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Empirical dynamic programming for model-free ecosystem-based management
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DOI：10.1111/2041-210X.14302.png)
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Abstract
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Quantitative ecosystem-based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best-studied systems. Here, we develop a management scheme that is based on predictive models driven by the observed dynamics. We show that near-optimal management policies can be constructed from time-series data by merging empirical dynamic modelling and stochastic dynamic programming. The Empirical Dynamic Programming approach performs well in cases we examined and outperformed a commonly used single-species alternative. We expect model-free ecosystem-based management to be of use wherever ecosystem dynamics are uncertain or observations of the system do not cover all relevant species.
Keywords:
approximate dynamic programming
ecosystem management
Gaussian process regression
nonlinear methods
temporal difference learning
time-delay embedding
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