Ludonarrative Media as a Bridge Toward the Complexity Out There
Bibliographic record
Abstract
How humanity may harmoniously situate itself within the world’s multiscale complexity (Wu 2013) is an enduring topic made increasingly urgent by today’s wicked crises (Lönngren and van Poeck 2021). Among many scholarly endeavors on the topic, the resonance theory (Rosa 2019) is notable for offering a comprehensive framework for such ecological mutualism. Relatedly, ludonarrative media like narrative games have shown great potential in facilitating resonance, as reflected in their recent usage to communicate complex issues (Koenitz, Barbara, and Eladhari 2023) and their increasing ubiquity (Karhulahti 2015), the latter best exemplified by the metaverse (Deniz 2023). Drawing upon our recent research on designing interactive narratives for communicating complex issues (Atmaja and Sugiarto 2022) and a model of “ludonarratification” of society (Atmaja et al. 2024), we integrate the resonance theory and ludonarrative media’s state of the art to help realize that potential. The resonance theory starts from the act of resonance itself, which consists of cognitive, affective, and sensorimotor aspects, aligning with three domains of learning (Dettmer 2005). Upon its application, it expands in three directions: the horizontal axis of social life, the diagonal axis of adjacent and more-than-human realities, and the vertical axis of metaphysics and metanarratives. Firstly, as Figure 1 shows, a well-designed ludonarrative medium can help our cognitive, affective, and sensorimotor faculties connect with the world’s complexity by (1) providing a ludonarrative world that simplifies reality without incorrectly representing it, (2) presenting the ludonarrative world through engaging and empathetic storytelling, and (3) manifesting the storytelling as representative assets and empowering user interfaces (Atmaja and Sugiarto 2022). Secondly, as seen in Figure 2, there is also a close alignment between the axes of resonance and our three-dimensional model of ludonarratification, which describes how ludonarrative media may organize to “ludonarratify” every activity in society, i.e., turn the activity into a narrative game. First, as demonstrated by the metaverse (Deniz 2023) and megagames (Johansson, Berggren, and Leifler 2023), the media can form a vast systemic hierarchy while retaining each’s independence, which lets the player safely experience various social institutions. Second, similar to transmedia multiverses (Kustritz 2014), there can be many such supersystems, separate yet influencing each other, which allow resonating with living and non-living “others” from adjacent realities. Lastly, game science shows us how to manage the media according to higher aspirations through metagaming (Klabbers 2018) and meta-metagaming (Boluk and LeMieux 2017). We will provide a hypothetical example of the application of ludonarrative media and their supersystems, multiverse, metagames, and meta-metagames to assist humanity in resonating with the world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".