Software Exclusivity and the Scope of Indirect Network Effects in the U.S. Home Video Game Market
Bibliographic record
Abstract
This paper investigates the scope of indirect network effects in the home video game industry. We argue that the increasing prevalence of non-exclusive software gives rise to indirect network effects that exist between users of competing and incompatible hardware platforms. This is because software non-exclusivity, like hardware compatibility, allows a software firm to sell to a market broader than a single platform's installed base, leading to a dependence of any particular platform's software on all firms' installed bases. We look for evidence of these market-wide network effects by estimating a model of hardware demand and software supply. Our software supply equation allows the supply of games for a particular platform to depend not only on the installed base of that platform, but also on the installed base of competing platforms. Our results indicate the presence of both a platform-specific network effect and -in recent years- a cross-platform (or generation-wide) network effect. Our finding that the scope of indirect network effects in this industry has widened suggests one reason that this market, which is often cited as a canonical example of one with strong indirect network effects, is no longer dominated by a single platform.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".