontto, J., Alho, H., & Salonen, A. H. (2018). Association between gambling harms and game types: Finnish population study. International Gambling Studies, 18(1), 124142. https://doi.org/10.1080/14459795.2017.1388830.

    • Search Google Scholar
    • Export Citation
  • Chen, Z., Doekemeijer, R. A., Noël, X., & Verbruggen, F. (2022). Winning and losing in online gambling: Effects on within-session chasing. Plos One, 17(8), e0273359. https://doi.org/10.1371/journal.pone.0273359.

    • Search Google Scholar
    • Export Citation
  • Delfabbro, P., King, D., & Parke, J. (2023). The complex nature of human operant gambling behaviour involving slot games: Structural characteristics, verbal rules and motivation. Addictive Behaviors, 137, 107540. https://doi.org/10.1016/j.addbeh.2022.107540.

    • Search Google Scholar
    • Export Citation
  • Devos, M. G., Clark, L., Bowden-Jones, H., Grall-Bronnec, M., Challet-Bouju, G., Khazaal, Y., … Billieux, J. (2020). The joint role of impulsivity and distorted cognitions in recreational and problem gambling: A cluster analytic approach. Journal of Affective Disorders, 260, 473482. https://doi.org/10.1016/j.jad.2019.08.096.

    • Search Google Scholar
    • Export Citation
  • Dixon, M. J., Harrigan, K. A., Sandhu, R., Collins, K., & Fugelsang, J. A. (2010). Losses disguised as wins in modern multi‐line video slot machines. Addiction, 105(10), 18191824. https://doi.org/10.1111/j.1360-0443.2010.03050.x.

    • Search Google Scholar
    • Export Citation
  • Donati, M. A., Chiesi, F., Iozzi, A., Manfredi, A., Fagni, F., & Primi, C. (2018). Gambling-related distortions and problem gambling in adolescents: A model to explain mechanisms and develop interventions. Frontiers in Psychology, 8, 2243. https://doi.org/10.3389/fpsyg.2017.02243.

    • Search Google Scholar
    • Export Citation
  • Donati, M. A., Primi, C., & Chiesi, F. (2014). Prevention of problematic gambling behavior among adolescents: Testing the efficacy of an integrative intervention. Journal of Gambling Studies, 30, 803818. https://doi.org/10.1007/s10899-013-9398-1.

    • Search Google Scholar
    • Export Citation
  • Ejova, A., Delfabbro, P. H., & Navarro, D. J. (2015). Erroneous gambling-related beliefs as illusions of primary and secondary control: A confirmatory factor analysis. Journal of Gambling Studies, 31(1), 133160. https://doi.org/10.1007/s10899-013-9402-9.

    • Search Google Scholar
    • Export Citation
  • Ejova, A., & Ohtsuka, K. (2020). Erroneous gambling-related beliefs emerge from broader beliefs during problem-solving: A critical review and classification scheme. Thinking & Reasoning, 26(2), 159187. https://doi.org/10.1080/13546783.2019.1590233.

    • Search Google Scholar
    • Export Citation
  • Gilovich, T., Vallone, R., & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive Psychology, 17(3), 295314. https://doi.org/10.1016/0010-0285(85)90010-6.

    • Search Google Scholar
    • Export Citation
  • Goodie, A. S., & Fortune, E. E. (2013). Measuring cognitive distortions in pathological gambling: Review and meta-analyses. Psychology of Addictive Behaviors, 27(3), 730. https://doi.org/10.1037/a0031892.

    • Search Google Scholar
    • Export Citation
  • Harrigan, K. A., & Dixon, M. (2009). PAR sheets, probabilities, and slot machine play: Implications for problem and non-problem gambling. Journal of Gambling Issues, (23). https://doi.org/10.4309/jgi.2009.23.5.

