Bibliographic record
Abstract
The last two chapters might have been a bit more than what you were expecting (or bargaining for) from a book on personal financial planning. Indeed, when we mention the terms Ito's lemma, control theory , or diffusion processes to graduate students and university colleagues – when asked about the tools of our research trade – they immediately assume that we work in the esoteric field of option pricing or derivative hedging. For most of the 1980s and 1990s that would have been the only conclusion to draw, but not anymore. Indeed, most people are surprised to learn that one can actually use these mathematical concepts to analyze and provide guidance on practical questions such as how much and which type of life insurance you should purchase, the best age at which to start drawing a retirement pension annuity, or the optimal home mortgage loan for their family. Indeed, the models of quantitative finance that spawned a revolution in the 1970s and 1980s, which then filtered through to corporate finance and strategy in the 1980s and 1990s, have arrived at your personal doorstep in the last decade or two. The objective is crystal clear: to use the concept of “consumption smoothing” – in all its mathematical glory – to help individuals make better personal wealth and risk management decisions. We call it strategic financial planning (SFP) over the lifecycle. In the last decade financial luminaries such as Harry Markowitz, William Sharpe, and Robert Merton have penned articles, given presentations, and encouraged research on this same topic.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.098 | 0.034 |
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".