Bibliographic record
Abstract
ABSTRACT Financial advice is fragmented and not living up to its potential. Despite 75+ years of coexistence, the lifecycle models stemming from Ramsey (1926), Fisher (1930), Modigliani and Brumberg (1954), Friedman (1957), Modigliani (1966), Samuelson (1969), Merton (1969, 1971, 1992), as well as others, and the single‐period optimization models of de Finetti (1940 [2006]), Roy (1952), Tobin (1958), and Markowitz (1952, 1959, 1987) have largely remained separate; let alone, have they been brought together in a meaningful way. This lack of connection is indicative of the current paradigm of disconnected piecemeal approaches that dominate financial planning and investing. Building on the insights of Samuelson (1969) and Fama (1970) and methods developed by Idzorek and Kaplan (2024), we link lifecycle models and mean–variance optimization models into a combined, integrated model. This model simultaneously provides unified financial planning associated with lifecycle finance with integrated portfolio recommendations from single‐period optimization models. We argue that the industry should move toward an interconnected, hybrid lifecycle net worth optimization model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".