    • Search Google Scholar
    • Export Citation
  • Järvinen-Tassopoulos, J., Marionneau, V., & Nikkinen, J. (2021). Gambling harms caused by electronic gambling machines should be prevented with state control. Nordic Studies on Alcohol and Drugs, 38(6), 631639. https://doi.org/10.1177/14550725211034030.

    • Search Google Scholar
    • Export Citation
  • Kilby, J., Fox, J., & Lucas, A. F. (2005). Casino operations management. New York, NY: John Wiley & Sons.

  • Ledgerwood, D. M., Dyshniku, F., McCarthy, J. E., Ostojic-Aitkens, D., Forfitt, J., & Rumble, S. C. (2020). Gambling-related cognitive distortions in residential treatment for gambling disorder. Journal of Gambling Studies, 36, 669683. https://doi.org/10.1007/s10899-019-09895-4.

    • Search Google Scholar
    • Export Citation
  • Leonard, C. A., & Williams, R. J. (2016). The relationship between gambling fallacies and problem gambling. Psychology of Addictive Behaviors, 30(6), 694704. https://doi.org/10.1037/adb0000189.

    • Search Google Scholar
    • Export Citation
  • Leonard, C. A., Williams, R. J., & Vokey, J. (2015). Gambling fallacies: What are they and how are they best measured? Journal of Addiction Research and Therapy, 6(4), 256. https://doi.org/10.4172/2155-6105.1000256.

    • Search Google Scholar
    • Export Citation
  • Mercier, J., Sévigny, S., Jacques, C., Goulet, A., Cantinotti, M., & Giroux, I. (2018). Sports bettors: A systematic review. Journal of Gambling Issues, (38), 203236. https://doi.org/10.4309/jgi.2018.38.11.

    • Search Google Scholar
    • Export Citation
  • Newall, P. W., Byrne, C. A., Russell, A. M., & Rockloff, M. J. (2022). House-edge information and a volatility warning lead to reduced gambling expenditure: Potential improvements to return-to-player percentages. Addictive Behaviors, 130, 107308. https://doi.org/10.1016/j.addbeh.2022.107308.

    • Search Google Scholar
    • Export Citation
  • Newall, P. W. S., Russell, A. M. T., & Hing, N. (2021). Structural characteristics of fixed-odds sports betting products. Journal of Behavioral Addictions, 10(3), 371380. https://doi.org/10.1556/2006.2021.00008.

    • Search Google Scholar
    • Export Citation
  • Newall, P. W., Russell, A. M., Sharman, S., & Walasek, L. (2021). Recollected usage of legal youth gambling products: Comparisons between adult gamblers and non-gamblers in the UK and Australia. Addictive Behaviors, 114, 106685. https://doi.org/10.1016/j.addbeh.2020.106685.

    • Search Google Scholar
    • Export Citation
  • Newall, P. W., Walasek, L., & Ludvig, E. A. (2022). Risk communication improvements for gambling: House-edge information and volatility statements. Psychology of Addictive Behaviors, 36(4), 358. https://doi.org/10.1037/adb0000695.

    • Search Google Scholar
    • Export Citation
  • Newall, P. W., Walasek, L., Ludvig, E. A., & Jenkins, M. (2022). How best to improve upon return- to-player information in gambling? A comparison of two approaches in an Australian sample. Experimental Results, 3, e20. https://doi.org/10.1017/exp.2022.21.

    • Search Google Scholar
    • Export Citation
  • Ohtsuka, K. (2013). Views on luck and winning, self-control, and gaming service expectations of culturally and linguistically diverse Australian poker machine gamblers. Asian Journal of Gambling Issues and Public Health, 3(1), 117. https://doi.org/10.1186/2195-3007-3-9.

    • Search Google Scholar
    • Export Citation
  • Palomäki, J., Laakasuo, M., Cowley, B. U., & Lappi, O. (2020). Poker as a domain of expertise. Journal of Expertise, 3(2), 6687.

