Dynamic Optimization Meets Budgeting: Unraveling Financial Complexities
Bibliographic record
Abstract
This paper explores sources of complexity in dynamic optimization, examining how individuals navigate variation in incomes, prices, and returns in ten-period consumption-saving decisions.Our findings reveal that dynamic optimization poses significant challenges, resulting in suboptimal choices even in straightforward scenarios with stable parameters, full information, no uncertainty, and opportunities to learn.These challenges intensify in scenarios involving complexities such as inflation and compounding returns, marked by a pronounced tendency to over-smooth consumption.Additionally, we introduce a novel budgeting calculator designed to assist with consumption planning and to collect valuable non-choice data on subjects' planning strategies and horizonsan approach not previously utilized in studies of dynamic optimization.We observe significant heterogeneity in planning horizons and ability to optimize given a chosen horizon.Complete planning leads to better performance in more complex scenarios, even when people do not optimally utilize the calculator.However, there is little reoptimization after the first period and participants tend to stick with suboptimal plans for most of their life cycle.The decision to plan is less influenced by the complexity of the economic environment and more by the length of the planning horizon.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".