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Strategies for sustainable management of renewable resources during environmental change
Stockholm Resilience Centre, Stockholm University.ORCID iD: 0000-0003-1546-0934
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).ORCID iD: 0000-0002-2792-1622
Stockholm Resilience Centre, Stockholm University.ORCID iD: 0000-0003-1206-4864
2017 (English)In: Proceedings of the Royal Society of London. Biological Sciences, ISSN 0962-8452, E-ISSN 1471-2954, Vol. 284, 20162762Article in journal (Refereed) Published
Abstract [en]

As a consequence of global environmental change, management strategies that can deal with unexpected change in resource dynamics are becoming increasingly important. In this paper we undertake a novel approach to studying resource growth problems using a computational form of adaptive management to find optimal strategies for prevalent natural resource management dilemmas. We scrutinize adaptive management, or learning-by-doing, to better understand how to simultaneously manage and learn about a system when its dynamics are unknown. We study important trade-offs in decision-making with respect to choosing optimal actions (harvest efforts) for sustainable management during change. This is operationalized through an artificially intelligent model where we analyze how different trends and fluctuations in growth rates of a renewable resource affect the performance of different management strategies. Our results show that the optimal strategy for managing resources with declining growth is capable of managing resources with fluctuating or increasing growth at a negligible cost, creating in a management strategy that is both efficient and robust towards future unknown changes. To obtain this strategy, adaptive management should strive for: high learning rates to new knowledge, high valuation of future outcomes and modest exploration around what is perceived as the optimal action.

Place, publisher, year, edition, pages
2017. Vol. 284, 20162762
Keyword [en]
natural resource management, adaptive management, learning-by-doing, reinfircement learning, growth, climate change
National Category
Environmental Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-202916DOI: 10.1098/rspb.2016.2762Scopus ID: 2-s2.0-85014536417OAI: oai:DiVA.org:kth-202916DiVA: diva2:1078865
Note

QC 20170314

Available from: 2017-03-06 Created: 2017-03-06 Last updated: 2017-03-14Bibliographically approved

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