  • Parke, A., & Parke, J. (2019). Transformation of sports betting into a rapid and continuous gambling activity: A grounded theoretical investigation of problem sports betting in online settings. International Journal of Mental Health and Addiction, 17(6), 13401359. https://doi.org/10.1007/s11469-018-0049-8.

    • Search Google Scholar
    • Export Citation
  • Percy, C., Tsarvenkov, K., Dragicevic, S., Delfabbro, P. H., & Parke, J. (2021). Volatility under the spotlight: Panel regression analysis of online slots player in the UK. International Gambling Studies, 21(3), 395410. https://doi.org/10.1080/14459795.2021.1891273.

    • Search Google Scholar
    • Export Citation
  • Primi, C., & Donati, M. A. (2022). The prevention of adolescent problem gambling through probabilistic reasoning: Evidence of the intervention’s efficacy. Canadian Journal of Science, Mathematics and Technology Education, 111. https://doi.org/10.1007/s42330-022-00229-y.

    • Search Google Scholar
    • Export Citation
  • R Core Team (2022). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. URL https://www.R-project.org/.

    • Search Google Scholar
    • Export Citation
  • Salonen, A. H., Kontto, J., Perhoniemi, R., Alho, H., & Castrén, S. (2018). Gambling expenditure by game type among weekly gamblers in Finland. BMC Public Health, 18(1), 112. https://doi.org/10.1186/s12889-018-5613-4.

    • Search Google Scholar
    • Export Citation
  • Schwartz, D. G. (2013). Penny wise, player foolish? Slot-Hold regulation and consumer preference. Journal of Business Research, 66(9), 16231628. https://doi.org/10.1016/j.jbusres.2012.12.007.

    • Search Google Scholar
    • Export Citation
  • Thaler, R. H., & Johnson, E. J. (1990). Gambling with the house money and trying to break even: The effects of prior outcomes on risky choice. Management Science, 36(6), 643660. https://doi.org/10.1287/mnsc.36.6.643.

    • Search Google Scholar
    • Export Citation
  • Toneatto, T., Blitz-Miller, T., Calderwood, K., Dragonetti, R., & Tsanos, A. (1997). Brief report: Cognitive distortions in heavy gambling. Journal of Gambling Studies, 13(3), 253266. https://doi.org/10.1023/A:1024983300428.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E. (2011a). Volatility, house edge and prize structure of gambling games. Journal of Gambling Studies, 27, 607623. https://doi.org/10.1007/s10899-011-9238-0.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E. (2011b). Report from the global gaming expo, Las Vegas, November 17–19, 2009. Journal of Gambling Issues, (25). https://doi.org/10.4309/jgi.2011.25.10.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E. (2019). A report from the global gaming expo in Las Vegas, Oct. 15, 2019: Updates on innovations and gambling technology. Journal of Gambling Issues, 43.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., & Fritz, B. (2001). The effect of skilled gamblers on the success of less skilled gamblers. Journal of Gambling Issues, 5(10.4309).

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., & Horbay, R. (2003). Doubling revisited: The mathematical and psychological effect of betting strategy. Gambling Research: Journal of the National Association for Gambling Studies (Australia), 15(2), 1634.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., Jain, U., Spence, W., & Zangeneh, M., (2008c). Pathways to pathological gambling: Component analysis of variables related to pathological gambling. International Gambling Studies, 8(3), 281298. https://doi.org/10.1080/14459790802405905.

    • Search Google Scholar
    • Export Citation
  • Turner, N., Littman-Sharp, N., & Zangeneh, M. (2006). The experience of gambling and its role in problem gambling. International Gambling Studies, 6, 237266. https://doi.org/10.1080/14459790600928793.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., Maas, M. V. D., Shi, J., Liu, E., Zangeneh, M., Cool, S., … Marshall, T. E. (2022). Knowledge of random events and chance in people with gambling problems: An item analysis. International Gambling Studies, 22(3), 412431. https://doi.org/10.1080/14459795.2021.2014930.

