Multi-Objective Evolutionary Computation for the Portfolio Optimization Problem with Respect to Environmental, Social, and Governance Criteria
Bibliographic record
Abstract
A common problem that faces many is the tension between doing what aligns with our values and doing what is fiscally best. A system leveraging Multi-Objective Evolutionary Computation, specifically MOEA/D, was proposed to produce highly performant portfolios tailored to an individual’s ESG preferences given a custom survey. The survey, written using the greater context of other risk and ESG relevant surveys, was conducted and used to construct a weighting to normalize a given investor’s own survey responses and allow a single portfolio from the collection of the best portfolios to be matched to that investor. Two potential architectures were considered to build the proposed system: Architecture 1, where the optimization is run for each investor that takes the survey, and Architecture 2 where a multi-objective optimization is run less frequently and the investor is given a portfolio from the Pareto front. This subset consists of all the non-dominated portfolios. The user may have a different experiences, including quality or time waiting, depending on the architecture chosen. The result of the experiment was that both architectures produced high quality portfolios that performed comparably. However, the best portfolio from Architecture 2 was better in most regards than any portfolio from Architecture 1. All Architecture 1 portfolios were more significantly tailored to each of the individuals preferences. For Architecture 2, a limited number of high performing portfolios was generated: as a result, more investors would potentially be recommended the same few portfolios, especially in comparison to Architecture 1.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".