Difficulty is one of the major motivational pull of video games, and thus many games use Dynamic Difficulty Adjustment (DDA) systems to improve the game experience. This paper describes our research investigating the influence of DDA systems on player's confidence, evaluated using an in-game bet system. Our hypothesis is that DDA systems may lead players to overconfidence, revealed by an overestimation of their success chances when betting. This boost of confidence may be a part of the positive impact of DDA systems on the quality of game experience. We explain our method to evaluate player's confidence and implement it into three games related to logical, motor and sensory difficulties. We describe two experimental conditions where difficulty is either randomly chosen or adapted using a DDA algorithm. Results show how DDA systems can lead players to high level of overconfidence. CCS CONCEPTS • Applied computing → Computer games; Psychology; • Human-centered computing → User studies.

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Abstract. This paper introduces issues about a methodology for the design of serious games that help players/learners understand their decisionmaking process. First, we discuss the development of a video game system based on a switching-role mechanic where the player becomes the game leader of the experience. Then, we introduce game mechanics designed to induce a specific behavior, overconfidence, that helps to understand the players' decision-making processes. Finally, we describe tools for measuring the players' self-reflection regarding their judgment process.

This paper describes an automated evaluation of the overall game experience using a synthetic agent, that we contextualize for First-Person Shooter games. This evaluation method is based on the characterization of the game experience through dynamics of major FPS games. We define dynamics as sequences of events that are meaningful for the player during the game session. As they trigger players' emotional responses, and influence their overall enjoyment and motivation, we classify them according to Motives for Play like curiosity, thrill-seeking, problem-solving, victory, and acquisition, in order to facilitate the evaluation process. Based on that, our evaluation method proposes to select synthetic agent routines that target a distinct game experience while playing a game session, using a selection of game dynamics. As the agent navigates through the level and interacts with opponents, dynamics may occur and, if so, are automatically identified, and then classified as Motives for Play. In the end, this classification can be used to evaluate the game experience and the quality of the level itself during playtesting sessions. It may also be utilized to assist the procedural generation of any level that target a specific game experience.CCS Concepts: • Human-centered computing → User models; • Applied computing → Computer games; Psychology.