    • Search Google Scholar
    • Export Citation
  • Turner, N., Macdonald, J., Bartoshuk, M., & Zangeneh, M. (2008b). The evaluation of a one-hour prevention program for problem gambling. International Journal of Mental Health & Addiction, 6, 238243. https://doi.org/10.1007/s11469-007-9121-5.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., Macdonald, J. & Somerset, M. (2008a). Life skills, mathematical reasoning and critical thinking: A curriculum for the prevention of problem gambling. Journal of Gambling Studies, 24, 367380. https://doi.org/10.1007/s10899-007-9085-1.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., Robinson, J., Harrigan, K., Ferentzy, P., & Jindani, F. (2018). A pilot evaluation of a tutorial to teach clients and clinicians about gambling game design. International Journal of Mental Health and Addiction, 16(1), 136149. https://doi.org/10.1007/s11469-017-9816-1.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., & Shi, J. (2015). The relationship between game volatility, house edge and prize structure of gambling games and what it tells us about gambling game design. International Journal of Computer Research, 22(2), 107.

    • Search Google Scholar
    • Export Citation
  • Tversky, A., & Kahneman, D. (1990). Judgment under uncertainty: Heuristics and biases. In P. K. Moser (Ed.), Rationality in action: Contemporary approach. Cambridge University Press.

    • Search Google Scholar
    • Export Citation
  • Woolley, R., Livingstone, C., Harrigan, K., & Rintoul, A. (2013). House edge: Hold percentage and the cost of EGM gambling. International Gambling Studies, 13(3), 388402. https://doi.org/10.1080/14459795.2013.829515.

    • Search Google Scholar
    • Export Citation
  • Yücel, M., Carter, A., Harrigan, K., van Holst, R. J., & Livingstone, C. (2018). Hooked on gambling: A problem of human or machine design? The Lancet Psychiatry, 5(1), 2021. https://doi.org/10.1016/S2215-0366(17)30467-4.

    • Search Google Scholar
    • Export Citation
  • Zhou, K., Tang, H., Sun, Y., Huang, G. H., Rao, L. L., Liang, Z. Y., & Li, S. (2012). Belief in luck or in skill: Which locks people into gambling? Journal of Gambling Studies, 28, 379391. https://doi.org/10.1007/s10899-011-9263-z.

    • Search Google Scholar
    • Export Citation
1

Here the low volatility game is still much higher in volatility compared to an even money game such as Baccarat, and the high volatility game is lower in volatility compared to a lottery or progressive gambling EGMs. The terms “low”, “medium” and “high” volatility are merely defined here with respect to one another.

  • Ariyabuddhiphongs, V. (2011). Lottery gambling: A review. Journal of Gambling Studies, 27, 1533. https://doi.org/10.1007/s10899-010-9194-0.

    • Search Google Scholar
    • Export Citation
  • Barton, K. R., Yazdani, Y., Ayer, N., Kalvapalle, S., Brown, S., Stapleton, J., … Harrigan, K. A. (2017). The effect of losses disguised as wins and near misses in electronic gaming machines: A systematic review. Journal of Gambling Studies, 33, 12411260. https://doi.org/10.1007/s10899-017-9688-0.

    • Search Google Scholar
    • Export Citation
  • Binde, P. (2014). Gambling advertising: A critical research review. London: Responsible gambling trust.

  • Blaszczynski, A., & Nower, L. (2002). A pathways model of gambling and problem gambling. Addiction, 97, 487499. https://doi.org/10.1046/j.1360-0443.2002.00015.x.

    • Search Google Scholar
    • Export Citation
  • Browne, M., Delfabbro, P., Thorne, H. B., Tulloch, C., Rockloff, M. J., Hing, N., … Stevens, M. (2023). Unambiguous evidence that over half of gambling problems in Australia are caused by electronic gambling machines: Results from a large-scale composite population study. Journal of Behavioral Addictions, 12(1), 182193. https://doi.org/10.1556/2006.2022.00083.

