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Players create personalized tarot decks instead of using traditional tarot imagery. The design is grounded in Jungian psychology and archetype theory.
Self Arcana is a design-research paper presenting a collaborative two-player storytelling and self-reflection card game in which players create personalized tarot decks rather than using traditional imagery. The authors ground the design in Jungian psychology, archetype theory, and role-playing practice, arguing that role-playing can function as a space for active imagination and individuation. The central proposition is that tarot's archetypal structure, when paired with symbols drawn from a player's own life, can prompt both narrative generation and introspection.
The paper's design follows a structured sequence. Players build a five-card personal deck derived from the Rider-Waite-Smith Major Arcana, which the authors regrouped into conceptual clusters such as Self, Creation, Transformation, and Enlightenment, with reversed cards representing shadow aspects. Players then select a character type drawn from a nine-type role-playing taxonomy, choose a location, and perform end-game rituals such as burning, keeping, or changing a card. Gameplay proceeds in two stages, one involving individual role-play and one in which the pair combines decks into a ten-card spread.
The evidence is qualitative and limited. It comes from a duoethnographic playtest conducted by the two designers, who report that the card-creation process produced more reflection than the gameplay itself, that improvised rules broke immersion, and that personal attachment to certain archetypes was emotionally intense for one participant. The authors acknowledge that these experiences are not generalizable and that the study does not include comprehensive testing with external players. Their takeaways emphasize the need for familiarity with role-playing, pre-session negotiation of terms, and clearer story-generation rules, and they outline future work involving broader playtests with both qualitative and quantitative measures.
DiGRA · 2026