A Network Effects Perspective on Software Piracy
Why this work is in the frame
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Bibliographic record
Abstract
The software industry frequently maintains that software piracy is no more than theft and a cause of huge losses. Courts and other policy makers easily adopt that straightforward argument. Surprisingly, however, many software publishers do not employ any technological measures to protect their software from piracy and many popular software products are distributed without any means of protection and are easily pirated. This lack of protection is puzzling; a solution for that puzzle is the focus of the article. The first part of the article challenges the conventional explanations that are usually given for the failure to protect software and offers an alternative explanation. The paper argues that not protecting software is a profitable strategy based on several elements: the first element is cross-sectional price discrimination in which the lower tiers of customers do not pay for their software. In the face of network effects that exist in many software markets, such a strategy achieves the most expeditious and widest dissemination of software, maximizes the value of the network, may accelerate the tipping of the market in favor of the more dominant publisher and later create higher barriers to entry. The article analyzes the advantages of such implicit price discrimination over explicit price discrimination and the advantages from complaining about piracy over preventing it. The second element is dynamic pricing in a multiple-period setting. At a second stage, due to a lock-in phenomenon, software publishers are able to hold-up potential ex-pirates who face a threat of litigation and charge them higher prices. According to the basic copyright paradigm each pirated copy is a net loss for the copyright holder. According to the proposed network theory some level of piracy generates higher revenues from other customers and over time. The second part of the article explores whether and to what extent legal doctrines, particularly in antitrust and copyright law should respond to these counter-paradigmatic circumstances.
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it