    • Search Google Scholar
    • Export Citation
  • Canadian Partnership for Responsible Gambling (CPRG) (2018). Canadian gambling digest 2016–2017. Canadian Partnership for Responsible Gambling (CPRG).

    • Search Google Scholar
    • Export Citation
  • Cantinotti, M., Ladouceur, R., & Jacques, C. (2004). Sports betting: Can gamblers beat randomness? Psychology of Addictive Behaviors, 18(2), 143147. https://doi.org/10.1037/0893-164X.18.2.143.

    • Search Google Scholar
    • Export Citation
  • Castrén, S., Perhoniemi, R., Kontto, J., Alho, H., & Salonen, A. H. (2018). Association between gambling harms and game types: Finnish population study. International Gambling Studies, 18(1), 124142. https://doi.org/10.1080/14459795.2017.1388830.

    • Search Google Scholar
    • Export Citation
  • Chen, Z., Doekemeijer, R. A., Noël, X., & Verbruggen, F. (2022). Winning and losing in online gambling: Effects on within-session chasing. Plos One, 17(8), e0273359. https://doi.org/10.1371/journal.pone.0273359.

    • Search Google Scholar
    • Export Citation
  • Delfabbro, P., King, D., & Parke, J. (2023). The complex nature of human operant gambling behaviour involving slot games: Structural characteristics, verbal rules and motivation. Addictive Behaviors, 137, 107540. https://doi.org/10.1016/j.addbeh.2022.107540.

    • Search Google Scholar
    • Export Citation
  • Devos, M. G., Clark, L., Bowden-Jones, H., Grall-Bronnec, M., Challet-Bouju, G., Khazaal, Y., … Billieux, J. (2020). The joint role of impulsivity and distorted cognitions in recreational and problem gambling: A cluster analytic approach. Journal of Affective Disorders, 260, 473482. https://doi.org/10.1016/j.jad.2019.08.096.

    • Search Google Scholar
    • Export Citation
  • Dixon, M. J., Harrigan, K. A., Sandhu, R., Collins, K., & Fugelsang, J. A. (2010). Losses disguised as wins in modern multi‐line video slot machines. Addiction, 105(10), 18191824. https://doi.org/10.1111/j.1360-0443.2010.03050.x.

    • Search Google Scholar
    • Export Citation
  • Donati, M. A., Chiesi, F., Iozzi, A., Manfredi, A., Fagni, F., & Primi, C. (2018). Gambling-related distortions and problem gambling in adolescents: A model to explain mechanisms and develop interventions. Frontiers in Psychology, 8, 2243. https://doi.org/10.3389/fpsyg.2017.02243.

    • Search Google Scholar
    • Export Citation
  • Donati, M. A., Primi, C., & Chiesi, F. (2014). Prevention of problematic gambling behavior among adolescents: Testing the efficacy of an integrative intervention. Journal of Gambling Studies, 30, 803818. https://doi.org/10.1007/s10899-013-9398-1.

    • Search Google Scholar
    • Export Citation
  • Ejova, A., Delfabbro, P. H., & Navarro, D. J. (2015). Erroneous gambling-related beliefs as illusions of primary and secondary control: A confirmatory factor analysis. Journal of Gambling Studies, 31(1), 133160. https://doi.org/10.1007/s10899-013-9402-9.

    • Search Google Scholar
    • Export Citation
  • Ejova, A., & Ohtsuka, K. (2020). Erroneous gambling-related beliefs emerge from broader beliefs during problem-solving: A critical review and classification scheme. Thinking & Reasoning, 26(2), 159187. https://doi.org/10.1080/13546783.2019.1590233.

    • Search Google Scholar
    • Export Citation
  • Gilovich, T., Vallone, R., & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive Psychology, 17(3), 295314. https://doi.org/10.1016/0010-0285(85)90010-6.

