Consequence and Utility Modeling in Rational Decision Making
Bibliographic record
Abstract
Offshore design and risk assessment are typically marked by far-reaching choices and important one-time decisions. Decision analysis involving large structures, sensitive environments, and difficult operations, requires a very careful formulation of utility and consequences. It is shown in this paper that one of the most important shortcomings of such analyses stems from an incomplete definition of the system, and from the failure to include various “follow-up” consequences. “Follow-up” consequences are, generally speaking, triggered by extreme losses, such as excessive business losses, consequences from unexpected cascade effects, collateral and indirect losses, or other intangible losses. The non-inclusion of such losses occurs either voluntarily or involuntarily. Often the identification and the valuation of follow-up consequences can be prohibitively difficult. For such cases, it is possible to use a simple model based on risk aversion to the consequences associated with extreme discrete hazards during the lifetime of a system. This model is developed in the framework of a lifecycle utility optimization. To add practical value to this model, we also introduce the concept of a Bayesian updating of utility functions. Since utility functions are all about expressing the preferences of expert decision makers, we refer to the Bayesian parameters as “preference” parameters. The paper shows that the approaches developed lead to better and more risk-consistent decision making. An illustrative example is given in the paper, highlighting the significance of the findings.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".