The Efficiency of Direct Public Involvement in Environmental Policymaking: An Experimental Test
Bibliographic record
Abstract
In one of the most ambitious forms of environmental decision-making, representatives of interested parties – environmentalists, developers, farmers, loggers, miners, etc.- are charged with the responsibility of developing a set of public policies that is acceptable to all of them. Although this approach has become increasingly popular, and has been widely discussed in the academic literature, little is known about the characteristics of the outcomes that are reached in this type of negotiation. We do not know, for example, whether these outcomes meet the standard criteria for efficiency or equity. In this paper, we use laboratory experiments to test whether a number of axiomatic models of bargaining can predict the behavior of the parties to environmental decision making. In recognition of the multi-dimensional aspect of most public land use conflicts, we ask pairs of subjects to negotiate over two goods, without the possibility of cash side payments. We thus provide one of the first experimental tests of a prediction associated with the Edgeworth Box: that parties with an initial endowment that is Pareto inefficient will make trades until they reach a Pareto efficient allocation. We further test whether parties in particular reach the Nash bargain when it coincides with or conflicts with outcomes that maximise the parties ’ joint payoffs and with outcomes at which the parties ’ receive equal payoffs. Finally, the effect of providing parties with full or partial information regarding payoffs is also examined.
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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 | 0.027 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".