The Reward for Luck: Understanding the Effect of Random Reward Mechanisms in Video Games on Player Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Random Reward Mechanisms (RRMs) in video games are systems in which rewards are issued probabilistically upon certain trigger conditions, such as completing gameplay tasks, exceeding a playtime quota, or making in-game purchases. We investigated the relationship between RRM implementations and user experience. Video analysis of 35 RRM systems allowed for the creation of a classification system based on contrasting observed dimensions. Interviews with 14 video game players provided insights into how factors such as the affordances of non-optimal rewards and the trade-off between random luck and skill impact player perception and interaction with RRMs. We additionally investigated the relationship between auditory, visual, and gameplay design decisions and player expectations for RRM reward presentations, finding that the resources required to obtain the reward and the relative value of the reward impact its expected presentation. Finally, we applied our findings to propose design methodologies for creating engaging and significant RRM systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it