A Cheating Mood: The Emotional and Psychological Benefits of Cheating in Single-Player Games
Bibliographic record
Abstract
Players, developers, and researchers generally agree that "cheating" to gain an unfair advantage over others fosters negative player experiences. Despite social and experiential repercussions and cheating's negative stigma, the majority of players regularly cheat in some form. Fixation on cheating's social and moral axes disservices understanding the ways in which players cheat in single-player settings, cheating's potential benefits, and cheating's effects on player experience. Surveying 188 U.S. players on their beliefs, preferences, and experiences of cheating in single-player contexts, mixed-methods analyses reveal that, unlike in multiplayer contexts, most players endorse cheating in single-player settings. They do so to facilitate mood repair, stress relief, and flow, and to exercise agency over satisfying their psychological needs during gameplay. Building off prior studies in support of cheating, we discuss the ludic, cognitive, and wellness benefits found, and argue against imposing the moral dilemmas of multiplayer cheating on single-player contexts.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".