Market experience with modeling for defined-benefit pension funds: evidence from four countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper takes a look at the modeling side of pension fund management. It is based on interviews with pension fund managers, regulators, consultants, and academics in four countries – the Netherlands, Switzerland, the United Kingdom, and the United States. The objective was to understand, through the experience of market participants, the role of modeling in managing defined-benefit pension funds. The 28 defined-benefit pension funds participating in the study have a total of €334 billion ($436 billion) assets under management. The findings of our study show that modeling is now considered an indispensable tool by many market participants. The need to manage the risk inherent in defined-benefit pension plans is the key motivation behind the growing use of modeling. In the Netherlands, for example, where private-sector plans did not experience serious underfunding problems after the 2000 market crash, the use of modeling is widespread and well-integrated in the decision-making process. Dutch regulators have recently mandated a risk-based approach and specified broad principles of sound modeling, including the marking to market of assets and liabilities.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it