Search

Chasing refers to the escalation of betting behaviour. It is conventionally seen when losing but can also be seen after wins. Diagnostic and screening items for gambling problems describe chasing as returning 'another day' to gamble. However, gamblers may also chase within sessions, and this is particularly relevant in online gambling. This study focused on two expressions of within-session chasing: (1) increasing the bet amount, or (2) a reduced probability of quitting the session, as a function of prior losses or wins. These expressions were examined across five online gambling products: slot machines, probability games, blackjack, video poker, and roulette. Our results showed that gamblers bet more and played longer sessions after immediate losses, but they bet less and played shorter sessions when losing cumulatively. The reversed pattern in the cumulative model may be due to financial constraints. For wins, gamblers bet more after both immediate and cumulative wins, but they also played shorter sessions. Chasing patterns were qualitatively similar by game type—with limited evidence for our hypothesis that chasing would be greatest for slot machines as an established high-risk category. Overall, chasing is multi-faceted, varying across the behavioural expressions, by the immediate or cumulative timeframe of prior outcomes, and by game type. [ABSTRACT FROM AUTHOR]

The authors of the paper undertook an analysis of the regulations on the organization of a poker game under the Gambling Act. The main subject of the analysis revolves around the question to what extent state authorities should monitor and restrict the activity of poker players. The scope of the statutory restrictions on arranging and participating in poker games depends on the culture of perception of gambling in a given country. Assigning poker to the appropriate regulatory category, either as a gambling activity carrying the highest social risks in terms of public health or safety, or as a game primarily for entertainment, is by no means obvious and depends on determining such factors as the purpose of organizing the poker game and the manner in which it is played, i.e. whether players are playing against a casino or against each other. The regulatory differentiation depending on the variety of poker games is the result of years of efforts by national legislators and the jurisprudence to define and categorize the game correctly. The article cites the jurisprudence of the Constitutional Court justifying the formal and material prerequisites of the existing restrictions on the gambling services market. The authors also relied on the extensive body of case law of the Court of Justice of the European Union explicating the permissibility of restrictions on freedom of establishment and freedom to provide services in the sphere of gambling. The conclusions drawn from these reflections confirm that the current provisions of the Polish Gambling Act are part of a consistent and systematic state policy aimed at ensuring the highest possible level of consumer protection, and the introduced restrictions are proportionate and necessary. [ABSTRACT FROM AUTHOR]

Poker is a game of skill, much like chess or go, but distinct as an incomplete information game. Substantial work has been done to understand human play in poker, as well as the optimal strategies in poker. Evolutionary game theory provides another avenue to study poker by considering overarching strategies, namely rational and random play. In this work, a population of poker playing agents is instantiated to play the preflop portion of Texas Hold'em poker, with learning and strategy revision occurring over the course of the simulation. This paper aims to investigate the influence of learning dynamics on dominant strategies in poker, an area that has yet to be investigated. Our findings show that rational play emerges as the dominant strategy when loss aversion is included in the learning model, not when winning and magnitude of win are of the only considerations. The implications of our findings extend to the modeling of sub-optimal human poker play and the development of optimal poker agents. [ABSTRACT FROM AUTHOR]

Fourier ptychography (FP) technique is a promising super‐resolution tool in noninterferometric synthetic aperture research thanks to its unique capabilities for circumventing the physical limitation of space bandwidth product. However, FP imaging in the long‐range scenario suffers from field‐of‐view (FOV) attenuation due to spherical‐wave imaging characteristics, and the speckle noise of most targets further restricts the FP's application in far‐field detection. Herein, a remote FP (R‐FP) technique is proposed to address the issues by combining the idea of quasi‐plane wave illumination and the total variation‐guided filtering (TVGF) method. Compared with conventional macroscopic Fourier ptychographic imaging, R‐FP overcomes the contradiction between FOV and detection range and mitigates the effect of speckle noise by utilizing the TVGF method, as demonstrated by high‐resolution imaging of various targets (including United States Air Force (USAF) resolution chart, spade poker, and fingerprint). In particular, the long‐range FP capability of the proposed approach is demonstrated by the synthetic aperture imaging of a King poker card at 12 m away. To the best of the authors' knowledge, R‐FP achieves the farthest detection range of macroscopic Fourier ptychographic imaging up to date, demonstrating its potential for application in far‐field detection. [ABSTRACT FROM AUTHOR]

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-toplayer 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 FROM AUTHOR]

