Bibliographic record
Abstract
Social aspects of multiplayer games are well known as contributors to game success, with online friendships and socialization expected to expand and strengthen a player-base. Understanding the nature of social behavior and determining the impact of cooperation on gameplay is thus important to game design. In this work, we make use of data exposed through in-game and web-based API's of two contemporary multiplayer games, World of Warcraft and Halo: Reach. We use this data to investigate the extent of cooperation among players and the effect on individual player behavior. We moreover show how the quantitative assessment of cooperative behavior can be used to isolate potential problem areas in games which may require additional balancing. We first monitor group health and position to measure the pacing of a cooperative scenario in World of Warcraft. We measure a scenario's pacing as the temporal progression of its difficulty, which directly reflects the required level of cohesion and coordination among the players in a group. Our results verify the informal perception that statically designed content becomes increasingly trivial as players obtain stronger stats, thus reducing the need for cohesion. Direct quantification of this behavior, as enabled by designs such as ours, allows for online, adaptive pacing that should better foster player community by consistently emphasizing the need for communication.The benefits of actual group behavior also has a reverse impact on game design. In our experiment involving Halo: Reach, our results demonstrate that players who enter as a group into the multiplayer matchmaking system have, on average, a significantly higher win-to-loss ratio than players who enter the matchmaking system alone. This gives them an advantage over less social players, and thus attests to the potential for refinement in group matchmaking techniques. In addition, our exploratory principal component analysis of individual player performances reveals a set of novel player types adapted to the multiplayer context and quite distinct from player types found in other game genres.From a general standpoint, the data collection techniques outlined in this thesis reveal the use of publically-accessible game APIs as a relatively unexplored yet promising source of insight into real-world gameplay behavior. Our results serve as evidence for two widely-assumed notions of multiplayer game design; the first, that static game content adversely affects a game's replayability and ultimately lessens the need for communication and cohesion among players. The second, that coordination among players provides a significant advantage over those who choose to play independently in a team-based setting.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".