Promoting the Emergence of Behavior Norms in a Principal–Agent Problem—An Agent-Based Modeling Approach Using Reinforcement Learning
Bibliographic record
Abstract
One of the complexities of social systems is the emergence of behavior norms that are costly for individuals. Study of such complexities is of interest in diverse fields ranging from marketing to sustainability. In this study we built a conceptual Agent-Based Model to simulate interactions between a group of agents and a governing agent, where the governing agent encourages other agents to perform, in exchange for recognition, an action that is beneficial for the governing agent but costly for the individual agents. We equipped the governing agent with six Temporal Difference Reinforcement Learning algorithms to find sequences of decisions that successfully encourage the group of agents to perform the desired action. Our results show that if the individual agents’ perceived cost of the action is low, then the desired action can become a trend in the society without the use of learning algorithms by the governing agent. If the perceived cost to individual agents is high, then the desired output may become rare in the space of all possible outcomes but can be found by appropriate algorithms. We found that Double Learning algorithms perform better than other algorithms we used. Through comparison with a baseline, we showed that our algorithms made a substantial difference in the rewards that can be obtained in the simulations.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".