Deep reinforcement learning (DRL) has recently been employed in various games, with which superhuman intelligence has been achieved, including Atari, Go, no-limit, and Texas hold'em. However, this technique has not been fully considered for Doudizhu which is a popular poker game in Asia and involves confrontation and cooperation among multiple players with imperfect information. In this paper we present a new deep reinforcement learning approach NV-Dou for the game Doudizhu. It adopts a variant of neural fictitious self-play to approximate the Nash equilibria of the game. The loss functions of the neural network integrate Q-Based policy gradient (mean actor critic) with advantage learning and proximal policy optimization. In addition, parametric noises are adopted for the fully connected layers in the neural network. The experimental results show that it needs only a few hours of training and achieves almost state-of-the-art performance comparing with the well-known open implementations RHCP, CQL, MCTS and others for Doudizhu. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic accelerated the shift to online learning in higher education, making the need to rethink pedagogical approaches to on-campus education stronger than ever before. While online learning offers a wide range of benefits for both teachers and students, social, face-to-face interaction provides a strong rationale for in-person education. This paper proposes a novel approach to enhancing in-person social learning experiences through robot-supported collaborative learning facilitated by a social robot NAO in the case study "Planning poker with NAO", which is a collaborative game-based activity facilitated by the robot. The case study was designed to provide a scalable social learning experience in small groups of students. The evaluation results from a study with 46 university students who played Planning poker with NAO explore different quality aspects of the learning experience. The results indicate that students valued both the social learning experience and the robotic facilitator. The analysis revealed some significant differences among students with and without prior experience in interacting with NAO, suggesting directions for future research related to novelty and familiarisation effects as well as to scaling up of social learning in small groups by applying social robots. [ABSTRACT FROM AUTHOR]

Background and aims: This research aimed to characterize social information processing abilities in a population of regular nondisordered poker players compared to controls. Methods: Participants completed the Posner cueing paradigm task including social cues (faces) to assess attention allocation towards social stimuli, including the effect of the presentation time (subliminal vs supraliminal) and of the emotion displayed. The study included two groups of participants: 30 regular nondisordered poker players (those who played at least three times a week in Texas Hold’em poker games for at least three months) and 30 control participants (those who did not gamble or gambled less than once a month, whatever the game). Results: The group of regular nondisordered poker players displayed an enhancement of the inhibition of return during the Posner cueing task. This means that in valid trials, they took longer to respond to the already processed localization in supraliminal conditions compared to controls. However, our results did not evidence any particular engagement or disengagement attention abilities toward specific types of emotion. Discussion and Conclusions: These results suggest that regular nondisordered poker players displayed social information processing abilities, which may be due to the importance to efficiently process social information that can serve as tells in live poker. The observed enhancement of the inhibition of return may permit poker players to not process a localization that has already processed to save attentional resources. Further research regarding the establishment of the IOR in other forms of gambling and with non-social cues needs to be performed. [ABSTRACT FROM AUTHOR]

Electron density irregularities in the ionosphere modify the phase and amplitude of trans-ionospheric radio signals. We aim to characterize the spectral and morphological features of E- and F-region ionospheric irregularities likely to produce these fluctuations or "scintillations". To characterize them, we use a three-dimensional radio wave propagation model—"Satellite-beacon Ionospheric scintillation Global Model of upper Atmosphere" (SIGMA), along with the scintillation measurements observed by a cluster of six Global Positioning System (GPS) receivers called Scintillation Auroral GPS Array (SAGA) at Poker Flat, AK. An inverse method is used to derive the parameters that describe the irregularities by estimating the best fit of model outputs to GPS observations. We analyze in detail one E-region and two F-region events during geomagnetically active times and determine the E- and F-region irregularity characteristics using two different spectral models as input to SIGMA. Our results from the spectral analysis show that the E-region irregularities are more elongated along the magnetic field lines with rod-shaped structures, while the F-region irregularities have wing-like structures with irregularities extending both along and across the magnetic field lines. We also found that the spectral index of the E-region event is less than the spectral index of the F-region events. Additionally, the spectral slope on the ground at higher frequencies is less than the spectral slope at irregularity height. This study describes distinctive morphological and spectral features of irregularities at E- and F-regions for a handful of cases performed using a full 3D propagation model coupled with GPS observations and inversion. [ABSTRACT FROM AUTHOR]

While it is well recognized that gambling behaviours are shaped by the contexts in which they occur, less research has investigated the relationship between poker and substance use (i.e., alcohol and other drugs). The current study explores poker players’ perceptions of the relationship between substance use and gambling. This qualitative descriptive study is a secondary data analysis of 25 interviews with poker players conducted as part of a broader prospective cohort project. From the thematic analysis, players described how specific contextual factors, such as social setting and location (e.g., bars, casinos) influenced their substance use. Poker players reported a relationship between substance use and gambling practices. However, players differed greatly in their decisions about whether, and how much, to use alcohol and other drugs, with individuals’ choices depending heavily on contexts (e.g., more inclined to partake when alcohol was available) and motivations (e.g., remaining sober to remain sharp and not impair their intellectual capacity). For those players who considered poker earnings to be their main source of income, increased use of alcohol, tobacco and cannabis were reported as a way of dealing with stress, anxiety and a lack of motivation related to their play.

