Bibliographic record
Abstract
Richard A. Young, Uncertainty and the Environment. Northhampton, MA, USA: Edward Elgar, 2001, 249 pp. Uncertainty and the Environment is the result of an interesting doctoral thesis on how George Shackle's ideas of uncertainty might be applied to complex environmental systems. Young makes an important contribution to the field of environmental decision making by exploring how Shackle's work might improve the procedural rationality decisions. Not surprisingly, the book is structured much like you would expect a well-written dissertation. It begins with an overview of the interdisciplinary literature surrounding uncertainty in ecological and economic systems of interest to economists, geographers, decision theorists, and environmental management researchers. Young describes how Shackie's theory is of use in ecological decisions and provides some background to the case study used in the research. The logical progression of the book was welcome although Young's writing style created the occasional awkward passage. By and large, however, the book was a pleasure to read. Young argues early in the book for delineation between and soft uncertainty, as in more and less uncertainty respectively. Hard uncertainty reflects the difficulty of predicting outcomes arid probability in highly complex ecological and economic systems. Soft uncertainty, on the other hand, captures more conventional notions of risk, where outcomes and probabilities are both reasonably known. Young then makes the case that hard uncertainty characterizes many environmental decisions, largely because ecosystems under stress have the potential for flips in their and structure. Coral reefs that become overcome with algae are one example of this type of flip: they shift unpredictably in response to smoothly changing external conditions. The thrust of Shackle's model, as modified by Young, is this: (1) uncertainty is better measured by of surprise rather than probability; and (2) decision makers weigh potential outcomes and their degree of according to an ascendancy function which measures the power of this pair [i.e., the outcome and surprise] to arrest the attention of the individual. (p. 90) Degree of differs from probability in that adding up the amount that someone is surprised for all imaginable outcomes does not have to equal one, allowing us to be highly surprised by many different outcomes. However, the sum of the probability of all imaginable outcomes must be one. Shackle's ideas are not as widely accepted as those from the expected utility school of decision making. Shackle thought of decisions not as repeated events, but rather unique choices that differ over time. …
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.040 |
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".