The use of personality to predict user preferences or select a job candidate are practical examples of how personality can be used in a variety of fields. But to assess someone's personality is not easy. The most commonly used techniques are personality questionnaires, but they are subject to social desirability bias and false responses, limiting the accuracy of personality assessment. Implicit techniques have also been used, but they require great amounts of user interactions with the system, and most of them only focus on broader personality dimensions. To overcome those limitations, we developed a short-duration mobile serious game, Coin Catcher, as a concept proof to implicitly measure the granular personality trait morality in less than 5 min. The game uses concepts related to normative morality, proposing dilemmas that the player must solve in order to progress in the game. Experiments with real users were conducted ($n=96$), showing morality can be implicitly determined by a simple game in a short period of time, without the bias associated with personality questionnaires. Additionally, correlations with other personality traits, such as altruism, cooperation, modesty, sympathy, and anger were also found, showing the game has the potential to measure several granular personality traits that better characterize a person.

Summary One of the most important factors to determine the success of an organization is the quality of decisions made. Supporting a decision‐making process is a complex task, mainly when decision‐makers are dispersed. Group decision support systems (GDSSs) have been studied over the last decades with the goal of providing support to decision‐makers; however, their acceptance by organizations has been difficult. This happens mostly due to usability problems, loss of interaction between decision‐makers, and consequently, loss of information. In this work, we present a web‐based GDSS developed to support groups of decision‐makers, regardless of their geographic location. The system allows the creation of multicriteria problems and the configuration of the preferences, intentions, and interests of each decision‐maker. The presented system uses a multiagent system to combine and process this information, using virtual agents that represent each decision‐maker. We believe that, with this approach, we will proceed in the refinements of a successful GDSS to correctly support decision‐makers while preserving the valuable intelligence and knowledge that can be generated in face‐to‐face meetings. Furthermore, the high level of usability that the system provides will contribute to an easier acceptance and adoption of this kind of systems.