Sustainable preferences via nondiscounted, hyperreal intergenerational welfare functions
Bibliographic record
Abstract
We define an intergenerational social welfare function Sigma from |R^|N (the set of all infinite-horizon utility streams) into *|R (the ordered field of hyperreal numbers). The function Sigma is continuous, linear, and increasing, and is well-defined even on unbounded (e.g. exponentially increasing) utility streams. This yields a complete social welfare ordering on |R^|N which is Pareto and treats all generations equally (i.e. does not discount future utility). In particular, it is what Chichilnisky (1996) calls a `sustainable' preference ordering: it is neither a `dictatorship of the present' nor a `dictatorship of the future'. We then show how an agent with no `pure' time preferences may still `informationally discount' the future, due to uncertainty. Last, we model intergenerational choice for an exponentially growing economy and population. In one parameter regime, our model shows `instrumental discounting' due to declining marginal utility of wealth. In another regime, we see a disturbing `Paradox of Eternal Deferral'.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".