Leveraging Asymmetries in Multiplayer Games
Bibliographic record
Abstract
Many people develop lasting social bonds by playing games together, and there are a variety of games available so that individuals are likely to find games that appeal to their specific play preferences, abilities, and available time. However, there are many instances where people might want to play together, but would normally choose vastly different games for themselves, due to these various asymmetries in play experiences, such as grandparents and grandchildren, highly skilled players and novices, or even simply two players that enjoy different games. In this work, we aim to improve the design of asymmetric games-games that are designed to embrace and leverage differences between players to improve multiplayer engagement. This paper builds upon prior work to describe the elements of asymmetry that can be used to design such games, and uses these elements in the design of an asymmetric game, Beam Me 'Round Scotty'! We present the results of a thematic analysis of a player experience study, discuss these findings, and propose an initial conceptual framework for discussion of design elements relevant to asymmetric games.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".