ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2023
Bridging adaptive management and reinforcement learning for more robust decisions.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- AI-driven technological breakthroughs and practical pathways for biodiversity conservation and ecological management.iScience · 2026Review
- Statistical limits and conditional complexity in real-world reinforcement learning: a tutorial survey.Frontiers in artificial intelligence · 2026Article
- Developing new technologies to protect ecosystems: Planning with adaptive management.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Economic factors underlying biodiversity loss.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- Perspective: sustainability challenges, opportunities and solutions for long-term ecosystem observations.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- Detecting and attributing the causes of biodiversity change: needs, gaps and solutions.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional and uncertain situations. But can these methods help us devise robust strategies for managing environmental systems under great uncertainty? Here we explore how reinforcement learning (RL), a subfield of artificial intelligence, approaches decision problems through a lens similar to adaptive environmental management: learning through experience to gradually improve decisions with updated knowledge. We review where RL holds promise for improving evidence-informed adaptive management decisions even when classical optimization methods are intractable and discuss technical and social issues that arise when applying RL to adaptive management problems in the environmental domain. Our synthesis suggests that environmental management and computer science can learn from one another about the practices, promises and perils of experience-based decision-making. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.