Join Up or Stay Away? Coalition Formation for Critical IT Infrastructure
Bibliographic record
Abstract
PRACTICE AND POLICY ABSTRACT We consider the formation of a coalition when districts invest in critical IT infrastructure that, if disrupted, can cause significant damage to security, the economy, public health, or safety. The benefits from these investments can spill over to other districts. Districts choose whether to participate in a coalition, and the coalition subsequently makes IT infrastructure investment decisions for those districts that join the coalition. These inside districts have superior interoperability in their spillovers relative to outside districts. We find that inside districts’ resource levels decrease with the size of the coalition, and this size depends on the coalition’s economies of scale and relative interoperability. Depending on these factors, any size coalition can be an equilibrium or socially optimal. In most cases, the socially optimal coalition size is larger than the equilibrium coalition. A subsidy or tax can incentivize the equilibrium coalition size and district investment levels to be socially optimal, providing a general solution to the provisioning of critical IT infrastructure. We use the European Union’s Digital COVID Certificate program providing vaccine status information and the U.S. Government’s Direct Project that supports the establishment of nationwide health information exchanges to illustrate elements of our model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".