The Aesthetics of Human-Machine Interaction: Generative Textuality in Hello Games’s <i>No Man’s Sky</i>
Bibliographic record
Abstract
Generative design is the artistic practice where an artist uses external systems (rules, algorithms, organic processes) to complete an artwork. Commonly utilized in video game design, generative design suggests an aesthetics of human-machine interaction, as machine decision-making partially shapes the experience of the player. In generative games, then, the relationship between designer, system, and audience dynamically shapes the conception, production, and reception of the game as a text.The interaction between designer, system, and audience unfolds in three ways. Firstly, by applying generative methods, the designer is explicitly de-centered, instead creating an environment in which systems somewhat-autonomously interact with entities. Secondly, generative games frame players as both entities within the game-world and agents from outside of it. Thirdly, generative games produce unexpected or emergent outcomes. Emergence is a product of the tension between decisions made by designer, system, and player(s). In essence, generative games produce novel worlds into which players are thrown, forcing them to learn to dwell within them. As Gianni Vattimo argues in Art’s Claim to Truth, in such artworks where “the hermeneutic circle is conceived in more existentialist terms, the act of interpretation is dramatized” (85).Further analysis of the interpretive drama found in games which utilize generative methods, especially as these texts continue to pervade popular culture and daily life, can unpack theories of intentionality, presence, and creativity in modern digital contexts. To elaborate these theories, I examine generative methods found in Hello Games’s No Man’s Sky. In this game the relationship between designer, text, and player produces emergent narratives with each subjective experience of the text, suggesting that human-machine interaction is inherently processual and relational.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".