    • Search Google Scholar
    • Export Citation
  • Goodie, A. S., & Fortune, E. E. (2013). Measuring cognitive distortions in pathological gambling: Review and meta-analyses. Psychology of Addictive Behaviors, 27(3), 730. https://doi.org/10.1037/a0031892.

    • Search Google Scholar
    • Export Citation
  • Harrigan, K. A., & Dixon, M. (2009). PAR sheets, probabilities, and slot machine play: Implications for problem and non-problem gambling. Journal of Gambling Issues, (23). https://doi.org/10.4309/jgi.2009.23.5.

    • Search Google Scholar
    • Export Citation
  • Järvinen-Tassopoulos, J., Marionneau, V., & Nikkinen, J. (2021). Gambling harms caused by electronic gambling machines should be prevented with state control. Nordic Studies on Alcohol and Drugs, 38(6), 631639. https://doi.org/10.1177/14550725211034030.

    • Search Google Scholar
    • Export Citation
  • Kilby, J., Fox, J., & Lucas, A. F. (2005). Casino operations management. New York, NY: John Wiley & Sons.

  • Ledgerwood, D. M., Dyshniku, F., McCarthy, J. E., Ostojic-Aitkens, D., Forfitt, J., & Rumble, S. C. (2020). Gambling-related cognitive distortions in residential treatment for gambling disorder. Journal of Gambling Studies, 36, 669683. https://doi.org/10.1007/s10899-019-09895-4.

    • Search Google Scholar
    • Export Citation
  • Leonard, C. A., & Williams, R. J. (2016). The relationship between gambling fallacies and problem gambling. Psychology of Addictive Behaviors, 30(6), 694704. https://doi.org/10.1037/adb0000189.

    • Search Google Scholar
    • Export Citation
  • Leonard, C. A., Williams, R. J., & Vokey, J. (2015). Gambling fallacies: What are they and how are they best measured? Journal of Addiction Research and Therapy, 6(4), 256. https://doi.org/10.4172/2155-6105.1000256.

    • Search Google Scholar
    • Export Citation
  • Mercier, J., Sévigny, S., Jacques, C., Goulet, A., Cantinotti, M., & Giroux, I. (2018). Sports bettors: A systematic review. Journal of Gambling Issues, (38), 203236. https://doi.org/10.4309/jgi.2018.38.11.

    • Search Google Scholar
    • Export Citation
  • Newall, P. W., Byrne, C. A., Russell, A. M., & Rockloff, M. J. (2022). House-edge information and a volatility warning lead to reduced gambling expenditure: Potential improvements to return-to-player percentages. Addictive Behaviors, 130, 107308. https://doi.org/10.1016/j.addbeh.2022.107308.

    • Search Google Scholar
    • Export Citation
  • Newall, P. W. S., Russell, A. M. T., & Hing, N. (2021). Structural characteristics of fixed-odds sports betting products. Journal of Behavioral Addictions, 10(3), 371380. https://doi.org/10.1556/2006.2021.00008.

    • Search Google Scholar
    • Export Citation
  • Newall, P. W., Russell, A. M., Sharman, S., & Walasek, L. (2021). Recollected usage of legal youth gambling products: Comparisons between adult gamblers and non-gamblers in the UK and Australia. Addictive Behaviors, 114, 106685. https://doi.org/10.1016/j.addbeh.2020.106685.

    • Search Google Scholar
    • Export Citation
  • Newall, P. W., Walasek, L., & Ludvig, E. A. (2022). Risk communication improvements for gambling: House-edge information and volatility statements. Psychology of Addictive Behaviors, 36(4), 358. https://doi.org/10.1037/adb0000695.

    • Search Google Scholar
    • Export Citation
  • Newall, P. W., Walasek, L., Ludvig, E. A., & Jenkins, M. (2022). How best to improve upon return- to-player information in gambling? A comparison of two approaches in an Australian sample. Experimental Results, 3, e20. https://doi.org/10.1017/exp.2022.21.

