1.1k citations · 1.6k across the 20 of their papers we have counts for
22 papers
Pretty darn good control: when are approximate solutions better than approximate models
Felipe Montealegre-Mora, Marcus Lapeyrolerie, Melissa Chapman +2
Existing methods for optimal control struggle to deal with the complexity commonly encountered in real-world systems, including dimensionality, process error, model bias and data h…
Bridging adaptive management and reinforcement learning for more robust decisions
Melissa Chapman, Lily Xu, Marcus Lapeyrolerie +1
From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and…
The Forecast Trap
Carl Boettiger
Encouraged by decision makers' appetite for future information on topics ranging from elections to pandemics, and enabled by the explosion of data and computational methods, model…
Power and accountability in reinforcement learning applications to environmental policy
Melissa Chapman, Caleb Scoville, Marcus Lapeyrolerie +1
Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental reg…
Social-ecological feedbacks drive tipping points in farming system diversification
Melissa Chapman, Serge Wiltshire, Patrick Baur +16
The emergence and impact of tipping points have garnered significant interest in both the social and natural sciences. Despite widespread recognition of the importance of feedbacks…
Deep Reinforcement Learning for Conservation Decisions
Marcus Lapeyrolerie, Melissa S. Chapman, Kari E. A. Norman +1
Can machine learning help us make better decisions about a changing planet? In this paper, we illustrate and discuss the potential of a promising corner of machine learning known a…