Two potentially costly errors are common in sequential investment decisions: sticking too long to a failing course of action (escalation of commitment), and abandoning a successful course of action prematurely. Past research has mostly focused on escalation of commitment, and identified three critical determinants: personal responsibility, preferences for prior decisions, and decision framing. We demonstrate in three studies using an incentivized poker inspired task that these determinants of escalation reliably lead decision makers to keep investing even when real money is on the line. We observed in Experiments 1, 2 and 3 that reinvestments were more likely when decision makers were personally responsible for prior decisions. This likelihood was also increased when the decision makers had indicated a preference for initial investments (Experiments 2 and 3), and when outcomes were framed in terms of losses as compared to gains (Experiment 3). Both types of decision errors - escalation of commitment and prematurely abandoning a course of action - could be traced to the same set of determinants. Being personally responsible for prior decisions, having a preference for the initial investment, and loss framing did increase escalation, whereas lacking personal responsibility, having no preference for the initial investment, and gain framing increased the likelihood of prematurely opting out. Finally, personal responsibility had a negative effect on decision quality, as decision-makers were still more likely to reinvest when they were personally responsible for prior decisions, than when prior decisions were assigned optimally by an algorithm (Experiments 2 and 3). [ABSTRACT FROM AUTHOR]

Agile methods are used more and more frequently to develop products by reducing development time. Requirements are typically written in user stories or epics. In this paper, a new method called UX Poker is presented. This is a method to estimate the impact of a user story on user experience before development. Thus, there is the opportunity that the product backlog can also be sorted according to the expected UX. To evaluate UX Poker, a case study was conducted with four agile teams. Besides, a workshop followed by a questionnaire was conducted with all four agile teams. The goal of being able to estimate the UX even before development was achieved. Using UX Poker to create another way to sort the product backlog can be considered achieved in this first evaluation. The results show that UX Poker can be implemented in a real-life application. Additionally, during the use of UX Poker, it was found that a shared understanding of UX began. The participants clarified in the team discussion about UX Poker what related to influence the user stories had on UX and what UX meant for their product. [ABSTRACT FROM AUTHOR]

Poker is a game of skill, much like chess or go, but distinct as an incomplete information game. Substantial work has been done to understand human play in poker, as well as the optimal strategies in poker. Evolutionary game theory provides another avenue to study poker by considering overarching strategies, namely rational and random play. In this work, a population of poker playing agents is instantiated to play the preflop portion of Texas Hold’em poker, with learning and strategy revision occurring over the course of the simulation. This paper aims to investigate the influence of learning dynamics on dominant strategies in poker, an area that has yet to be investigated. Our findings show that rational play emerges as the dominant strategy when loss aversion is included in the learning model, not when winning and magnitude of win are of the only considerations. The implications of our findings extend to the modeling of sub-optimal human poker play and the development of optimal poker agents.

Poker has been considered a challenging problem in both artificial intelligence and game theory because poker is characterized by imperfect information and uncertainty, which are similar to many realistic problems like auctioning, pricing, cyber security, and operations. However, it is not clear that playing an equilibrium policy in multi-player games would be wise so far, and it is infeasible to theoretically validate whether a policy is optimal. Therefore, designing an effective optimal policy learning method has more realistic significance. This paper proposes an optimal policy learning method for multi-player poker games based on Actor-Critic reinforcement learning. Firstly, this paper builds the Actor network to make decisions with imperfect information and the Critic network to evaluate policies with perfect information. Secondly, this paper proposes a novel multi-player poker policy update method: asynchronous policy update algorithm (APU) and dual-network asynchronous policy update algorithm (Dual-APU) for multi-player multi-policy scenarios and multi-player sharing-policy scenarios, respectively. Finally, this paper takes the most popular six-player Texas hold 'em poker to validate the performance of the proposed optimal policy learning method. The experiments demonstrate the policies learned by the proposed methods perform well and gain steadily compared with the existing approaches. In sum, the policy learning methods of imperfect information games based on Actor-Critic reinforcement learning perform well on poker and can be transferred to other imperfect information games. Such training with perfect information and testing with imperfect information models show an effective and explainable approach to learning an approximately optimal policy. [ABSTRACT FROM AUTHOR]