Exploring Intervention in Co-Evolving Deliberative Neuro-Evolution with Reflective Governance for the Sustainable Foraging Problem
Bibliographic record
Abstract
Cooperation has been widely studied in multi-agent foraging tasks. However, the impact of agent-environment interactions on the longer term and the achievement of sustainability have been largely unexplored in this context. This work contributes to the development of a testbed for exploring social dynamics between agents: the ‘sustainable foraging problem’. This testbed explores the effect of agent behaviour and the agent’s dilemma of choosing between individual reward and collective long-term goals for sustainable resource management. To incorporate varied levels of replenishment rates in this testbed, forest, pasture and desert environment types are formulated. A co-evolving deliberative loop with neuro-evolution that asks the agents to act with greedy or moderate behaviour is demonstrated. This deliberative layer is shown to be insufficient in situations of social dilemma where the agents learn to increase their individual rewards instead of collectively increasing these rewards through the sustainability of the environment. A simple reflective governor based on the notion of the agent’s self-awareness is illustrated to allow the agents to occasionally reason about the long-term impacts of their immediate actions on future resource availability in the environment, which may eventually ensure sustainability.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".