The epistemic, ethical, and political dimensions of uncertainty in integrated assessment modeling
Bibliographic record
Abstract
Integrated assessment models (IAMs) of global climate change that combine representations of the economic and the climate system have become important tools to support policymakers in their responses to climate change. Yet, IAMs are built in the face of pervasive uncertainty, both scientific and ethical, which requires modelers to make numerous choices in model development. These modeling choices have epistemic, ethical, and political dimensions. First, modeling choices determine how well our current (lack of) knowledge about the elements and processes of the modeled system is represented. Second, modeling choices have ethical implications, for example, the choice of a social discount rate, which is well documented. For other modeling choices, the ethical assumptions and implications are more subtle. Third, climate‐economic models are not produced and used in a political vacuum; they shape and are shaped by the social relations they are embedded in. We review findings from various literatures to unpack the complex intersection of science, ethics, and politics that IAMs are developed and used in. This leads us to suggest theoretical frameworks that may enable an integrated epistemic–ethical–political understanding of IAMs and increase transparency about all three dimensions of model uncertainties. WIREs Clim Change 2016, 7:627–645. doi: 10.1002/wcc.415 This article is categorized under: Integrated Assessment of Climate Change > Integrated Assessment Modeling
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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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".