Preparing for the Worst: The Space Insurance Market's Realistic Disaster Scenarios
Bibliographic record
Abstract
Abstract Approximately 30 satellite launches are insured each year, and insurance coverage is provided for about 200 in-orbit satellites. The total insured exposure for these risks is currently in excess of US$25 billion. Commercial communications satellites in geostationary Earth orbit represent the majority of these, although a larger number of commercial imaging satellites, as well as the second-generation communication constellations, will see the insurance exposure in low Earth orbit start to increase in the years ahead, from its current level of US$1.5 billion. Regulations covering Lloyd's of London syndicates require that each syndicate reserves funds to cover potential losses and to remain solvent. New regulations under the European Union's Solvency II directive now require each syndicate to develop models for the classes of insurance provided to determine their own solvency capital requirements. Solvency II is expected to come into force in 2016 to ensure improved consumer protection, modernized supervision, deepened EU market integration, and increased international competitiveness of EU insurers. For each class of business, the inputs to the solvency capital requirements are determined not just on previous results, but also to reflect extreme cases where an unusual event or sequence of events exposes the syndicate to its theoretical worst-case loss. To assist syndicates covering satellites to reserve funds for such extreme space events, a series of realistic disaster scenarios (RDSs) has been developed that all Lloyd's syndicates insuring space risks must report upon on a quarterly basis. The RDSs are regularly reviewed for their applicability and were recently updated to reflect changes within the space industry to incorporate such factors as consolidation in the supply chain and the greater exploitation of low Earth orbit. The development of these theoretical RDSs will be overviewed along with the limitations of such scenarios. Changes in the industry that have warranted the recent update of the RDS, and the impact such changes have had will also be outlined. Finally, a look toward future industry developments that may require further amendments to the RDSs will also be covered by the article.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".