Exploring Heterogeneity in Common Pool Resource Experiments with Intelligent Agent Based Simulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Author's Abstract: \n \n"This work utilizes previously documented common pool resource experiments as a foundation for the construction of a series of computer simulations in which the individuals participating in the experiments are represented as separate intelligent agents. An intelligent agent is an autonomous, self-contained entity that resides within a virtual, computer-based, environment. In this study, agents are created to represent the individual participants in the CPR experiment and the resource that they share in common. By programming the agents with different strategies and endowments, the researcher can allow the agents to interact within a prespecified environment and observe the outcomes. These outcomes may include the performance of individual strategies in a specific environment, or the overall behavior of the group that emerges as a result of the numerous interactions of the individual agents. These models allow the researcher to observe the relative performance, at the individual and group level, of different combinations of individual strategies and to begin to draw connections between individual behaviors and group outcomes. \n \n"Group performance in heterogeneous simulations can vary significantly with minor changes in the initial parameters of the environment or the characteristics of the agents. Simulations which allow for simplified communication between agents show that a lock-in can occur in which the agents agree on a group wide investment strategy which may or may not be an optimal solution. Some general discussion of the results of these simulations is provided, including a comparison with some observations from experimental economics and game theory. Preliminary observations on the advantages and disadvantages of agent based simulation as a tool for the analysis of the commons dilemma and issues related to heterogeneity are provided, along with some suggestions for future directions in which this work might proceed."
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it