    • Search Google Scholar
    • Export Citation
  • Ohtsuka, K. (2013). Views on luck and winning, self-control, and gaming service expectations of culturally and linguistically diverse Australian poker machine gamblers. Asian Journal of Gambling Issues and Public Health, 3(1), 117. https://doi.org/10.1186/2195-3007-3-9.

    • Search Google Scholar
    • Export Citation
  • Palomäki, J., Laakasuo, M., Cowley, B. U., & Lappi, O. (2020). Poker as a domain of expertise. Journal of Expertise, 3(2), 6687.

  • Parke, A., & Parke, J. (2019). Transformation of sports betting into a rapid and continuous gambling activity: A grounded theoretical investigation of problem sports betting in online settings. International Journal of Mental Health and Addiction, 17(6), 13401359. https://doi.org/10.1007/s11469-018-0049-8.

    • Search Google Scholar
    • Export Citation
  • Percy, C., Tsarvenkov, K., Dragicevic, S., Delfabbro, P. H., & Parke, J. (2021). Volatility under the spotlight: Panel regression analysis of online slots player in the UK. International Gambling Studies, 21(3), 395410. https://doi.org/10.1080/14459795.2021.1891273.

    • Search Google Scholar
    • Export Citation
  • Primi, C., & Donati, M. A. (2022). The prevention of adolescent problem gambling through probabilistic reasoning: Evidence of the intervention’s efficacy. Canadian Journal of Science, Mathematics and Technology Education, 111. https://doi.org/10.1007/s42330-022-00229-y.

    • Search Google Scholar
    • Export Citation
  • R Core Team (2022). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. URL https://www.R-project.org/.

    • Search Google Scholar
    • Export Citation
  • Salonen, A. H., Kontto, J., Perhoniemi, R., Alho, H., & Castrén, S. (2018). Gambling expenditure by game type among weekly gamblers in Finland. BMC Public Health, 18(1), 112. https://doi.org/10.1186/s12889-018-5613-4.

    • Search Google Scholar
    • Export Citation
  • Schwartz, D. G. (2013). Penny wise, player foolish? Slot-Hold regulation and consumer preference. Journal of Business Research, 66(9), 16231628. https://doi.org/10.1016/j.jbusres.2012.12.007.

    • Search Google Scholar
    • Export Citation
  • Thaler, R. H., & Johnson, E. J. (1990). Gambling with the house money and trying to break even: The effects of prior outcomes on risky choice. Management Science, 36(6), 643660. https://doi.org/10.1287/mnsc.36.6.643.

    • Search Google Scholar
    • Export Citation
  • Toneatto, T., Blitz-Miller, T., Calderwood, K., Dragonetti, R., & Tsanos, A. (1997). Brief report: Cognitive distortions in heavy gambling. Journal of Gambling Studies, 13(3), 253266. https://doi.org/10.1023/A:1024983300428.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E. (2011a). Volatility, house edge and prize structure of gambling games. Journal of Gambling Studies, 27, 607623. https://doi.org/10.1007/s10899-011-9238-0.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E. (2011b). Report from the global gaming expo, Las Vegas, November 17–19, 2009. Journal of Gambling Issues, (25). https://doi.org/10.4309/jgi.2011.25.10.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E. (2019). A report from the global gaming expo in Las Vegas, Oct. 15, 2019: Updates on innovations and gambling technology. Journal of Gambling Issues, 43.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., & Fritz, B. (2001). The effect of skilled gamblers on the success of less skilled gamblers. Journal of Gambling Issues, 5(10.4309).

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., & Horbay, R. (2003). Doubling revisited: The mathematical and psychological effect of betting strategy. Gambling Research: Journal of the National Association for Gambling Studies (Australia), 15(2), 1634.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., Jain, U., Spence, W., & Zangeneh, M., (2008c). Pathways to pathological gambling: Component analysis of variables related to pathological gambling. International Gambling Studies, 8(3), 281298. https://doi.org/10.1080/14459790802405905.

