Knowledge Sharing and Investment Decisions in Information Security
Bibliographic record
Abstract
We study the relationship between decisions made by two similar firms pertaining to knowledge sharing and investment in information security. The analysis shows that the nature of information assets possessed by the two firms, either complementary or substitutable, plays a crucial role in influencing these decisions. In the complementary case, we show that the firms have a natural incentive to share security knowledge and no external influence to induce sharing is needed. However, the investment levels chosen in equilibrium are lower than optimal, an aberration that can be corrected using coordination mechanisms that reward the firms for increasing their investment levels. In the substitutable case, the firms fall into a Prisoners' Dilemma trap where they do not share security knowledge in equilibrium, despite the fact that it is beneficial for both of them to do so. Here, the beneficial role of a social planner to encourage the firms to share is indicated. However, even when the firms share in accordance to the recommendations of a social planner, the level of investment chosen by the firms is sub-optimal. The firms either enter into an “arms race” where they over-invest or reenact the under-investment behavior found in the complementary case. Once again, this sub-optimal behavior can be corrected using incentive mechanisms that penalize for over-investment and reward for increasing the investment level in regions of under-investment. The proposed coordination schemes, with some modifications, achieve the socially optimal outcome even when the firms are risk-averse. Implications for information security vendors, firms, and social planner are discussed.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".