Half a Century of Public Software Institutions: Open Source as a Solution to Hold-Up Problem
Bibliographic record
Abstract
We argue that the intrinsic inefficiency of proprietary software has historically created a space for alternative institutions that provide software as a public good. We discuss several sources of such inefficiency, focusing on one that has not been described in the literature: the underinvestment due to fear of hold-up. An inefficient hold-up occurs when a user of software must make complementary investments, when the return on such investments depends on future cooperation of the software vendor, and when contracting about a future relationship with the software vendor is not feasible. We also consider how the nature of the production function of software makes software cheaper to develop when the code is open to the end users. Our framework explains why open source dominates certain sectors of the software industry (e.g., programming languages), while being almost non existent in some other sectors (e.g., computer games). We then use our discussion of efficiency to examine the history of institutions for provision of public software from the early collaborative projects of the 1950s to the modern "open source" software institutions. We look at how such institutions have created a sustainable coalition for provision of software as a public good by organizing diverse individual incentives, both altruistic and profit-seeking, providing open source products of tremendous commercial importance, which have come to dominate certain segments of the software industry. Copyright © 2010 Wiley Periodicals, Inc..
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".