    • Search Google Scholar
    • Export Citation
  • Turner, N., Littman-Sharp, N., & Zangeneh, M. (2006). The experience of gambling and its role in problem gambling. International Gambling Studies, 6, 237266. https://doi.org/10.1080/14459790600928793.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., Maas, M. V. D., Shi, J., Liu, E., Zangeneh, M., Cool, S., … Marshall, T. E. (2022). Knowledge of random events and chance in people with gambling problems: An item analysis. International Gambling Studies, 22(3), 412431. https://doi.org/10.1080/14459795.2021.2014930.

    • Search Google Scholar
    • Export Citation
  • Turner, N., Macdonald, J., Bartoshuk, M., & Zangeneh, M. (2008b). The evaluation of a one-hour prevention program for problem gambling. International Journal of Mental Health & Addiction, 6, 238243. https://doi.org/10.1007/s11469-007-9121-5.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., Macdonald, J. & Somerset, M. (2008a). Life skills, mathematical reasoning and critical thinking: A curriculum for the prevention of problem gambling. Journal of Gambling Studies, 24, 367380. https://doi.org/10.1007/s10899-007-9085-1.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., Robinson, J., Harrigan, K., Ferentzy, P., & Jindani, F. (2018). A pilot evaluation of a tutorial to teach clients and clinicians about gambling game design. International Journal of Mental Health and Addiction, 16(1), 136149. https://doi.org/10.1007/s11469-017-9816-1.

    • Search Google Scholar
    • Export Citation
  • Turner, N. E., & Shi, J. (2015). The relationship between game volatility, house edge and prize structure of gambling games and what it tells us about gambling game design. International Journal of Computer Research, 22(2), 107.

    • Search Google Scholar
    • Export Citation
  • Tversky, A., & Kahneman, D. (1990). Judgment under uncertainty: Heuristics and biases. In P. K. Moser (Ed.), Rationality in action: Contemporary approach. Cambridge University Press.

    • Search Google Scholar
    • Export Citation
  • Woolley, R., Livingstone, C., Harrigan, K., & Rintoul, A. (2013). House edge: Hold percentage and the cost of EGM gambling. International Gambling Studies, 13(3), 388402. https://doi.org/10.1080/14459795.2013.829515.

    • Search Google Scholar
    • Export Citation
  • Yücel, M., Carter, A., Harrigan, K., van Holst, R. J., & Livingstone, C. (2018). Hooked on gambling: A problem of human or machine design? The Lancet Psychiatry, 5(1), 2021. https://doi.org/10.1016/S2215-0366(17)30467-4.

    • Search Google Scholar
    • Export Citation
  • Zhou, K., Tang, H., Sun, Y., Huang, G. H., Rao, L. L., Liang, Z. Y., & Li, S. (2012). Belief in luck or in skill: Which locks people into gambling? Journal of Gambling Studies, 28, 379391. https://doi.org/10.1007/s10899-011-9263-z.

    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
The author instruction is available in PDF.
Please, download the file from HERE

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

Indexing and Abstracting Services:

  • Web of Science [Science Citation Index Expanded (also known as SciSearch®)
  • Journal Citation Reports/Science Edition
  • Social Sciences Citation Index®
  • Journal Citation Reports/ Social Sciences Edition
  • Current Contents®/Social and Behavioral Sciences
  • EBSCO
  • GoogleScholar
  • PsycINFO
  • PubMed Central
  • SCOPUS
  • Medline
  • CABI
  • CABELLS Journalytics

2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Astrid MÜLLER  (Hannover Medical School, Germany)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Róbert URBÁN  (ELTE Eötvös Loránd University, Hungary)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN  (Ariel University, Israel)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Nov 2023 0 114 59
Dec 2023 0 220 53
Jan 2024 0 161 33
Feb 2024 0 214 40
Mar 2024 0 156 42
Apr 2024 0 94 8
May 2024 0 0 0