Pathological video game use among young Swiss men: the use of monothetic and polythetic formats to distinguish between pathological, excessive and normal gaming

There is no agreement about the distinction between pathological, excessive and normal gaming. The present study compared two classifications for defining pathological gaming: the polythetic format (gamers who met at least half of the criteria) and monothetic format (gamers who met all criteria). Associations with mental, health and social issues were examined to assess differences between subgroups of gamers. A representative sample of 5,663 young Swiss men filled in a questionnaire as part of the ongoing Cohort Study on Substance Use Risk Factors (C-SURF). Game use was assessed with the Game Addiction Scale. Mental, social and physical factors (depression, anxiety, aggressiveness, physical and mental health, social and health consequences), gambling and substance use (illicit drug use, alcohol dependence and problematic cannabis use) were also assessed. The results indicated that monothetic gamers shared problems with polythetic gamers, but were even more inclined to mental health issues (depression, anxiety, and aggressiveness) and were more vulnerable to other dependencies like substance use, alcohol dependence or gambling. A second analysis using Latent Class Analysis confirmed the distinction between monothetic and polythetic gamers. These findings support the use of a monothetic format to diagnose pathological gaming and to differentiate it from excessive gaming.

Simple Heuristics in a Social Game

Simple Heuristics in a Social World invites readers to discover the simple heuristics that people use to navigate the complexities and surprises of environments populated with others. The social world is a terrain where humans and other animals compete with conspecifics for myriad resources, including food, mates, and status, and where rivals grant the decision maker little time for deep thought, protracted information search, or complex calculations. Yet, the social world also encompasses domains where social animals such as humans can learn from one another and can forge alliances with one another to boost their chances of success. According to the book's thesis, the undeniable complexity of the social world does not dictate cognitive complexity as many scholars of rationality argue. Rather, it entails circumstances that render optimization impossible or computationally arduous: intractability, the existence of incommensurable considerations, and competing goals. With optimization beyond reach, less can be more. That is, heuristics--simple strategies for making decisions when time is pressing and careful deliberation an unaffordable luxury--become indispensible mental tools. As accurate as or even more accurate than complex methods when used in the appropriate social environments, these heuristics are good descriptive models of how people make many decisions and inferences, but their impressive performance also poses a normative challenge for optimization models. In short, the Homo socialis may prove to be a Homo heuristicus whose intelligence reflects ecological rather than logical rationality.

A cognitive hierarchy model of behavior in the action commitment game

We apply the cognitive hierarchy model of Camerer et al. (Q J Econ 119(3):861-898, 2004)-where players have different levels of reasoning-to Huck et al. (Games Econ Behav 38:240-264, 2002) discrete version of Hamilton and Slutsky (Games Econ Behav 2:29-46, 1990) action commitment game-a duopoly with endogenous timing of entry. We show that, for an empirically reasonable average number of thinking steps, the model rules out Stackelberg equilibria, generates Cournot outcomes including delay, and outcomes where the first mover commits to a quantity higher than Cournot but lower than Stackelberg leader. We show that a cognitive hierarchy model with quantal responses can explain the most important features of the experimental data on the action commitment game in (2002). In order to gauge the success of the model in fitting the data, we compare it to a noisy Nash model. We find that the cognitive hierarchy model with quantal responses fits the data better than the noisy Nash model.