Manufacturer’s “1-Up” from Used Games: Insights from the Secondhand Market for Video Games
Bibliographic record
Abstract
The video game industry has a robust secondhand market for games, even though some of the major gaming-console companies possess the means to shut it down. What is the special ingredient in this industry that would incentivize a manufacturer to give tacit approval to buying and selling used games? In this study, leveraging a game-theoretic model, we investigate the effect of gaming console on a manufacturer’s strategy in the presence of a secondhand market for games. We find that when the manufacturer offers a console that provides additional value outside of playing games (e.g., media hub with apps), the secondhand market improves the manufacturer’s profit, consumer surplus, and social welfare, all at the same time. Moreover, the manufacturer enjoys greater benefit from the secondhand market as the intrinsic value of the console increases. This is in stark contrast with cases where there are no consoles involved or the consoles do not offer any intrinsic value; in such settings, the manufacturer would opt to shut down the secondhand market. Overall, our results have implications that apply not only to the past and present of the gaming industry but also to its future and to other types of platform-based markets for contents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".