Norms from outside and from inside: An experimental analysis on the governance of local ecosystems
Bibliographic record
Abstract
Many forest ecosystems provide multiple goods and services to both local users (e.g. firewood, water) and to other external beneficiaries (biodiversity conservation, carbon sequestration). This calls for alternative approaches in the governance of these local ecosystems. Even if local users solve the commons dilemma they face regarding the optimal provision of the direct benefit, there might still be a need for introducing mechanisms that also address the externality that involves those outside of the group. This paper addresses the analysis of different types of mechanisms, endogenously emerged from groups vs. externally imposed to them, when facing the typical tragedy of the commons. During 2000_2002 we conducted a series of economic experiments in several rural communities in Colombia. The sub-set reported here of 53 sessions with 265 actual users of local ecosystems, were focused specifically on the effect of external and self-governing rules for inducing cooperative behavior within groups. A group extraction or 'commons' game was used to explore how rules, formal and informal, emerge and how individual behavior responds to regulatory mechanisms aimed at solving the dilemma. Three treatments were compared to a baseline design: Two external regulations (high and low penalties, and only 20% of the players monitored), and a self-governed system where individuals were allowed to have in each round a few minutes of non-binding face-to-face communication. Surprisingly, both external regulations generated very similar results regardless of the level of the penalty, and they induced behaviors very similar to those achieved by the self-governed treatment. The experimental results suggest that individuals do not seem to follow entirely the conventional economic prediction of a minimizer of expected costs of regulations against the benefits from over extracting the resource, and that humans can develop norms based on non-enforceable rules of cooperation. Instead, other elements related to social norms and subjective valuation of the benefits and costs of the regulations might be in play.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".