Common-pool resource management and the Prisoner's Dilemma: how the potlatch changes the game
Bibliographic record
Abstract
Common-pool resources often become depleted because they are rival goods and exclusion of users is difficult.Collective action systems are historically effective solutions to this problem that use agent heterogeneity and complex social structures to motivate sustainable extraction and generate abundance in the community.However, most economic analyses of the depletion problem and its solutions are based on individualistic choice models which assume that agents are homogenous, self-interested utility maximisers who make decisions independently of other agents.Such models cannot process social features or unique agent characteristics and thus treat collectives as single decision-making units, preventing the mechanisms within them from being explored and understood.Methodologies such as agent-based modelling can overcome these limitations.Drawing inspiration from the Indigenous potlatch tradition, an agent-based model was built to simulate a heterogenous community interacting with a common-pool resource and engaging in periodic post-extraction resource reciprocity.An analysis of the time-averaged per capita payoffs experienced by different types of agents allowed us to identify certain mechanisms and examine how they shift resource-based incentives at the individual level.This shows that agent-based modelling can improve our understanding of how collective solutions guide individual-level decision-making in order to avoid depletion and generate abundance.Les ressources communes sont souvent puises parce qu'elles sont des biens comptitives et l'exclusion des usagers est difficile.Les systmes d'action collective sont des solutions historiquement efficaces ce problme qui utilisent l'htrognit des agents et les structures sociales complexes pour motiver l'extraction durable et gnrer l'abondance dans la communaut.Cependant, la plupart des analyses conomiques du problme dficitaire et de ses solutions sont bases sur des modles de choix individualistes qui supposent que les agents sont des optimiseurs homognes et d'intrt personnel qui prennent des dcisions indpendamment des autres agents.De tels modles ne peuvent pas traiter des caractristiques sociales ou d'agent uniques et traitent ainsi les collectifs comme des units de dcision uniques, empchant ainsi l'exploration et la comprhension des mcanismes en leur sein.Des mthodologies telles que la modlisation base sur les agents peuvent surmonter ces limitations.S'inspirant de la tradition autochtone du potlatch, un modle bas sur les agents a t labor pour simuler une communaut htrogne interagissant avec une ressource commune et s'engageant dans une rciprocit priodique des ressources aprs l'extraction.Une analyse des gains moyens par habitant dans le temps subis par diffrents types d'agents nous a permis d'identifier certains mcanismes et d'examiner comment ils modifient les incitations bases sur les ressources au niveau individuel.Cela montre que la modlisation base sur les agents peut amliorer notre comprhension de la faon dont les solutions collectives guident la prise de dcision au niveau individuel afin d'viter l'puisement et de gnrer l'abondance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".